How Did We Get Here?

The current anti-expertise sentiment is the predictable result of at least four converging forces.

1) The Great Disappointment of Neoliberalism. Codified in the Washington Consensus and launched by the Reagan/Thatcher revolution of the 1980s, a neoliberal policy agenda took hold across the Western world: deregulate markets, shrink government, liberalize trade, and trust markets over governments to deliver broad prosperity. The collapse of the Soviet Union seemed to settle the argument permanently. The economists, policy advisors, and international institutions who championed the neoliberal agenda were unambiguous in their predictions: rising tides, they insisted, would lift all boats. What actually materialized, however, was dramatically increasing inequality with the share of income going to the top 1% in the US surpassing even the Robber Barons of the Gilded Age. When experts have been so spectacularly wrong about something so consequential, it is not entirely irrational to question their authority.

2) The Collapse of a Shared Information Commons. For most of the twentieth century, a relatively small number of authoritative sources, most notably Walter Cronkite and the CBS Evening News, shaped a shared set of facts held by most citizens. The internet destroyed that model. Today we are awash in an unmediated torrent of information in which expert opinion, amateur speculation, and deliberate disinformation are indistinguishable. The packaging is identical. People self-select into algorithmic echo chambers that reinforce what they already believe. In this environment, expertise becomes just one more competing voice.

3) The Credibility Crisis in Institutions. Trust in virtually every major institution has fallen to historic lows, and some of that distrust is earned. Financial institutions that caused the 2008 crisis were bailed out while millions of ordinary citizens lost their homes and savings. Political dysfunction and visible incompetence have made governance seem like a performance rather than a function. And the collapse of replication in academic research has revealed that many landmark findings, including some that shaped public policy, simply don’t hold up. When institutions that are supposed to embody expertise fail so visibly and repeatedly, the logical inference is that the experts running them don’t really know what they’re doing.

4) COVID: The Right Lesson Is Not the Obvious One. The COVID-19 pandemic is frequently cited as Exhibit A that experts cannot be trusted. This is both understandable and wrong. The problem was not that the true experts failed. Virologists and epidemiologists such as Christian Drosten (Charité, Berlin) and Michael Osterholm (University of Minnesota) were consistently insightful and prescient. The problem was that the pandemic created a vast stage for impostors. Economists, physicists, tech entrepreneurs, and even family practitioners far outside their clinical lane claimed epidemiological authority they did not possess. The correct lesson from COVID is not that expertise failed, but that non-experts lack the tools to distinguish real expertise from self-declared expertise.

Together, these forces have created an environment in which skepticism of expertise has become not just common but normalized. And the same cultural forces that eroded public trust in expertise did not miraculously stop at the corporate door. Executives who built their careers during the neoliberal era absorbed its core belief in the power of markets and incentives, whereas those who came of age in the internet age absorbed the related belief that data, dashboards, and AI-assisted tools are adequate substitutes for the deep domain expertise previous generations had to develop the hard way. Neither assumption is entirely wrong but, taken too far, both are dangerous. As a result, inside many organizations there is a silent but significant degradation of standards: for what counts as genuine capability, for what level of expertise is required for making high-commitment decisions, and for what it actually means to know something as opposed to merely having access to information about it.

That environment is made worse, and more difficult to escape, by what psychologists call the Dunning-Kruger effect: people with low competence in a domain not only systematically overestimate their own ability but also lack the metacognitive awareness to recognize their deficiency. Ignorance, in other words, is invisible to itself.

The Dunning-Kruger effect is not merely an individual psychological phenomenon. It is the primary reason organizations fail to self-correct on capability gaps, and its implications grow more dangerous with seniority. A mid-level analyst who overestimates their competence may produce a flawed model, but a senior executive who overestimates their strategic judgment may lock the organization into a flawed strategy with no internal mechanism for correction. The gap between perceived and actual expertise is where poor decisions live.

The Cost of Not Knowing is Measurable

Of course expertise matters. We don’t question whether plumbers should know something about pipes or whether dentists should understand teeth. The same logic should apply, with even higher stakes, to the organizations that shape our economies and govern our lives. And yet, increasingly, it does not – not at the national level, not at the organizational level, and not at the functional level.

Brexit: The Definitive Field Experiment. Brexit represents a rare “live” economics experiment at national scale. When Michael Gove, a senior cabinet minister and one of the Leave campaign’s chief strategists, declared that “people in this country have had enough of experts,” he was explicitly dismissing the overwhelming consensus of economists, political scientists, trade experts, and international relations scholars who predicted that leaving the European Union would do substantial harm to the British economy. Nearly a decade later, the verdict is in: the experts were right. The UK has underperformed comparable economies, trade friction has increased, financial services have migrated to continental Europe, and growth has lagged. The costs of dismissing expertise are being borne by millions of ordinary British citizens, most of whom are now bearing costs they did not anticipate and cannot easily reverse.

Boeing: When Engineering Culture Erodes. The same dynamic operates at the organizational level. Boeing’s 737 MAX disasters did not emerge from a single catastrophic failure but from a cultural erosion over roughly two decades. Engineers who had previously held ultimate authority over safety decisions were progressively outranked by program managers focused on schedule and cost. By the time the MAX was in development, the culture had normalized shortcuts that an earlier Boeing would not have tolerated. The spreadsheet, it turned out, was not running the numbers correctly either.

The Marketing Capability Gap. The same dynamic plays out at the functional level. A 2023 Ipsos study of 1,226 marketing practitioners found that only 35% met a basic benchmark of foundational marketing knowledge (covering concepts such as brand positioning, media planning, and market share dynamics) even though the vast majority expressed high levels of confidence in their own abilities. Moreover, the weakest areas were precisely the ones that matter most for long-term brand growth: understanding how advertising investment links to market share, and what it actually means to build mental availability. The study also found that formally trained marketers are four times more likely to meet the benchmark than those who learned purely on the job, and that training is a far stronger predictor of capability than seniority, specialization, or years of experience. Experienced people who don’t know the fundamentals are, it turns out, confidently wrong in ways that matter commercially. This is Dunning-Kruger at scale.

The pattern is consistent across levels of analysis - national, organizational, and functional. Survey after survey of senior leaders finds the same pattern: nearly universal acknowledgment that capability building is urgent, paired with near-universal doubt that it has actually been achieved. Awareness of a capability gap and the will to close it are not the same thing. Most organizations have the first. Far fewer sustain the second. The deficit of genuine expertise creates measurable costs at every level. It shows up in poor decisions, slow execution, misallocated resources, and strategies that never translate into outcomes.

Expertise as Strategy

The diagnosis points directly to the prescription. If functional capability gaps are the organizational expression of a broader cultural devaluation of expertise, then closing them requires not just operational fixes but also a deliberate decision, at the leadership level, to treat genuine expertise as a strategic asset and to build the organizational structures that develop, protect, and reward it accordingly.

The operational foundations remain essential: build core capabilities deliberately rather than assuming they will accumulate through experience. Hire and train for genuine depth, not just credential signals. Provide enabling infrastructure, including tools, systems, and decision support, that allows expertise to be applied effectively. Incentivize and reward mastery rather than confident presentation. But these are the floor, not the ceiling. The hard part is what comes next.

Expertise for a Non-Linear World

The expertise that was decisive in a stable, slow-moving competitive environment is not the expertise that will be decisive now. Strategic foresight, the ability to think systematically about alternative futures and their implications, is undervalued and underdeveloped in most organizations. So is the capacity for high-quality decision-making under genuine uncertainty, where the probability distribution itself is unknown, not merely the outcome. Most organizations have learned to manage modeled risk. Far fewer have built any capability for navigating genuine uncertainty.

The distinction matters practically. Shell’s scenario planning unit, built in the 1970s and sustained across leadership transitions and market cycles, allowed it to anticipate both the 1973 oil shock and the collapse of oil prices in the 1980s while competitors were still reacting. Similarly, scenario analysis allowed a small number of financial institutions to anticipate the 2008 mortgage crisis while peers remained fully exposed, not because they had superior data, but because they had analysts with enough domain depth to question what the models were assuming. In both cases the advantage was not more information but the expertise to interpret the data correctly under conditions where most others could not.

Can AI Fill the Expertise Gap?

There is a tempting narrative in which AI compensates for human expertise deficits. This narrative is dangerous. Without genuine expertise in the humans directing it, evaluating its outputs, and making the final calls, AI produces confident-sounding answers to poorly framed questions. Knowing what a concept means, how a mechanism works, or why a methodology matters determines whether AI outputs are accepted at face value or examined with commercial discipline. The organizations that will benefit most from AI are those with the deepest human expertise to direct it.

Depth by Design Instead of Default

In complex, fast-changing environments, generalists who can synthesize across domains often outperform specialists who cannot see beyond them. The resolution is a portfolio answer, not a universal one: foundational literacy is non-negotiable for anyone in the relevant domain; deep specialization is context-dependent and should be calibrated against strategic need. The Capability Investment Audit provides the structure for making that distinction rigorously rather than by default.

The Capability Investment Audit

The most useful practical tool for C-Suite leaders is a Capability Investment Audit: a structured, honest assessment of where expertise actually lives in the organization, whether it matches strategic priorities, and where the critical gaps are. It is, at its core, a modern version of the classic make-vs-buy decision, applied to capabilities rather than physical products and components. The logic is identical: build what differentiates you, source what others can do better or more efficiently, and be rigorous about which is which.

The audit is organized around three questions and two dimensions, each designed to force the kind of clarity most organizations avoid.

The three questions: Which capabilities drive differentiated competitive performance; the ones that, if lost, would directly compromise your strategic position? Which capabilities are table-stakes, necessary but not differentiating, required to play but not to win? And which capabilities are genuinely best sourced externally, where the external market provides superior depth, speed, or flexibility that internal investment cannot match?

The two dimensions: depth of current capability and strategic criticality or how central is this capability to the value proposition, now and in the future that is emerging? Mapped against each other, these two dimensions produce a simple but powerful portfolio view of organizational capability.

The resulting capability portfolio map requires active allocation decisions. Capabilities that are both differentiating and currently shallow require urgent, sustained investment; table-stakes capabilities can be managed for efficiency. Capabilities that are genuinely best sourced externally should be managed with a different discipline: the ability to evaluate, direct, and integrate outside expertise. That ability, to be an intelligent buyer and integrator of external knowledge, is itself a capability that must be built deliberately. As with physical make-vs-buy decisions, outsourcing a capability you don’t understand is a path to dependency and eventual strategic vulnerability.

The Expert and the Bathwater

The anti-expertise sentiment that has taken hold in public culture is, at some level, understandable. The experts of recent decades have given people genuine reasons for skepticism. The COVID pandemic made the pattern visible at global scale: self-described expertise proliferates fastest precisely when genuine expertise is most needed.

The question for every C-Suite leader is not whether expertise matters. The evidence on that is unambiguous. The question is whether you are building it deliberately enough, in the right places, to matter when it counts.

• Though the strike is against Dauch Corporation – known until recently as American Axle & Manufacturing – the UAW’s main leverage comes from the strike’s potential impact on OEMs, particularly GM.

• Dauch has little direct incentive to settle quickly, since it is losing money, faces tough competition, and the union is asking for a 40% raise.

• GM wants the strike to end quickly, since each day of lost full-size truck production at even a single plant reduces profit by at least $25M.

• The UAW is hoping to increase wages at suppliers substantially for a variety of strategic reasons and would like to use an increase at Dauch to set a new baseline.

• The unique history and leverage at Dauch may enable the UAW to demand more, last longer and push harder than at other suppliers, but could also cause it to overplay its hand.

• Because this game will be repeated across the unionized supply base and over time, GM needs to be very strategic in its role and careful in its tactics.

• The outcome of this strike will powerfully affect U.S. automotive industry bargaining through the next round of national negotiations with the Detroit 3 in 2028 and help determine its long-term competitiveness.

The History of the UAW-Dauch-GM Game

To understand this strike and its stakes, context and history are needed. The plant is owned by Dauch Corporation, formerly known as American Axle & Manufacturing. American Axle was created in 1994 when investors led by Richard Dauch bought facilities owned by GM’s Saginaw Division, an early move in the unwinding of the vertical integration of U.S. automakers. A major reason for the unwinding was that labor costs were much higher at the Detroit 3 than at independent suppliers, particularly those not represented by the UAW.

Labor relations were contentious at American Axle for many years, with the company threatening to move business to Mexico and the UAW pressuring GM to keep the business with American Axle and in the U.S. In 2008, the UAW went on strike for 87 days, one of the longest in the history of the automotive industry. The strike finally ended with wages being cut from $29 to $18.50 per hour. Critical to the settlement was a $200M payment by GM – more than $50K per worker and half the total cost – for early retirements, buyouts, and “buydowns”. In addition to feeling the direct effects of the strike, GM was successfully pressured by the UAW to get involved by “health and safety” strikes at two unaffected, profitable plants.

In the aftermath of the strike, American Axle has continued to shrink its original U.S. footprint, expanding operations in Mexico and its product line through acquisitions, renaming itself as Dauch Corporation earlier this year. The Three Rivers Michigan plant that is on strike is the last remaining of its original U.S. operations.

Shortly after the 2008 strike ended, the Great Recession hit and American Axle nearly went bankrupt. It became profitable as the industry recovered, but it is highly leveraged. The company has had negative net income in four of the last six years and expected to lose money this year even before the current strike.

The Stakes of the Game

All three of the main players – UAW, Dauch, and GM – have much at stake in the strike. Shawn Fain and the rest of the UAW leadership face elections next year amid significant dissent and dissatisfaction within their ranks. They want to demonstrate their aggressiveness and effectiveness. They also want to narrow the gap between supplier and Detroit 3 wages and benefits that widened enormously with the 2023 contracts. Other suppliers, starting with Nexteer, will be watching how the strike plays out and what the final terms are.

For Dauch, the strike risks being existential. The stock price, though up over the last year, is down almost 50% over the last 5 years and investors were already questioning Dauch’s competitiveness and debt load. Facing losses and integrating recent acquisitions, an enormous wage increase or potential loss of GM’s business would be devastating. There are also likely to be emotions involved. The plant’s current competitiveness was bitterly won and the leadership has also had to manage through years of swings (some caused by the UAW) in the volume and direction of GM, its major customer. Even the company’s new name may have an impact, since it is shared by the CEO and his late father, the company founder. David Dauch will not want to have his company beholden to the UAW.

The short-term risks for GM are clear. Switching to another plant or another supplier would be quite costly, risky, and time-consuming. Once axle inventories run out, production of affected trucks will stop. Losing one day of full-size truck production at Flint Assembly will reduce GM profits by at least $25M, due to the high demand for and profitability of the trucks. A strike that extends beyond GM’s annual two-week shutdown in July would be very painful. But just writing a check to pay for labor peace would be strategically costly, since workers at other suppliers would make similar or even greater demands and the UAW would be emboldened right through 2028 bargaining.

The Impact of Framing on the Strike

As with many negotiations, how each side frames its position will influence the outcome. To justify their demand for a 40% wage increase over the current $22/hour, the UAW refers back to the wages before 2008, which after inflation adjustment would be more than $45. They claim the “concessions” were made “to save the company” and intended to be temporary. This is not historically accurate. The 2008 wage cuts occurred before the recession and after a bitter strike and were accompanied by buyouts/early retirements/ buydowns that show permanent intent; they were designed to achieve a competitive cost structure. However, the framing may have more impact than just PR value. It may reinforce worker willingness to withstand a long walkout and make it difficult to ratify any contract with a more moderate increase.

Framing also matters for Dauch. If it views the strike and UAW demands as final indications that the UAW cannot be worked with, it may decide to close the plant and move the business elsewhere or give it up. Moving to Mexico would be impossible in the short term given the ongoing USMCA negotiations and the certain angry reaction of the Trump Administration, but the UAW should be concerned about GM finding another source of supply.

Framing will likely determine GM’s actions as well. If the Dauch strike is viewed as a one-off event or if GM focuses on the short-term pain, GM will push Dauch to settle and perhaps (quietly) financially facilitate it. If it is seen to be a battle that will determine supplier wage levels, GM may scramble to keep full-size truck production going and prepare for a lengthy strike.

The Likely Battle

Shawn Fain is likely to view the Dauch strike as an ideal battleground. The plant’s products create maximum leverage, particularly since GM is eager to gain highly profitable market share while Ford deals with aluminum supply issues. Dauch workers’ wages have not kept up with inflation, even after the 2008 reset. The Trump Administration’s looming presence makes a threat to move production to Mexico not very credible. Since fewer than 1000 workers are affected, there is modest material impact on the UAW strike fund. A lengthy strike would signal strength to both members and the entire industry. A big win would resound in other negotiations and enhance his election prospects. As long as the workers are willing to bear the pain of the strike, Fain will feel in a comfortably strong position. However, there is a risk that the UAW will overplay its hand, either by killing the plant or setting expectations too high both for Dauch workers and UAW members at other suppliers.

Both GM and Dauch should realize that the final contract will have a sizable wage increase. They should avoid pointing the finger at each other and saying, “you pay for it”; when there was a three-way stalemate among the UAW, Delphi, and GM in 2005, the outcome was disastrous for all three players. Instead, both companies should be prepared for a lengthy walkout and Dauch should start pushing for a “fair” settlement with a sizable wage hike in a few weeks, after worker enthusiasm for the strike has started to wane. Whether GM needs to contribute financially to facilitate a settlement is something that the two companies can negotiate secretly.

Both the final wage number and how it is achieved will matter for the rest of the industry. They should be watching developments and tactics closely and preparing for battle. They may well be next.

Welcome to the June issue of The Briefing Table where we review each month’s major events from a business strategy and risk management perspective.

Last month, we described a pattern of deferred costs arriving simultaneously on compressed timelines. If anything, May accelerated that pattern, with the Iran war giving way, conditionally, to a framework deal and the Fed changing hands for the first time in eight years, while the EU is quietly moving ahead with an industrial policy framework that will reshape European markets, and, just below the surface, China continues to accumulate strategic options while others absorb costs.

The question for June is whether the resolutions now emerging represent genuine turning points or simply new baselines for the next set of deferred costs to arrive into. This issue makes the case for the latter.

1) Iran: Don’t Plan for the End. Plan for the Next Phase.

Both parties want a deal. The United States wants an exit it can characterize as a strategic success and a domestic economic win before the midterms. Iran wants to end the blockade, resume oil revenues, and buy time to reassess its position from a less exposed posture. Those interests converge on one outcome: a short-term interim agreement, probably 30 to 60 days, under which the Strait reopens and the blockade lifts in exchange for Iran freezing enrichment and entering nuclear talks.

What they do not converge on is anything beyond that. Whether Iran retains any right to enrich uranium, what happens to its existing highly enriched stockpile, what verification regime is acceptable are all questions where the two parties’ interests are structurally incompatible; Iran’s government has no political path to concessions that could satisfy Washington; Washington has no political path to terms that could satisfy Tehran.

The key issue is that the new Iranian leadership believes it has achieved a genuine weakening of American power in the region. As a result, expect no large concessions from Tehran in the nuclear talks. More concretely, Hormuz control is now a permanent Iranian strategic asset, not a temporary crisis measure: the Strait will serve as an economic lever and a deterrent against future attacks.

An interim agreement is the most likely near-term outcome not because it resolves anything, but because it defers everything that cannot yet be resolved while still giving both sides something to claim. C-Suite executives should plan for a cycle, not an endpoint. Interim agreement, partial Hormuz reopening, nuclear talks begin, talks fail, crisis resumes: that is the base case.

The physical reality will lag the headlines by months

The physical relief from a deal will lag the financial relief by months. The energy disruption of the Hormuz Strait’s closing has been partially offset by three factors: emergency SPR releases from IEA member countries, floating storage drawdowns from Russian and Iranian tankers, and pipeline bypass capacity from Saudi Arabia and the UAE.

These buffers are temporary. Together they have created a race between buffer depletion and market expectations about the duration of the closure. As long as markets believe a deal is imminent, prices stay suppressed. If that belief falters, prices will move sharply and non-linearly. Brent crude could reach $120 per barrel on an expected 10% supply decline, and approach $150 if markets conclude the temporary buffers are exhausted.

A tail scenario that deserves to be named is Iranian targeting of Saudi Arabia’s Abqaiq processing facility, where equipment replacement timelines are measured in years, not months. If Abqaiq is struck during a breakdown of the interim agreement, $150 is a floor, not a ceiling. Shipping insurers may not immediately greenlight traffic through waters where Iran dropped mines, with the tentative agreement requiring their removal within 30 days. Crude prices respond quickly to headlines. Energy prices for end users go up like a rocket and down like a feather.

Plan for thresholds, not probabilities

Uncertainty has become so extreme that standard forecasting tools, which assume a distribution of outcomes around a central forecast, provide a false sense of precision in a business environment where the actual distribution of outcomes is unknown and tail events are unusually consequential. Rather than asking what the most likely outcome is, identify the oil price at which your cost structure breaks, and how much warning you would have. For businesses with Gulf supply chain dependencies, the trigger is a specific input shortage date. Senior executives who use a deal announcement as an occasion to close their scenario planning books will find they have to reopen them sooner than expected.

What no Deal changes

Beijing has taken a position of “active neutrality”: non-alignment, risk mitigation, continued economic relationships with all regional parties. It has already accumulated the diplomatic capital of being the region’s indispensable facilitator, not as a replacement for American primacy, but as the second anchor in a dual-core order that Beijing is actively constructing. Iran’s oil exports have been increasingly settled in renminbi throughout the conflict, a practical arrangement that will outlast any peace agreement. CFOs and treasurers who have been treating currency diversification as a long-range planning item should treat it as a near-term one. The optionality to begin the transition cheaply will not persist indefinitely.

Bottom Line: An interim agreement is the most likely near-term outcome but also the least stable one. It defers rather than resolves the issues that make this conflict intractable. For energy and supply chain planning, model the threshold price at which your cost structure breaks, then work backward to determine how much Strait-closure time it takes to reach it. Replace probability-weighted forecasts with threshold-trigger planning. Watch June 30, the hard checkpoint for the nuclear memorandum that determines whether the interim framework holds.

2) Trump Got His Fed Chair. He May Not Get His Rate Cuts.

Kevin Warsh was confirmed as the 17th chair of the Federal Reserve on May 22 and sworn in at the White House, the first such ceremony held there since Alan Greenspan in 1987, reflecting how his appointment was conducted and what it signals about the relationship between the executive and the central bank. His predecessor, Jerome Powell, remains on the board as a governor through January 2028, adding to an already unusual and challenging arrangement.

What Warsh inherited was not the story either his supporters or his critics anticipated. The combination of the tariff architecture and the Iran energy shock has produced something the Fed has not seen since the early 1980s: a meaningful rise in medium-term inflation expectations. Five-year expected inflation, as measured by the University of Michigan survey, reached 3.9% in May, the worst reading since the early 1980s. It is not the 9% of 1980, but the mechanism is the same: when medium-term expectations rise, forward-looking price decisions rise with them, and inflation becomes partially self-fulfilling. The 3.9% figure suggests that this leapfrogging process may have begun. If it continues, the cost of reversing it, as the Fed demonstrated between 1979 and 1983, is a severe recession. Even short of a hike, elevated inflation expectations put a floor under long-term rates, meaning the cost of capital is unlikely to fall materially even if the Fed holds.

The probability of a 2026 rate hike, dismissed as implausible six months ago, now sits at 35% on prediction markets. But the rate is not the only instrument to watch. Warsh has been an outspoken advocate for reducing the Fed’s balance sheet, which remains historically large following the quantitative easing programs of the past decade. Accelerating balance sheet runoff would be contractionary independently of any rate decision, putting upward pressure on long-term rates and increasing recession risk at precisely the moment the economy is absorbing simultaneous tariff and energy shocks. Executives with long-duration capital commitments, leveraged balance sheets, or real estate exposure should model this scenario alongside the rate hike probability. The July 14 CPI release, covering June data, is the next meaningful signal on the rate side. If inflation remains above 3%, that probability moves higher.

The question the May issue raised will be answered when Warsh chairs his first FOMC meeting on June 16-17: does he remove the easing bias from the statement, and how does he frame the Fed’s inflation mandate relative to the political pressure to cut? The rate decision itself matters less than those two signals.

Bottom Line: Warsh’s first meeting on June 16-17 is the signal our May issue flagged. Whether the easing bias is removed, how he frames the inflation mandate, and whether he signals balance sheet acceleration will say more about the rate path for the rest of 2026 than the decision itself. The July 14 CPI release, covering June data, will be the next forcing function: above 3% raises the probability of a 2026 hike materially. Capital allocation decisions premised on a benign rate path need to be rebuilt around a genuinely two-directional environment.

3) Europe Is Not Offering Subsidies. It Is Rewriting the Rules.

Most senior executives tracking European regulatory risk in 2026 are focused on the EU AI Act and its August 2 enforcement deadline. That is the right priority but not the only one.

The EU Industrial Accelerator Act, formally proposed in March, goes further than the U.S. Inflation Reduction Act by embedding “Made in EU” and low-carbon requirements directly into public procurement eligibility and state aid qualification. In other words, the IAA redefines who qualifies to compete in European markets at all, not merely who gets rewarded for competing there.

European manufacturers qualifying for IAA benefits, accelerated permitting, infrastructure prioritization and procurement preference, will operate with a structurally different cost base than global competitors. And the strategic window is closing faster than the regulation itself. The IAA’s final adoption may be more than a year away, and EU member states remain divided, but by the time the rules are finalized, the best sites, suppliers, partnerships, and incentive pools may already be spoken for.

The trigger is a notification requirement for when a single non-EU country controls more than 40% of global manufacturing capacity in the relevant sector. The primary target is China, which currently exceeds the 40% threshold, under the current draft, in EV batteries, electrolyzers, heat pumps, wind and solar equipment, and several automotive components. Any transaction involving a Chinese-controlled entity, whether as acquirer, joint venture partner, or significant supplier, in any of these sectors will require advance authorization from EU member state authorities before proceeding. This is not a tariff. It is a structural gate on deal-making in some of the most active M&A categories in European industrials.

Senior executives assessing their European competitive position need to model both the eligibility question and the adaptive Chinese response to it. Chinese analysts are already framing the IAA as an accelerant toward localized production and joint ventures inside Europe: capacity cooperation rather than goods trade. The competitive threat does not disappear when Chinese firms are disqualified from procurement. It adapts.

Bottom Line: The IAA is not yet law and will arrive unevenly. The companies best positioned to benefit will assess their European supply chain footprint, ownership structures, and carbon compliance posture now, before the incentive pools solidify around early movers. For M&A teams, any deal involving a Chinese-controlled entity in EV batteries, electrolyzers, heat pumps, wind, solar, or automotive components now requires a prior authorization check. The first question is: which elements of your European business qualify as genuinely European under the “Made in EU” framework taking shape, and which do not?

4) The U.S. Economy: Fine On the Surface. Not Below.

It has now been one year since the administration announced its new tariff regime. Enough time for a verdict: not on the policy’s intent, but on its actual cost pass-through.

At the macro level, the numbers look resilient. Q1 2026 GDP came in at an annualized rate of 2.0% while unemployment is holding between 4.3 and 4.5%. The inflation picture is much more worrying, however, as tariffs now represent the largest U.S. tax increase as a share of GDP since 1993 and consumer confidence has hit a record low, worse than during either the 2008 financial crisis or the stagflation of the early 1980s.

Importantly, U.S. importers absorbed the bulk of tariff costs through 2025 by drawing down pre-tariff inventory stockpiles. Those stockpiles are now depleted. The macro numbers also conceal significant structural fragmentation in consumer spending. The top 10% of U.S. households now drive a near-majority of consumer expenditure, supported by non-labor income (dividends, rents, and capital gains) that is relatively insulated from tariff pass-through and energy cost increases. The bottom 90% is not.

Companies whose customer base skews toward the middle- and lower-income distribution are operating in a materially different environment than macro figures imply. Pricing decisions, labor agreements, and supply chain contracts signed in the second half of 2026 will be made into an environment that is genuinely two-directional on rates. Companies still running multi-year plans against single-point forecasts, as opposed to scenarios, are carrying significantly more risk than they have recognized. The tariff pass-through and the inflation expectation drift compound each other; planning models that treat them separately understate the combined exposure.

Bottom Line: Reopen every major cost assumption made in Q1 2025 and compare it against actual pass-through over the past 12 months. For most companies, the gap is larger than planned. Assume the remaining pass-through arrives on the current schedule, not the optimistic one, and model its interaction with the inflation expectation drift.

Key Take-Aways

Iran: plan for a cycle, not an endpoint. An interim deal is the most likely near-term outcome and the least stable one. It defers everything intractable while giving both sides something to claim. Model the threshold oil price at which your cost structure breaks. Replace probability-weighted forecasts with threshold-trigger planning. Watch June 30. The renminbi settlement arrangement Iran built during this conflict will outlast any peace agreement; begin the treasury diversification conversation now.

Warsh chairs his first FOMC meeting June 16-17. Watch whether the easing bias is removed and how he frames the inflation mandate: those signals matter more than the rate decision itself. A 2026 rate hike probability of 35% on prediction markets is no longer fringe. The July 14 CPI print is the next forcing function after that. Capital allocation plans premised on rate cuts need to be rebuilt around a genuinely two-directional environment.

The EU IAA is not a subsidy program; it is a gate. Not yet law, but the strategic window is closing before the rules are finalized. For M&A teams: any deal involving a Chinese-controlled entity in EV batteries, electrolyzers, heat pumps, wind, solar, or automotive components requires a prior authorization check now. Discovering that requirement mid-process is costly.

Liberation Day is a forcing function. Reopen every major cost assumption made in Q1 2025. The remaining half of tariff pass-through will be substantially complete by mid-2026. “The U.S. consumer” is no longer a useful planning unit: the top 10% of households drive a near-majority of spending and are insulated in ways the bottom 90% are not.

These forces interact. The Iran cycle feeds the inflation drift. The inflation drift constrains Warsh’s options. Warsh’s options set the cost of capital against which every other decision is made. Senior executives who see those connections and build planning models flexible enough to absorb simultaneous movements across all four, will be better positioned than those managing each force in sequence after the fact.

Amit Mathrani is an Executive Director of Energy Transition Research at Rabobank North America, where he leads sector research on power markets, renewables, and energy infrastructure. Before joining Rabobank’s RaboResearch team, he led corporate strategy at Consolidated Edison and National Grid, focusing on electric business growth across New York and the Northeast. Prior to that, he worked in management consulting across capital-intensive industries including mining, oil and gas, and industrials. His recent research has reframed the data center power crunch as a strategic and financial challenge for senior executives, not merely an infrastructure management problem.

C-Suite: Your report describes a structural mismatch at the heart of the AI build-out: data centers go up in 12 to 24 months, but grid connections take 36 to 84 months. How big is this problem?

Bigger than most executives have absorbed. US data centers consumed roughly 176 terawatt-hours of electricity in 2025. By 2030, that figure is forecast to reach somewhere between 420 and 728 terawatt-hours. The incremental demand alone is equivalent to the combined annual electricity consumption of New York and California. The five largest hyperscalers – the cloud and AI infrastructure giants building data centers at a scale no other companies can match – plan to spend between $745 billion and $775 billion on capital expenditure in 2026 alone, and every one of them reported on their most recent earnings call that AI compute demand continues to outpace available capacity.

That scale is the context for the mismatch. In California, interconnection processing times now exceed seven years. AEP Ohio told PJM’s Load Analysis Subcommittee that none of the 13 gigawatts of new data center load in its territory can be reliably served until Q4 2031. Companies setting the pace for AI innovation have concluded they cannot wait. They are taking power procurement into their own hands, and in doing so constructing what is effectively a parallel energy system.

C-Suite: Why does that make it a boardroom issue rather than a facilities management problem?

Because of where the real cost lives. Power is only 10 to 15 percent of a data center’s total cost of ownership. But it is the single binding constraint on whether a facility generates revenue at all. A data center’s semiconductor assets cost $10 to $15 billion and depreciate at $3 to $5 billion per year. The moment those chips arrive, the facility needs to be operational. You cannot wait four to seven years for a grid connection. That is why companies are paying $100 to $165 per megawatt-hour for behind-the-meter power versus $90 to $95 for grid alternatives. The cost of the power is not the issue. The cost of idle hardware is.

And then there is the price signal in the capacity markets. PJM’s capacity auction cleared at $333 a megawatt-day in December 2025, against a price of $29 two years earlier. PJM estimated the clearing price would have approached $530 without the regulatory cap. That is not a procurement fluctuation. That is a signal that belongs in the boardroom.

C-Suite: Can a non-hyperscaler realistically build its own power solution?

My honest answer, if I were sitting on the board of a Fortune 500 industrial today, is: no. And I would say that directly. The risk is asymmetric in a way that makes self-supply a dangerous idea for most companies.

A hyperscaler can build or even overbuild a behind-the-meter solution and either sell the excess into the wholesale market or absorb the write-down. They have trillion-dollar balance sheets. A non-hyperscaler has none of that. We have seen what happens when companies get that wrong: the merchant gas turbine cycle of 1997 to 2002 ended in a wave of stranded asset write-downs. Enron is the most famous name, but there were many others.

The framework I would offer has three variables. Scale: unless you are thinking about at least 500 megawatts to a gigawatt, the economics do not work. Below 100 megawatts, they definitely do not work. Duration: can you credibly commit to a 20- or 25-year contract? Most companies will overestimate that certainty, and the CFO signing that contract will probably have left the company before it expires. Balance sheet: can you absorb that asset if your demand curve flattens? If the answer to any of those three is no, you should not be owning your own power plant.

C-Suite: So what are the realistic options for a company below that threshold?

The most underappreciated tool in this conversation is the virtual power purchase agreement, or VPPA. The logic is straightforward: a VPPA means the renewable asset or power plant sits in an entirely different electricity market from where your facility is located. You never touch the plant. The developer builds it, operates it, finances it, and carries the construction and permitting risk. What you get is long-term price certainty and the financial benefit when market prices rise above your contract strike, which, given the trajectory of capacity markets, they increasingly will. If prices fall, you owe a modest difference. It is not a perfect hedge, but it gives a company genuine optionality without a physical asset on its balance sheet.

Mars recently signed a wind VPPA in Europe on exactly this logic. The company gets the economic benefit of renewable ownership, reduces its carbon exposure, and manages long-term price volatility, without building a wind farm. That is capital-light exposure to an infrastructure project that keeps balance sheet flexibility intact. It is the right model for most companies sitting below the self-supply threshold – with one important caveat: a VPPA is a financial instrument, not a physical one. It does not help you connect to the grid or power new facilities.

Beyond VPPAs, there are smaller-scale options: rooftop solar on an industrial facility, battery storage to offset peak hours, and fractional capacity arrangements with merchant developers. The electric grid remains one of the greatest inventions of the past hundred years. The advice for most companies is: stay with the grid, use financial instruments to manage your exposure, and treat power as the strategic variable it has become.

C-Suite: How serious is the price volatility risk for companies that cannot self-insure?

Very serious, and not yet fully reflected in how most boards think about risk. Last year we saw a 5 percent increase in national retail rates. This year, 6 percent is expected. I think the real numbers are probably higher than the forecasts suggest, given what we are seeing in capacity markets.

The uncomfortable implication: a company that misjudges its energy position today will not just feel it in next year’s earnings. It will feel it in 2031, when it discovers it cannot expand because it did not lock in the megawatts in time. The cost of getting this wrong compounds. It is not a question of what power costs per kilowatt-hour. It is a question of what it means for your production capacity and your growth options not to have secured it.

A CFO getting this right understands that this is no longer a procurement question. It requires someone on the management team, or on the board itself, who can read a power purchase agreement the way someone reads a credit commitment. Long-duration physical assets behave very differently from the asset-light businesses most boards know how to oversee. The risk profile of a balance sheet changes materially when you start taking positions in energy infrastructure, even through financial instruments. Boards that do not have that expertise in the room are taking a risk they may not fully understand.

C-Suite: Will the data center build-out slow, and what are the real constraints?

It will be constrained, not stopped. Of the 130-plus gigawatts of co-located energy resources announced across the US, roughly a quarter is expected to actually deploy by 2030. The rest runs into a cascade of overlapping constraints. Renewable tax credits, the Income Tax Credit and Production Tax Credit that made solar and wind behind-the-meter solutions economically attractive, had their qualification timelines compressed to mid-2026 under the One Big Beautiful Bill Act, removing a major cost advantage from the renewable pipeline beyond that date. Turbine backlogs at GE Vernova, Siemens, and Mitsubishi are running four to five years out. Transformer lead times are 36 to 48 months. One constraint after another.

The technology choices being made today reflect that reality directly. Fuel cells can be deployed in as little as 90 days, the only near-term option for immediate energization. Gas engines and aeroderivatives deploy in 12 to 24 months. Heavy-duty turbines and combined-cycle plants, which are the most efficient at scale, take 36 to 84 months and face wait times of up to seven years for new orders. Gas accounts for over 80 percent of the announced behind-the-meter pipeline, not because of any preference for gas, but because it is the fastest workable option on the timeline AI infrastructure demands. The market is paying a premium for speed.

C-Suite: For companies that depend on third-party compute for their AI strategies, what should they be doing?

Treating compute access as a strategic resource, not a commodity input. The announced pipeline overstates what will actually materialize. That supply shortfall is real, and it will affect pricing and availability. Companies that wait for the market to normalize may find themselves renting their growth capability from someone else at terms they did not negotiate.

Locking in capacity now, even at a premium, may well be the more defensible decision, depending on how central AI compute is to your competitive position over the next three to five years. The question to ask is not what compute costs today. It is what it costs your business not to have it when you need it.

C-Suite: Power availability is reshaping where companies can locate and grow. What should executives be factoring into expansion decisions?

The US industrial geography is shifting faster than most corporate real estate and site selection teams have absorbed. ERCOT in Texas can get a data center interconnection in about three years, the shortest timeline anywhere in the country. Land is available, cheap, and largely without community resistance in rural Texas. PJM and MISO – the regional transmission grids stretching from New Jersey to Minnesota – account for more than 60 percent of projected US data center capacity growth but face the most severe interconnection backlogs. Demand is clustering faster than infrastructure can expand, turning what should be a national growth story into a set of regional capacity crises.

The belt pulling ahead runs from Texas through Ohio and Pennsylvania. These states are actively making it easier: the Texas Energy Fund is providing capital for gas plant developers; PJM is working on cluster connections and process reform. The coasts keep the talent advantage. They are losing the build advantage.

The uncomfortable corollary is this: if you are not hyperscaler scale, you are effectively renting your growth capability from someone else. That should be a conscious strategic choice, not something that happens by default. Where you can locate, where you can grow, whether you can execute on the strategy you are developing, all of that is being reshaped by power access. Don’t find out too late that you didn’t lock in the megawatts.

C-Suite: If you had to leave boards and senior executives with one thought, what would it be?

Power has crossed from procurement to strategy, and most boards have not fully absorbed what that means yet.

Tech companies are becoming infrastructure companies. Industrials are becoming compute buyers. Utilities are becoming financiers. The boundary between these worlds is dissolving faster than the organizational charts can keep up. Look at where the most sophisticated capital allocators in the world are putting their money: Google acquired Intersect Power to own the power pipeline. BlackRock GIP and its partners acquired a utility company. Microsoft secured Three-Mile Island. Amazon signed a contract for an entire campus. These companies did not secure their AI futures by buying model companies or chip designers. They went and bought power.

When the smartest balance sheets in the world are acquiring power plants and utility companies, that tells you where the moat is actually living.

The harder question, whether behind-the-meter power is a temporary bridge to the grid or the foundation of a permanent parallel energy system, remains open. That answer will determine the economics of this decade.

The leaders and boards that can recognize that, and reorient their capital allocation toward it, will outperform. The ones who keep defending the old boundaries won’t. The gap will be measured in enterprise value and total shareholder return. Not in basis points on the electric bill.

• The EU’s IAA introduces ‘Made in EU’ and low-carbon requirements, impacting procurement, subsidies and foreign investments in sensitive sectors.

• European policies aim to retain industrial capacity, jobs and innovation within the continent, especially in strategic sectors like clean-tech and semiconductors.

• U.S. manufacturers will need to reassess supply chains, ownership structures and manufacturing footprints to meet new eligibility and localization standards.

• Executives should act swiftly to identify vulnerabilities, build strategic partnerships and develop flexible operational models before regulations fully take effect.

A German automaker could soon discover that building vehicles in Mexico with Chinese batteries may disqualify it from competing for parts of Europe’s future industrial economy.

Ever since the Reagan-Thatcher revolution of the 1980s, and especially after the end of the Cold War, companies have operated in an environment where economics generally outweighed geopolitical and national security considerations. Governments broadly embraced free trade, deregulation, and market liberalization, enabling multinationals to build globally integrated supply chains optimized for efficiency and cost.

That era is now beginning to fade. The Inflation Reduction Act (IRA), signed into law on August 16, 2022, marked a major turning point. It signaled that even the United States, long the leading champion of free-market globalization, was once again embracing industrial policy as a strategic tool. More broadly, it reflected a growing shift in how governments think about industrial capacity, supply chains, economic security, and technological leadership.

Now the European Union (EU) is responding with something potentially even more disruptive: an industrial policy framework designed not merely to attract investment, but to redefine who qualifies to compete in Europe at all.

The EU’s Industrial Accelerator Act (IAA), unveiled in March 2026, marks a significant shift in European industrial policy away from climate ambition alone and toward competitiveness, resilience, and strategic autonomy. The Act introduces “Made in EU” and low-carbon requirements for public procurement and state aid. It also streamlines industrial permitting through digital one-stop approval systems, creates industrial acceleration zones with simplified approvals, and imposes tighter conditions on large foreign investments in sensitive sectors.

For European policymakers, the IAA is not merely protectionism. It is also a response to mounting concerns over industrial hollowing-out, Chinese clean-tech dominance, and the geopolitical vulnerabilities exposed by the energy crisis following Russia’s invasion of Ukraine. If European taxpayers subsidize the energy transition, Europe increasingly wants the factories, jobs, intellectual property, and industrial capacity tied to that transition to remain in Europe as well.

The key difference between the IRA and the IAA is subtle but profound. The IRA primarily uses subsidies to attract investment into the United States. The IAA goes further by embedding industrial policy directly into market access itself. Public procurement and support schemes could increasingly favor EU-origin products, local value-add, and low-carbon compliance.

In practice, this could affect everything from EV battery sourcing and grid equipment manufacturing to low-carbon steel, industrial automation systems, transformers, and semiconductor packaging. An automotive supplier using Chinese cathode materials, Korean battery cells, and final assembly in Eastern Europe could face a materially different competitive position under future EU procurement and subsidy rules.

The IAA represents a new form of industrial policy built around eligibility, resilience, and strategic alignment rather than traditional tariff barriers alone. As a result, competition may increasingly shift away from lowest-cost supply chains toward what might be called “qualified supply chains” that satisfy political, regulatory, carbon, and localization requirements. Companies will also need to determine whether their ownership structures, sourcing models, supplier networks, and manufacturing footprints satisfy whatever definition of “European” ultimately emerges.

Ironically, the primary geopolitical target is not the United States, but China. European policymakers increasingly fear losing control of EV, battery, clean-tech, and industrial supply chains to Chinese competitors, particularly in sectors tied to decarbonization and strategic technologies.

For American firms with globally distributed supply chains, the operational implications could be significant.

Market Access Pressure

Access to public procurement, subsidies, and policy-supported demand pools could increasingly depend on origin, local assembly, and carbon compliance. Export-led models into Europe may gradually lose competitiveness even if their products remain technologically superior.

Structural Cost Gap

European manufacturers may benefit from subsidies, accelerated permitting, infrastructure prioritization, and regulatory advantages that improve project economics. Meanwhile, global supply chains could face mounting compliance burdens tied to traceability, audits, certification, and carbon reporting.

Investment Diversion

Capital, suppliers, talent, and manufacturing clusters are likely to concentrate around favored European industrial hubs. Early movers may secure subsidies, strategic sites, preferred supplier relationships, and advantages that become difficult to replicate later.

Supply-Chain Redesign

The proposal includes provisions tied to local employment, EU sourcing, domestic R&D spending, and ownership structures for major investments. Many firms may discover that some degree of localization becomes necessary simply to preserve long-term competitiveness.

Manufacturers may increasingly need to replace globally integrated supply chains with parallel regional operating models built around competing geopolitical blocs, each with distinct industrial policies, regulatory regimes, and supply-chain requirements. That shift may challenge one of the foundational assumptions of modern operations management: that maximum efficiency always minimizes cost.

For decades, manufacturers pursued hyper-optimized just-in-time supply chains with minimal redundancy. In a world of tariffs, export controls, industrial policy, geopolitical shocks, and supply disruptions, “just in case” capabilities may become strategically and financially superior to “just in time” efficiency. Global fragility may prove more expensive than regional redundancy.

For C-suite executives, this creates a profoundly uncomfortable reality: the operating model that dominated the post-Cold War economy may no longer fit the geopolitical realities now replacing it.

Should Europe remain primarily an export market, or become a localized manufacturing base? Which technologies and components are strategically important enough to move? Which supply-chain steps must become European, and which can remain global? How much redundancy should companies build into their operations? And perhaps most importantly: how much strategic flexibility should companies preserve before committing capital?

These are no longer operational questions delegated to procurement teams or regional business units but board-level strategic decisions with multi-billion-dollar implications.

Traditional strategic planning tools are poorly suited for this environment. Executives will need to think more in terms of scenarios than forecasts. Geopolitical foresight and adaptability are rapidly becoming core organizational capabilities in their own right.

More broadly, executives should begin preparing for a world in which competitiveness depends not only on operational excellence, but also on geopolitical alignment.

More importantly, executives should resist the temptation to wait for perfect clarity, as the IAA is still evolving politically. EU member states remain divided over how far and how fast the framework should go. France favors a stricter EU-only approach, while countries such as Germany and the Netherlands advocate greater flexibility and WTO compatibility. As with many EU industrial initiatives, implementation may ultimately prove more fragmented and uneven than the initial political ambition suggests. Final adoption may still be more than a year away.

Yet this creates a strategic paradox: by the time the rules are fully finalized, the best sites, suppliers, partnerships, and incentive pools may already be spoken for.

That creates a narrow but important strategic window. Over the next six months, companies still have time to assess exposure, preserve flexibility, secure partnerships, and position investments before industrial ecosystems begin to solidify around the new framework. The smartest firms will likely focus first on “no-regret moves”: identifying vulnerable supply-chain bottlenecks, improving traceability capabilities, evaluating alternative manufacturing footprints, and exploring strategic partnerships inside Europe.

Companies that wait for regulatory certainty before adapting may ultimately discover that the geopolitical map of global manufacturing has already been redrawn without them.

This article was originally published in IndustryWeek

The incident is a vivid example of a widespread problem in all types of consequential decisions: leaders operating with a closed mental model. In complex negotiations, this human tendency often emerges through failure to consider actions by parties not “at the table.” Good negotiators try to consider the perspectives and interests of both their “side” and the people they are negotiating with. They need to be aware of the full range of levers that each party has. But it is easy to forget to think about outside players and what they can do, yet these players can and often do dramatically affect the outcome. Regulators reject mergers, workers reject tentative contracts, customers or partners or suppliers or dealers take actions that make one or both parties regret their deal.

Forgetting a Player Kills an Initiative: GM Retail Holdings

General Motors decided in 1998 to buy and run some dealerships. There were good strategic reasons – get closer to the customer, test retail ideas, obtain important data. It knew that its franchised dealers would dislike the idea, but it was legal in most states. An experienced team was assembled, locations were identified, and financing was arranged. A splashy announcement of the new GM Retail Holdings was made - unwisely when GM’s National Dealer Council was in town. Within 48 hours, GM regretted the decision and never bought a dealership; a few months later, GM’s CEO apologized to the dealers and said GM would never try to own dealerships again.

What happened in the 48 hours? GM had underestimated dealers’ fury – organized by the Dealer Council - and their willingness to use levers they controlled to punish GM if it went forward. Even worse, GM forgot a player – state legislatures. Bills were introduced in six states – with the support of GM dealers – to make it illegal for an OEM to “compete” with franchised dealers. GM quickly realized that the bills would pass given the political power of dealers and that other states would follow. Forgetting a player led to a quick and costly end to a major initiative.

Outside Parties Improving Outcomes: The Detroit Bankruptcy Game

Considering outside players goes beyond protecting against disaster. Leveraging outsiders strategically can also help one or even both sides improve the result of the negotiation. A remarkable example occurred during Detroit’s bankruptcy in 2013-14. The emergency manager had difficult negotiations with creditors and unions, though he won some legal rulings. He proposed a plan that included significant spending cuts and deep cuts to pensions for city workers, but creditors pressed for the sale of the collection of the Detroit Institute of Arts (DIA), owned by the city for historical reasons.

A mediator appointed by the bankruptcy judge came up with the idea of asking a group of major foundations to contribute $350M into the city pension funds as part of a “grand bargain” with DIA donors contributing $100M and the state $350M. Pensions would still be cut by 4.5%, and the DIA would no longer be owned by the city but would keep the art. All the contributions were contingent on votes by city workers and bankruptcy judge approval.

The Grand Bargain was successful and Detroit emerged from bankruptcy. The mediator produced this remarkable result by inventing (!) a player – the foundations. He used this new player to leverage other outsiders – DIA donors and the state – to take favorable actions and then going back to the negotiating parties with a “take it or leave it” deal that was better than they could achieve on their own. Negotiators can often leverage the threat or promise of outside action to their benefit.

Win by 360° Planning

How can business and negotiation leaders manage the risks and opportunities from outside players? The key is analysis before negotiations start. Negotiation teams that consider the full landscape prior to bargaining will have a competitive advantage in most complex negotiations. They will often be able to influence – or at least predict – the behavior of important outside players.

A structured process in negotiation preparation improves the outcome, especially if it engages a cross-functional group of internal experts and decision makers. The range of experience and insight will help ensure that players or levers do not “fall through the cracks” and that the preferences of all players (including your own!) are assessed as accurately as possible.

The first step is to consider all the players and their potential strategic and tactical actions – their levers. Then the team should put itself into the shoes of all the players, thinking through how they feel about each of the levers, whether or not they control them. Do they want that lever pulled? How important – out of happiness or fear – is it for that player relative to the other levers? After these steps, the team will have a rich model full of useful insight.

The tools of game theory would enable the team to go even further. Game theory would identify which levers will be pulled and ways the outcome might be improved. Useful ideas for strategy and tactics both at the bargaining table and away from it are likely to emerge.

The Bottom Line

The players who derail or transform your negotiation are often ones you never thought to consider. The antidote to that blind spot is surprisingly simple — build the full map before you sit down at the table.

Prediction markets aggregate information

Prediction markets, like the stock market, aggregate expert knowledge and public and semi-public information. If the probability of an event is different than what is reflected in the price, people will bet to make a profit. Their bets will move the prediction market price toward the “correct” value. If the probability changes due to new information, the prediction market price will quickly adjust. If the market is mature and the number of contracts is large, experts have a financial incentive to participate and the market price should be an accurate real-time estimate of the probability of the event. This estimate –and its responsiveness to information – is the key source of business value.

Using prediction markets for crisis preparation and management

To understand the potential of prediction markets for crisis preparation and management, consider current geopolitical uncertainty. For example, many business decisions depend on North American trade and tariffs. Board and CEOs will want assurance that the company will be able to manage all the ramifications of USMCA scenarios, up to and including a full US exit. For many companies, however, going beyond a high-level analysis would be difficult and time-consuming and implementing full mitigation would be quite costly. The leadership could set “DefCon” levels [similar to the U.S. military’s readiness alert system], with higher risk triggering more aggressive and painful mitigation efforts. If the prediction markets are active, the probability of particular USMCA outcomes would be an excellent objective real-time signal for triggering the appropriate response.

This is not an isolated case. Whenever a change in the probability of an event would trigger a decision or action, a corresponding prediction market would be valuable information. This is true both of events that occur – Trump’s election or Russia’s invasion of Ukraine – and fears that do not materialize, like the collapse of the Euro in 2012 or a Korea crisis in 2018.

Probabilities from prediction markets could also improve scenario analysis and wargaming. If a consequential event has high enough probability of occurring (say at least 10%) that would be the signal to do a scenario analysis of the event, with the probability also helping mitigation decisions.

Improving forecasts with prediction markets

Decisions on product programs, capacity investments, hiring, and business plans all rely on forecasts of industry volume, market share, pricing, and costs. Too often these forecasts have an optimistic bias, reflecting pressure on the forecasters and leaders to meet targets and get funding approved. There is also a bias towards smooth growth, discounting disruptive events or technologies. Five-year plans often look like hockey sticks, with conservative estimates for the budget year (so that bonus targets are reached) and bright optimism beyond. The next year the hockey stick has moved out a year.

Public prediction markets might help correct some of the bias, particularly as the number of contracts and market participants increases. A market forecast of U.S. vehicle or full-size truck sales in 2027 or 2030 could help automakers and suppliers plan. Probabilities of election outcomes or high oil prices or a Taiwan invasion could help select and calibrate stress-test scenarios. One challenge to such applications is that the event contract might not exist for many risks relevant to the company, particularly for the longer horizon of investment decisions. Companies should experiment with establishing event contracts tied to information they need in decision-making.

Internal prediction markets

Public markets could be supplemented with internal “markets”. Employees could place bets, for example, on whether or not a particular market share would be achieved. While such markets could potentially overcome bias, there would be challenges to making it work. Companies would need to worry about insider trading or market manipulation or distorting individual incentives for company success. The best use-case might be an external event where there is diffuse insight within the company. For example, “will the new product launch of a leading competitor increase its market share by more than 1 point next year.” Companies might want to pilot an internal market with a group of internal experts.

Concerns with prediction markets

The growth of prediction markets has raised understandable and appropriate concerns about rules, regulatory arbitrage, insider trading, and market manipulation. The indictment of a U.S. soldier for allegedly profiting from insider information on the Venezuela operation is only one instance. But for a business using prediction markets for information rather than to make bets, insider trading is not a concern. Kalshi is currently regulated by the CFTC though the rules are in flux, while Polymarket is currently outside U.S. jurisdiction. Congress and states are trying to establish rules and guardrails. The inevitable increase in oversight and regulation of these markets will be good news for the same reason it is in the stock market; it will increase trust and transparency, encouraging wide participation.

The Bottom Line

Using the power of markets to find the probabilities of events is an important advance. Businesses should try to develop markets in event probabilities that could affect decisions. As prediction markets mature, they should monitor developments with the goal of leveraging this innovative information source.

Welcome to the May issue of The Briefing Table where we review each month’s major events from a strategic and risk management perspective.

April was a month where a familiar pattern reasserted itself. A ceasefire that was supposed to restore normal conditions didn’t. A tariff deadline that was supposed to be about pharmaceutical companies turned out to affect almost every large employer in the country. An AI model arrived that its own developer deemed too dangerous to release. A regulatory deadline that had been on the calendar for two years became a deal-room variable. And a Federal Reserve succession that was supposed to be a procedural formality became the first fully partisan confirmation vote for a Fed chair in American history.

These five forces, each a deferred cost arriving on a compressed timeline, are the subject of this issue.

1) Anthropic Mythos: The Threat That Wasn’t Supposed to Arrive This Year

Ask most CIOs what they expected from AI and cybersecurity in 2026, and the answer would have been incremental. A genuine capability breakthrough, serious enough to prompt a major AI lab to withhold a model from the public entirely, was not in the forecast.

It arrived anyway.

On April 7^th, Anthropic announced Claude Mythos Preview and declined to release it publicly. Whereas OpenAI’s temporary suppression of GPT-2 in 2019 was widely derided as theatrical, no one is mocking Anthropic. During testing, Mythos identified and exploited zero-day vulnerabilities across every major operating system and browser, including bugs that had survived decades of human review. The UK’s AI Security Institute found it succeeded at expert-level hacking tasks 73% of the time. One year ago, no AI model could complete those tasks at all.

In response, Anthropic launched Project Glasswing, committing $100M in usage credits to let AWS, Cisco, CrowdStrike, and forty other organizations use Mythos defensively to scan critical infrastructure before adversaries could exploit the same capabilities. Anthropic did not train Mythos to have offensive security capabilities. They emerged from general improvements in reasoning and code, which means every subsequent frontier model will have them whether its developer intends it to or not. The controlled release window is also shorter than it appears: independent researchers have shown open-weight models just three to five months behind the frontier can replicate much of the same analysis. U.S. regulators have drawn their own conclusion and are already urging major financial institutions to begin controlled testing now.

The temptation is to file this under “cybersecurity” and hand it to the CISO. That would be a mistake. Mythos-class discovery will flood enterprise vulnerability queues with findings that security teams have no additional capacity to remediate. More acutely, the agentic systems companies are deploying to automate workflows are now the most valuable targets for adversaries. A single prompt injection can co-opt an organization’s highest-privileged digital actor. Last month’s issue argued that the risk in agentic AI is in what agents do, not what they are. Mythos makes that argument viscerally. Ungoverned agents are unlocked doors, and the lockpicks just got dramatically better.

There is also a competitive dimension many C-Suite executives are missing entirely. The organizations currently using Mythos-class capabilities defensively are building a security posture their peers will not have for another year or two. This is the same structural dynamic that played out with every previous generation of security infrastructure. The firms that built the capability before the breaches gained lasting advantages. The ones that waited explained the gap to senior executives. The window to be the former rather than the latter is open now. It will not remain so.

Bottom Line: Commission an agentic AI inventory audit now; what agents are running, what systems they can access, what actions they can take without human review. The organizations that act in the next ninety days will be the ones that don’t have to explain why they didn’t.

2) Pharmaceutical Tariffs: This Is Not a Healthcare Story

Most coverage of the April 2^nd pharmaceutical tariff proclamation has treated it as a healthcare sector story. It is not. It is an employer story, a supply chain story, and a strategic optionality story with a deadline.

Under Section 232, the administration imposed 100% tariffs on imported patented pharmaceuticals and APIs, effective July 31st for large companies. The structure rewards speed: a 0% rate through 2029 for a MFN pricing agreement with HHS and a domestic onshoring plan with Commerce versus 20% for an onshoring plan only. Do neither and face 100%. This is the sectoral escalation we previewed in February’s edition under “The End of Emergency Tariffs.” Parallel Section 232 investigations are already underway in medical consumables, devices, and robotics – i.e., the pharmaceutical action is a template, not a one-off.

Importantly, the MFN/onshoring path is not a compliance exercise. It is a negotiation, and the tariff framework is effectively offering a four-year subsidy, zero tariff liability, in exchange for policy alignment. Organizations recognizing this and moving first will secure favorable terms. Those treating it as a burden to manage will pay the default rate. In addition, the majority of large U.S. companies self-insure their health benefits. Specialty biologics and patented therapies facing the 100% default represent a disproportionate share of those plans’ costs. This is a human capital cost problem, not a healthcare problem, and it arrives before year-end.

Bottom Line: Three parallel tracks: classify your exposure across patented pharmaceuticals and APIs; assess the MFN/onshoring compliance path, because the zero-rate tier requires simultaneous engagement with two federal agencies before July 31; and complete the supply chain audit begun in April; Chinese and Indian API production is a dependency as significant as Gulf-region helium and sulfur.

3) The Iran Ceasefire: Beijing Is the Story Now

Last month, we wrote: “Do not treat a sudden ceasefire as a return to normal.” A ceasefire arrived within two weeks and has been violated by both parties. As of publication, Iran submitted a formal proposal to reopen the Strait in exchange for the U.S. lifting its naval blockade, while deferring nuclear discussions, but Washington effectively rejected it. Meanwhile, Pakistan has stood down the security infrastructure it assembled for a second round of talks and there is no next meeting scheduled. The new baseline is not a ceasefire but a siege.

Just below the surface, a new regional power structure is taking shape. China did not merely benefit from this conflict. It is now shaping its resolution. Beijing’s co-mediator role in Islamabad was confirmed by the White House. Iran’s oil exports have been settled increasingly in renminbi throughout, a practical arrangement not reverting when hostilities end. The country helping end a war earns diplomatic capital persisting for years. China is accumulating it while the United States absorbs the costs.

Jeff Sachs observed in his April interview with C-Suite that the dollar’s share of global reserve holdings has been declining for two decades and that the pace is accelerating. The Iran conflict has now added a live demonstration that the renminbi can function as a credible settlement currency in the world’s most strategically sensitive energy corridor. Sachs’s projection - renminbi exceeding 20% of international trade settlement within a decade, the dollar falling below 50% - now feels less like a long-range forecast and more like an extrapolation of something already in progress. The right move for corporate treasurers, he advised, is to begin building renminbi-denominated banking capacity now. China’s central role in the ceasefire process makes the timeline feel urgent in a way that abstract reserve currency projections do not.

The practical prescription is the one from last month, now sharper: build explicitly for both endpoints. A rapid normalization would compress oil prices, normalize shipping, and release Gulf sovereign wealth into global markets. The 14% intraday swing in early April showed how fast transitions happen. The side you are not prepared for is the one that will cost you.

Bottom Line: The Iran story has two chapters. The first has been modeled: Hormuz disruption, energy costs, supply chain exposure. The second typically has not: China as indispensable regional broker, renminbi advancing in energy settlement, dollar primacy eroding in the one market where it was most entrenched. It should be.

4) The EU AI Act: Already a Valuation Variable

The EU AI Act’s August 2^nd enforcement deadline is now approximately 100 days out, and European deal rooms are already pricing compliance posture into transactions. One Q1 transaction closed at a €7 million discount written into the SPA as a specific AI Act indemnity. One HR analytics asset was pulled from market after four bidders flagged the same Annex III gap. An Austrian lender commanded a two-turn valuation premium because its credit model had been documented against Article 10 since late 2024.

High-risk categories, including hiring, credit, biometric identification, and critical infrastructure, require conformity assessments, technical documentation, CE marking, and EU database registration before August 2nd, with penalties reaching €15 million or 3% of global turnover. The Act applies extraterritorially.

Over half of organizations still lack systematic AI inventories. Without an inventory, risk classification is impossible. Conformity assessment takes six to twelve months. The arithmetic is unambiguous for anyone who has not started.

For organizations conducting M&A or operating in EU-regulated sectors, the compliance posture is no longer a legal question. It is a valuation question. And it lands on the same workforce managing every other demand in this issue. This publication’s March interview with Sponge Group found roughly 60% of employees describe themselves as ‘okay’ or ‘indifferent’ - what they called ‘Generation Numb’ - with change capacity quietly depleted across the organization. The executives who will deliver AI Act readiness within 100 days are not those who issue another mandate from the top. They are those who give their teams agency in how it gets done.

Bottom Line: Start with the inventory. For M&A teams, the question to every diligence counterparty is: show me your AI registry and risk classification. The difference between a two-turn premium and a seven-figure indemnity is documentation that either exists or doesn’t.

The Fed: A Convention Has Ended

Every Fed chair confirmation has included bipartisan support. That norm is gone now, with Kevin Warsh clearing the Senate Banking Committee on April 29^th on a straight party-line vote, enabled by the DOJ’s decision to drop its criminal investigation into incumbent Chairman Powell to clear Senator Tillis’s hold. That sequence set a precedent: threatening a sitting Fed chair with criminal prosecution is now an established executive branch tool. The Fed held rates steady at its final Powell meeting. Futures markets price a largely steady path through 2027. Trump wants 1%. That gap is the uncertainty premium embedded in every capital allocation decision right now.

The Federal Reserve Act is deliberately ambiguous on presidential removal authority. It has rested on political convention: an implicit agreement between both parties that the economic cost of undermining the central bank’s credibility exceeded any short-term political gain. That agreement ended on April 29th. Whether Warsh honors the substance of that convention while the form of it has been broken is now the central question for anyone managing dollar exposure, long-duration assets, or capital allocation decisions that depend on reliable inflation anchoring over the next decade.

Bottom Line: Watch Warsh’s first public statement. Does he frame the Fed’s mission in terms of the president’s agenda or its dual mandate? The former signals a repricing of dollar assets and U.S. sovereign risk. The latter suggests the guardrails held. Either way, five decades of institutional convention ended April 29^th.

Key Take-Aways

• Mythos has crossed a capability threshold the industry didn’t expect this year. The defensive window is months, not years. Ungoverned agents are unlocked doors. Commission the inventory audit.

• Pharmaceutical tariffs are not a healthcare story but a self-insured employer story with a July 31^st deadline. The compliance path is a negotiation with a hidden reward for speed. Start both tracks now.

• The Iran stalemate has hardened into a siege. China is the structural winner. Build for both endpoints as rapid normalization will wrong-foot you as badly as escalation if you’re not prepared.

• The EU AI Act has moved from legal to financial. Deal rooms are already pricing the gap. Start with the inventory. Everything else follows.

• The Fed succession is not a political story. It is a capital markets story. Watch Warsh’s first statement. A five-decade convention ended last week regardless of what he says next.

All five forces share a structure: deferred costs, compressed timelines, stretched execution capacity. The executives who navigate this environment best will be those who see that pattern and build resilience before the next deadline, not after.

In the spring of 2021, a ransomware group called DarkSide broke into Colonial Pipeline’s network through a single compromised password on a legacy VPN account that the company hadn’t known was still active. Within hours, 5,500 miles of fuel pipeline serving the U.S. East Coast went offline. Gas stations ran dry from Virginia to Florida. The CEO called the White House. Colonial paid $4.4 million in ransom, in Bitcoin, within 24 hours of the attack.

DarkSide didn’t need sophisticated AI to pull this off. They needed one unpatched gap in a sprawling, underdocumented legacy system. Now imagine what they could do with it.

AI has just handed attackers a capability that changes the game: the ability to scan millions of lines of code, including decades of custom, undocumented legacy code, and identify exploitable vulnerabilities at machine speed. At the same time, the very same AI tools that create this threat are reshaping how companies build products, manage data, and run operations. Companies that don’t deploy them will fall behind. Companies that do will expand their attack surface. There is no path that avoids both risks simultaneously.

What does this mean for senior business executives?

§AI has triggered a step-function increase in cybersecurity risk on three distinct vectors: AI’s ability to discover and exploit software vulnerabilities, the data security risks embedded in AI agents, and the speed at which AI writes code that may not be secure.

§ The tension is structural, not solvable. The same AI capabilities that create new vulnerabilities also create competitive value. Companies that try to eliminate AI cybersecurity risk entirely will simply be outcompeted. The question is not whether to accept risk, but which risks to accept, and where.

§ Large, established companies face a structural cybersecurity disadvantage relative to startups that investment alone cannot overcome. This is not a temporary gap. It is a permanent feature of competing with decades of accumulated technical debt.

§ The right response is triage: identify mission-critical systems and data, concentrate defenses accordingly, and elevate these decisions from the CISO’s office to the boardroom, where they belong.

Three Vectors of AI Cyber Risk

Cybersecurity risk has always been a fact of corporate life. What has changed is the magnitude, the speed, and, most importantly, the structural nature of the threat. AI has not simply made existing attacks easier. It has opened attack surfaces that were previously unmappable, enabled new categories of intrusion, and done so at precisely the moment when companies are most aggressively expanding those surfaces in pursuit of competitive advantage. Three vectors account for most of the new exposure.

1. AI-Powered Vulnerability Discovery

Anthropic recently revealed that its Mythos AI model is extraordinarily capable at identifying and potentially exploiting security flaws in software, having found vulnerabilities in every system it tested. The announcement was striking enough that Anthropic initially restricted Mythos to a handful of companies, including Microsoft and Google, so they could use it defensively: racing to find and fix their own flaws before the model fell into other hands.

That containment has already begun to erode. Unauthorized users claim to have accessed Mythos, and other AI companies and governments have developed comparable capabilities, or will very shortly. The target space is staggeringly large and, for most enterprises, effectively unmappable: decades of custom, undocumented code whose vulnerabilities have never been systematically catalogued, let alone remediated. AI models can scan this code for weaknesses far faster than any security team can respond. The Colonial Pipeline attackers found one forgotten door. AI-powered tools can try every door simultaneously.

2. The AI Agent “Lethal Trifecta”

AI agents, tools that can autonomously research, analyze, and act on user requests, introduce a different and less understood class of risk. Security researcher Simon Willison describes a “lethal trifecta” of agent capabilities that, when combined, create a reliable attack pathway: access to private data, exposure to untrusted content, and the ability to communicate externally. Any agent that combines all three can be manipulated by an attacker into accessing your private data and sending it outside the organization. The attacker need not penetrate your perimeter. They simply need to trick your agent.

The uncomfortable arithmetic: the risk grows in direct proportion to how useful the agent is. Some tech companies have already given AI agents complete access to internal systems, including email. The more data an agent can reach, the more productive it becomes, and the more catastrophic a breach becomes. As agents proliferate and employees grow more skilled in using them, the exposure compounds. The very behavior that makes agents powerful, autonomous action across large, integrated datasets, is precisely what makes them dangerous if compromised.

3. AI-Generated Code and the Velocity Problem

Services like Claude Code are already capable of writing production-quality code at a pace no human team can match, and capabilities are improving rapidly. Companies that insist on purely human-written code will fall behind. But speed and security are in tension. Code written rapidly and at volume is not necessarily secure. The pace of new code creation can, and in many companies already does, outrun the ability of security teams to audit it. The result is an attack surface that expands from the inside, driven not by attackers but by the company’s own development velocity.

The Legacy Disadvantage: A Gap That Cannot Be Closed

The Colonial Pipeline attack is instructive not because it was sophisticated - it wasn’t - but because it illustrated a structural truth about large, established companies: their accumulated technical debt is an attack surface that grows faster than it can be managed, even without AI. DarkSide didn’t breach Colonial’s state-of-the-art systems. They found a forgotten door that had been left unlocked, probably for years, by people who no longer worked there.

Now consider what AI-powered vulnerability scanning does to that equation. A typical legacy enterprise carries tens of millions of lines of custom code, accumulated over three or four decades, written by developers long since departed, documented incompletely if at all. Previously, finding exploitable vulnerabilities in that environment required skilled human attackers investing significant time. AI tools can now scan that entire codebase in hours and surface a prioritized list of weaknesses. The 2024 Change Healthcare breach, which compromised medical records for nearly one in three Americans and has cost UnitedHealth Group an estimated $3.1 billion in its first year of remediation, was enabled by precisely this kind of legacy exposure: a portal that lacked multi-factor authentication. A forgotten door, again.

The critical distinction is between an operational gap and a structural one. An operational gap, such as insufficient investment, inadequate staffing, or poor processes, can be closed with resources and management attention. A structural gap cannot, at least not fully. Startups build on modern cloud infrastructure with security baked in from day one. They have smaller, better-documented codebases with no forgotten VPN accounts, no legacy portals, no decade-old integrations nobody fully understands anymore. Legacy enterprises cannot replicate these conditions regardless of how much they spend. They can remediate known vulnerabilities, modernize specific systems, and build stronger detection and response capabilities. But the underlying reality, a vast, partially unmappable attack surface that AI-powered tools can probe faster than security teams can defend, does not fundamentally change.

The appropriate board response is neither denial nor despair. It is clear-eyed acceptance of the structural reality, followed by deliberate, prioritized risk management.

Which Industries Face the Sharpest Exposure

AI-driven cybersecurity risk is not uniformly distributed. Four sectors face a materially elevated threat profile, each for structural rather than incidental reasons.

• Healthcare and life sciences face the most acute combination of legacy infrastructure, high-value data, and regulatory consequence. Electronic health record systems at many large hospital networks were built on architectures that predate modern security standards by decades. Patient data commands premium prices in criminal markets: a complete medical record is worth multiples of a credit card number, and every successful breach triggers mandatory public notification. Change Healthcare demonstrated that a single compromised third-party system can cascade into industry-wide disruption of extraordinary scale.

• Financial services carry deep legacy exposure in core banking and payments infrastructure. Some large institutions still run COBOL-based mainframes written before most of their current employees were born. The sector also faces the agent data aggregation risk acutely: AI tools that synthesize transaction histories, credit profiles, and customer communications are enormously valuable for fraud detection and personalization, and enormously dangerous if compromised. Post-breach regulatory accountability now routinely reaches above the CISO level.

• Critical infrastructure, including energy, utilities, and transportation, faces a threat that extends beyond data breach into physical consequence. Operational technology systems controlling pipelines, power grids, and water treatment facilities were designed for reliability, not cybersecurity, and many were never intended to be networked at all. The convergence of IT and OT systems, accelerated by AI-powered monitoring tools, is creating new attack pathways into systems whose compromise carries life-safety implications. Colonial Pipeline was, in hindsight, a warning shot.

• Manufacturing and industrial companies are in the early stages of deploying AI agents across supply chain management, quality control, and production optimization, which puts them squarely in the velocity problem described above. Custom ERP and manufacturing execution systems built over decades create substantial legacy exposure, and competitive pressure to move fast means governance typically lags capability by a year or more. Intellectual property, including product designs, process specifications, and supplier relationships, is an underappreciated target that AI-powered attackers can now pursue systematically.

Other sectors, including retail, professional services, and media, face real exposure as well. But executives in these four industries should treat AI cybersecurity as an immediate board-level priority. Not a medium-term one.

Recommendations for Managing the Heightened Risk

The best strategy starts with two honest recognitions: the inherent risk has increased dramatically, and fully eliminating it is neither possible nor desirable, because the same AI capabilities that create vulnerabilities also create competitive value no company can afford to leave on the table. New security measures are nonetheless urgent. The question is where to concentrate them.

Step Zero: Triage

Before addressing any threat vector, every company needs to know what it is actually protecting. Not all systems carry equal risk, and treating them as though they do wastes resources and creates false security. A breach of marketing analytics is a bad day. A breach of customer health records, operational control systems, or core financial infrastructure can be existential. Identify mission-critical systems and data first, apply a materially higher standard of protection to them, and build the rest of your program around that foundation. In the four high-exposure sectors above, this exercise should already be complete.

Addressing the Three Vectors

• Security vulnerabilities: Use the best available AI model to test your own systems for weaknesses. This is table stakes now, not an advanced practice. Dedicate people to reviewing the findings and acting on them. Because AI models improve continuously and produce different results each time they run, repeat this process regularly for mission-critical systems, weekly for the highest-risk environments. The logic is simple: use the same tools your attackers are using, before they use them on you.

• AI agent data access: Establish and enforce rules governing what data your AI agents can access and whether they can communicate externally. Try to eliminate at least one leg of the lethal trifecta for every agent your employees use. For new deployments, treat the full combination of private data access, untrusted content exposure, and external communication as a disqualifying configuration unless a specific business case justifies it. Consider denying agents access to your most sensitive data entirely, even at some cost to productivity. That tradeoff is usually worth making.

• AI-generated code: Require security review of AI-generated code before it is deployed to systems that touch sensitive data. Where feasible, use AI models to test new code for vulnerabilities before it goes into production, matching AI-speed generation with AI-speed review. The goal is not to slow development. It is to make sure the attack surface does not expand faster than your security team can track it.

Elevate This to the Board

The most consequential governance change required is also the simplest to state: these decisions can no longer be delegated to the CISO. The tradeoffs between AI deployment speed and security exposure, between data aggregation and breach risk, between competitive positioning and risk tolerance, involve revenue, reputation, regulatory exposure, and in some industries, physical safety. They belong at the CEO and board level. Companies that continue to treat cybersecurity as an IT problem will make these tradeoffs poorly, either accepting too much risk in the wrong places or surrendering competitive ground by being too cautious in the right ones.

None of these steps will eliminate AI cybersecurity risk, and they will probably not reduce residual risk to where it was eighteen months ago. But failing to act creates the potential for catastrophic exposure. And excessive caution carries its own risk: falling behind competitors who are moving faster.

The Colonial Pipeline CEO had 24 hours to decide whether to pay $4.4 million in Bitcoin to people he’d never meet, to restore systems his team didn’t fully understand, through a vulnerability nobody had known existed. He paid. None of the risks described in this article are hypothetical. They are extensions of dynamics already in motion, now sharply accelerating. The companies that navigate this environment best will not be those that eliminate the risk. They will be those that understand it clearly enough to make the right tradeoffs, and who make them deliberately, before events make the choices for them.

A July 2025 study from MIT’s Media Lab, Project NANDA, found that roughly 95% of all AI initiatives deliver little or no measurable business value. Not a modest disappointment. Not a case of overblown expectations. Ninety-five percent. More disturbingly, the finding is not even new: an MIT Sloan study back in 2019 found that 40% of companies reported no business gain from machine learning despite significant investment.

What does this mean for senior business executives?

§ AI’s failure is not a technology problem, it is an organizational one. The technology works. Most companies simply don’t know how to deploy it in ways that actually move the needle, and their existing operating models and cultures actively resist the kind of change that would allow them to do so.

§ The potential, however, is enormous and real. Companies that master AI deployment - not experimentation, but genuine at-scale deployment - can expect productivity gains in innovation and R&D of roughly 40%, as well as material improvements in manufacturing quality, supply chain resilience, and customer lifetime value. A handful of early movers are already pulling ahead.

§ Closing the gap requires executives to stop treating AI as a technology initiative and start treating it as an organizational capability: the deliberate combination of technology, process, data, and people aligned around specific, measurable business outcomes.

The Promise Is Real And So Is the Chasm

The skeptics who dismiss AI as yet another overhyped technology cycle are simply wrong. The potential is genuine, it is large, and, in certain domains, nothing short of transformational.

Consider the automotive industry, a useful proxy for large, complex, capital-intensive enterprises. Today, most automakers are capturing incremental AI gains through, for example, engineering copilots, supply chain planning, and in-vehicle personalization. Useful certainly, but not yet the stuff of competitive revolution. The more consequential battlegrounds lie ahead: AI-enabled autonomous vehicle validation, connected-vehicle monetization, and fleet intelligence in the medium term; fully autonomous driving, AI-accelerated materials discovery, and virtual twins replacing physical testing in the longer term. None of this is science fiction. Most of it is already being prototyped. The question is not whether these shifts will happen, but who will be positioned to lead them and who will be left managing the consequences.

More broadly, AI’s strategic role in any enterprise ascends through four layers of increasing consequence. Personal productivity (individual employees using AI assistants) is the entry point. Operational efficiency follows: AI embedded in manufacturing, quality control, and supply chain delivering real cost reduction. Revenue impact comes next, as AI powers personalized experiences, dynamic pricing, and connected-product monetization. At the top sits strategic innovation: AI enabling entirely new products, business models, and industry ecosystems. This is where competitive repositioning actually happens. The trouble is that most companies are still camped on the ground floor, having deployed AI for individual productivity, run a handful of pilots in operations, and called it a day. The upper floors, where the real value lives, remain largely unoccupied.

“AI can unlock roughly 40% productivity gains in innovation and R&D alone. Most companies are leaving nearly all of that on the table.”

The Hard Truth: 95% of AI Initiatives Fail

Here is the reality check that most AI enthusiasts would prefer to skip. For all the investment, for all the pilots, for all the breathless announcements, the overwhelming majority of AI initiatives are not delivering meaningful business impact. The MIT Project NANDA findings are unambiguous: global AI investment has reached $35 billion; 80% of companies report no or minimal AI impact; 95% of AI pilots are generating little or no significant value. Only 5% are getting it right.

This is not primarily a technology problem. The AI itself (the models, the infrastructure, the tools) is already far more capable than most organizations know what to do with. The bottleneck is not the technology. It is the organization. And this pattern, it turns out, is not new at all.

In 1987, Nobel laureate Robert Solow made a quietly devastating observation about the Information Age: “You can see the computer age everywhere but in the productivity statistics.” Decades of investment in transistors, microprocessors, and mainframes had been expected to produce a great surge in productivity, and yet the statistics showed nothing of the kind. The dot-com boom of the 1990s followed the same script at higher velocity: extraordinary technology, extraordinary hype, billions invested, and then the crash of 2000–2001. The Nasdaq lost 78% of its value. Hundreds of companies evaporated. And yet, within a few years, the productivity gains that the skeptics had declared a mirage finally arrived: broad, sustained, and transformational. Amazon, Google, and the modern internet economy were all built in the rubble of the bust. The technology had been real all along. The organizations, the infrastructure, and the business models simply needed time to catch up.

History suggests that what we may be witnessing now has a name. In the diffusion of transformative technologies, initial enthusiasm drives rapid early adoption among pioneers, only to be followed by a dip as the early majority hesitates and disillusionment sets in, before a second, more durable wave of growth eventually materializes. Such “saddle patterns” are far more common than most executives appreciate. In fact, up to half of all high-tech products follow this path, with adoption sometimes dropping by 25% or more during the saddle before recovering.

The current AI moment has the hallmarks of a saddle in progress: extraordinary early momentum, widening disillusionment as the gap between promise and delivery becomes undeniable, and the very real possibility of a near-term correction as inflated valuations, boardroom fatigue, and unmet expectations collide. None of this necessarily means that the underlying technology is flawed or that the long-term transformation is in doubt. It does mean, as it did with the internet at the turn of the century and electric vehicles more recently, that the full realization of the hype is simply taking longer, and demanding more, than the hype itself suggested.

Four failure modes recur with depressing consistency across industries.

The Pilot Factory is probably the most common. Organizations launch dozens of interesting AI experiments, celebrate a few early wins, and then quietly watch the initiatives peter out without ever achieving scale. Pilots become a substitute for deployment rather than a path to it. The organization is perpetually “exploring” AI, which sounds dynamic and innovative but actually means nothing changes.

Tech-led, not Value-led is the second failure mode. AI pilots are designed as technology experiments - testing the capabilities of a new model, validating a use case in the abstract - rather than as value creation experiments with specific business targets, clear ownership, and meaningful accountability. The technology team declares success. The business sees no difference in its P&L.

Data Fragmentation is the silent killer. AI runs on data. Organizations that have spent decades accumulating data in siloed, inconsistent, poorly governed repositories find that their AI tools are only as good as the inputs they receive - which is to say, not very good. Garbage in, garbage out is not a new principle, but AI has a remarkable talent for amplifying it.

The Missing Adoption Engine is the fourth and perhaps most consequential failure mode. Even when AI tools work well, organizations fail to embed them in actual business processes and decisions. Individual employees may use AI assistants sporadically. But the AI is not wired into how decisions are actually made, who is accountable for outcomes, and what the incentives are for using it properly. Without an adoption engine - a deliberate, managed process of embedding AI in the organizational fabric - value evaporates.

Confirming the pattern, the MIT NANDA study found that the top barriers to scaling AI in the enterprise were not technical at all. They were challenging change management, lack of executive sponsorship, poor user experience, and user unwillingness to adopt new tools. The culprits, in short, are leadership, culture, and organization: the same forces that have derailed every major transformation wave before this one, and will derail this one too, for companies that fail to take them seriously. Both things are true simultaneously: the productivity gains are real and will eventually arrive, as they did after the dot-com bust; and most organizations are actively ensuring they will not be the ones to capture them.

Winners Redesign the Organization, Not Just the Technology

The companies that are successfully capturing AI value at scale share one defining characteristic: they have stopped treating AI as a technology initiative and started treating it as an organizational capability and redesign challenge. They are not just buying better tools. They are rebuilding how they work.

The research on what actually drives AI value is striking in its clarity, and its inconvenience. The algorithms themselves account for a relatively small share of the value AI can generate. The technology infrastructure required to deploy them accounts for somewhat more. But the overwhelming majority of the value, the part that actually shows up in the P&L, comes from people and organizational change: new ways of working, behavioral shifts, redesigned workflows, and the cultural transformation required to embed AI in how decisions are actually made. Most organizations are investing their attention and capital in the smaller parts and neglecting the larger one. This is why their results are so consistently disappointing, and why no upgrade in model capability will fix the problem.

The architecture of a genuinely AI-enabled organization rests on three interdependent foundations: a shared data foundation (governed, high-quality, accessible assets spanning the entire value chain, not data warehouses owned by IT and begrudgingly shared with business units); a reusable AI platform that prevents every team from reinventing the wheel and allows successful use cases to scale rather than remain one-off experiments; and, most critically, AI embedded in actual decision-making, not sitting alongside real processes as an optional add-on, but wired into the decisions that drive the business, with clear accountability for outcomes.

Supporting this core are two organizational enablers that many companies underestimate. Ways of working must change: AI deployment requires shorter experimentation cycles, genuine tolerance for failure, and rapid iteration - a cultural shift that can be genuinely jarring for organizations built on the rigorous, long-horizon engineering and planning processes that made them successful in the first place. And talent must evolve: the winning formula pairs deep domain expertise with AI and data talent in cross-functional teams. Not AI specialists working in isolation and presenting findings to skeptical business managers, but genuinely integrated teams where domain knowledge and technical capability reinforce each other.

Holding all of this together is executive leadership, and, specifically, AI literacy at the top. Not deep technical knowledge; C-suite executives do not need to understand transformer architectures. But enough fluency to ask the right questions, make informed investment decisions, sponsor change rather than merely endorse it, and refuse to accept “we’re running lots of pilots” as a satisfying answer to “what value are we creating?”

“The risk is not that AI fails. It is that value shifts outside your organization’s control while you are still running pilots.”

So What? Implications for C-Suite Executives

The gap between AI’s potential and most organizations’ ability to capture it represents both a significant risk and a significant opportunity, and the window for getting it right is narrowing faster than most boards appreciate. The performance difference between companies that have mastered AI deployment and those still running pilots is not modest. Early movers are generating materially higher revenue growth, larger cost reductions, and substantially higher total shareholder returns than their lagging peers, and they are reinvesting those gains into even greater AI capabilities. The gap is not merely widening. It is compounding.

The urgency is further amplified by what is coming next. The current wave of AI (generative tools, copilots, prediction engines) is already being succeeded by a new generation of agentic AI systems capable of planning, reasoning, and executing complex multi-step tasks with minimal human intervention. Agents that resolve customer issues end-to-end, renegotiate supplier contracts in real time, or redesign production schedules autonomously are no longer theoretical. They are being deployed now, and they are delivering EBITDA gains that dwarf what earlier AI waves produced. For organizations that have not yet crossed the threshold from experimentation to scaled deployment, this matters enormously: they risk being not one but two waves behind before they have mastered the first.

The strategic questions that follow are sharper and more urgent than most boards currently appreciate. Where should your organization choose to differentiate: in core products, software, or system intelligence? What proprietary data advantage can you build across your customers, operations, and value chain, and how quickly? Where should you control versus depend on platform providers and ecosystem partners? How does product development transform from discrete programs to continuous learning systems? And how does AI scale beyond isolated pilots into the core of your operations?

These are not technology questions. They are strategy questions. And they have no universal answers. The right response depends on your current position, your proprietary assets, your competitive context, and your organizational capabilities. But refusing to engage with them - treating AI as a technology project to be managed by the CTO and reported on quarterly - is no longer a viable option.

C-suite executives should consider three immediate priorities:

1) Make the organizational redesign explicit. AI transformation is a leadership challenge, not a technology project. Identify an executive sponsor with real authority, set clear value targets with measurable KPIs, and build the adoption engine that translates pilots into enterprise-wide capability.

2) Stop running more pilots. Assess honestly which of the four failure modes is limiting your organization. The answer will almost certainly involve data quality, operating model design, and culture - not the AI technology itself.

3) Invest in foundations, not just applications. The temptation is to fund visible AI use cases. The discipline is to invest in the underlying data infrastructure, platform, and change management capabilities without which use cases deliver nothing at scale.

The question is no longer whether AI will transform business. It will. The question is whether your organization will be among the 5% that capture the value, or the 95% that unwittingly fund their competitors’ advantage. Unlike the emperor’s clothes, the technology is real this time. The challenge is learning how to wear it.

Jeffrey D. Sachs is a world-renowned economics professor, bestselling author, innovative educator, and global leader in sustainable development. He is University Professor at Columbia University and Director of its Center for Sustainable Development. He is President of the UN Sustainable Development Solutions Network and has advised governments, heads of state, and international institutions on economic development for four decades. His books include The End of Poverty, The Price of Civilization, and The Age of Sustainable Development.

C-Suite: The postwar trading system is fracturing – tariffs, industrial policy, export controls – on a scale we haven’t seen in decades. What is driving this, and what should business leaders understand about it that most don’t?

The postwar trading system was a genuine success story. It produced decades of growth, enabled poorer regions to develop, diffused technology globally, and did exactly what Adam Smith described in 1776: a larger market allows for specialization and mutual benefit. The problem is that this history gets badly misread the moment you adopt a zero-sum mindset, where any gain by China is automatically a loss for America. On that logic, China’s development becomes a threat rather than a shared achievement and letting China into the WTO becomes the greatest mistake America ever made. I think that is a fundamental misunderstanding, and it is driving policy off a cliff.

There are two specific conceptual mistakes at the core of current U.S. trade policy. The first is that China took our jobs and we’re going to get them back. I was recently in a solar module plant outside Shanghai – several football fields in size, one of the top producers of panels in the world – and there were perhaps twenty people in the entire facility. Every machine was robotic and AI-driven. You cannot bring jobs back that no longer exist. Manufacturing employment in the U.S. actually fell by 100,000 jobs between January 2025 and January 2026. That trend will continue, because manufacturing is becoming overwhelmingly a robotics and AI-driven process across virtually every industry.

The second mistake is the belief that trade deficits reflect unfairness by China or others. This is something I address on the first day of my international monetary economics class. A trade deficit reflects the fact that you are spending more than you are producing, for whatever reason. In the American case, we have a giant national credit card called the federal government, which borrows roughly 7% of GDP right now. We spend a lot of money that we borrow from abroad. This has been going on for forty years, since Ronald Reagan initiated two generations of tax cutting. We’re now hemorrhaging public debt, and the trade deficit is a consequence of that, not of Chinese unfairness.

The trading system worked. We are now in the process of destroying it. What I hear from the people driving U.S. trade policy – the Lutnicks and Navarros of this world – would not pass for a first-year student in a first-year trade class. None of it holds any water.

C-Suite: How should boards be thinking about political risk in the context of U.S.-China relations becoming increasingly contentious and with some people invoking a Thucydides Trap (the idea that a rising power and an established one tend toward conflict)?

The clearest explanation of the U.S.-China situation that I know is a paper written in March 2015 by Robert Blackwill and Ashley Tellis for the Council on Foreign Relations, titled “A Grand Strategy for the United States Toward China.” I don’t agree with any of it, but it is admirably clear, and I think it is an explication of actual American statecraft rather than just a thought piece.

The paper’s logic is explicit: America’s grand strategy is primacy and China’s rise is a threat to primacy. Therefore, China’s continued rise is no longer in America’s interest. It then lists what to do, and the list is exactly what has been done; export restrictions on technology, trade blocs that exclude China, military build-up along China’s rimlands, and so forth. Nine or ten headline strategies, all of which have been pursued in the last decade. To my mind, none of them works. None actually contains China or advances American interests. But they do break apart the trading system and create significant problems for the United States going forward.

The two economies were deeply interpenetrated until recently. We have supply chains that depend on China, and we have markets in China, and they are being broken apart.

C-Suite: You described a solar module plant outside Shanghai. Green technology more broadly, where does that fit into the competitive picture for business leaders?

Climate change is not a hoax. It is something very serious, and it is getting worse at a dramatic and accelerating rate. Whoever dominates green technology in the future is going to, at a business level, make a lot of money, and at a national level, make a successful economy.

The United States has largely abandoned that sphere. China, I think, will be at the center of green technology for the next twenty to twenty-five years, given where things stand right now. This is very regrettable, and it is also a massive strategic mistake. On electric vehicles specifically, unless I am misreading something, we are absolutely surrendering that industry to China. Trump is not merely aggravating this. He may be putting a stake through the heart of the American automotive sector outside the protected domestic market. And we know that kind of protected market doesn’t last long.

For a C-suite executive, the first question should always be: what is the real future market or trend that is relevant to my business? And the answer, for almost anyone with exposure to energy, manufacturing, or transportation, is that green technology is one of the defining competitive battlegrounds of the next two decades. Simply denying this reality is deeply counterproductive.

C-Suite: You have been a long-standing critic of the IMF and the World Bank, and of the U.S. role in the international financial system. What do you see as the future of that system and what should CFOs be doing about it now?

Let me explain my critique from the ground up. At a core level, financial markets are crucial for a functioning economy, but they are intrinsically unstable. They rely overwhelmingly on expectations, which are subject to all sorts of influences and self-fulfilling prophecies. History is pockmarked by panics, manias, and crazes. This is well understood in financial history and has occasionally been rewarded with Nobel Prizes.

The two greatest accomplishments in modern monetary policy came in 1933 and 1934, in the depths of the Depression: the Federal Reserve finally understood that it was the lender of last resort to the banking system, and the introduction of federal deposit insurance. Those two changes put an end to the kind of commercial banking failures that had plagued the U.S. roughly every twenty years: 1873, 1893, 1907, 1933. What looked like an intrinsic feature of capitalism turned out to be a defect that could be corrected by clear and relatively straightforward policies.

When that lender-of-last-resort function fails, or is withheld, the consequences are catastrophic, as 2008 showed. The disaster of September 15th occurred because the Treasury Secretary forced Lehman Brothers into bankruptcy, almost to prove a point to Wall Street. He could have arranged a Barclays acquisition and avoided a multi-trillion dollar calamity. Instead, we had a collapse that put the world economy into a tailspin for several years. That was a pure policy mistake, and it is exactly the kind of mistake that a properly functioning international lender of last resort is supposed to prevent.

C-Suite: And where does the U.S. role in all this come in?

My critique of the current system is that the United States is misusing the dollar by weaponizing it. Twenty years ago, the U.S. began confiscating countries’ reserves, blocking countries from using SWIFT, and deploying other such measures as instruments of foreign policy. I believe these two things must be kept completely separate. Foreign policy is one thing; monetary policy is another. When foreign policy people meddle in the financial system, they destroy it and they are destroying the role of the dollar very quickly right now.

My critique of the IMF is that it is not the international lender of last resort that it should be. If financial markets are intrinsically unstable, you need a lender of last resort, just as the Fed is within the domestic system. Instead, the IMF functions more like a mortician. The country arrives dead after the crisis has already occurred, the debt is already in suspension, and the IMF dresses up the corpse. It takes three or four years for the zombie to come back to life. The IMF does not act proactively; it is not permitted to. It does not lend freely and quickly in a crisis mode to prevent the panic. It works on the debt workout once an insolvency event has happened.

The World Bank’s situation is different. My critique there is simply that it is too small. It lends $50 to $100 billion a year in a world economy of over $100 trillion. It has a prestigious address and a big name, but it is a tiny player. One reason it remains small is that if it were larger, China would get a bigger vote, and the United States prefers that it stay small with China’s influence limited.

C-Suite: Do you see any of that changing in the near future?

Probably not, because the geopolitical issue is not being wisely handled. What the United States should be doing is making the world safe for multipolarity. I started saying this about thirty years ago: we would have a multipolar world, and a far-sighted United States would help shape that world in ways that serve our long-term interests. Instead, we have pursued a strategy of preserving primacy, which puts us into conflict with the other major powers, Russia and China. That, I think, is the root of most of the problems we face right now.

I had some hope that Trump rather understood this was a multipolar world. Marco Rubio even said as much in one of his first statements as Secretary of State. But Trump said a couple of days ago that America’s goal is to remain the undisputed powerhouse of the world. That turned out to be the real policy. And that is very hard to sustain when you are 4% of the world’s population, a lot of clever people are everywhere else, and none of them are particularly interested in following American demands. The goal America has set for itself is unachievable, and the mere act of pursuing it is therefore extremely dangerous, because it is delusional.

C-Suite: On the dollar specifically, how significant is the de-dollarization risk on a five-to-ten-year horizon, and what does that mean for a CFO or treasurer?

Right now, the dollar is the currency of invoicing and settlement for roughly 60% of international transactions; the renminbi is around 5%. Ten years from now, I would expect the renminbi to be the currency of settlement for at least 20% of international transactions, and the dollar to be below 50%. We will definitely be in at least a two-currency system. You could imagine it being more. The Euro could become a more significant international currency if Europe and the U.S. have more separation, which seems increasingly likely given the current trajectory.

Twenty years from now, there will probably be an African currency covering most of the continent, a convertible rupee, a convertible renminbi, and the dollar will not dominate by any means. It may account for 30% of international trade, but it will not stand out as the world’s currency. We will not necessarily go the way of the pound sterling; it took two world wars and the end of the British Empire to break sterling, and God forbid anything remotely like that happens.

For a treasurer today, bank accounts denominated in renminbi, probably in rupees, and even in rubles, are all going to play a much bigger role in the future. That is something to be building into your financial planning now, not in ten years.

C-Suite: Given everything we’ve discussed about technology and economic development, what is your overall assessment of the potential impact of AI?

I am, in general, a technophile, and I am a believer that AI is very real, extraordinarily powerful, and not hyped. I would not have believed, ten years ago, that you could train a trillion-parameter model and have it available for a substantive intellectual discussion. As a daily and hourly user of AI for the work I do, I can say I am quite impressed. It reads journals faster than I do, it summarizes things extremely well, and for someone who works with data and information intensively, it is a huge advance.

In Chinese factories today, AI is already deeply embedded at every stage of production. In one plant I visited outside Shanghai, every machine had an AI role: testing whether wiring had been placed correctly, whether leads were properly set, whether electrolytes were balanced – all without a person anywhere in sight. This is manufacturing in China right now.

C-Suite: How does that translate to the labor market?

I also think AI will have profound and difficult effects on the labor market. Over the last two hundred years, technology has basically eliminated more jobs than it has created, and on the whole, that has been good. Our economy, at nearly $90,000 per capita, operates with adults putting in an average of three hours and fifteen minutes of work per day, according to U.S. government time-use surveys. What used to be twelve-to-fourteen-hour days of physical labor has been replaced, not by other jobs, but by leisure, study, retirement, and weekends. If you simply allow AI to do its job without any policy response, however, you will produce an underclass that is profoundly large. The approach, in one way or another, has to be to socialize a significant portion of consumption – through education, healthcare, public services – so that people’s living standards are not tied entirely to their individual position in the labor market. The United States does this much worse than Europe, and we have no coherent plan for what AI is going to do next, which will be a faster and larger effect than robotics was on the assembly line over the last thirty years.

C-Suite: What about for developing countries? Do you think AI will narrow or widen the gaps?

On the whole, I think it will narrow the gaps, though it does one thing that dramatically changes the development picture: it eliminates the labor-intensive manufacturing pathway that drove the great successes of Asia over the last fifty years. China, and the Asian Tigers before, all moved up the technology ladder from assembling electronics and cutting apparel to more advanced manufacturing. That pathway is gone. There are no jobs left in labor-intensive manufacturing; they are being automated and quickly.

But the advantages of AI for development on the other side are enormous. Education can be completely revolutionized. Healthcare can be completely revolutionized. Payment systems, credit systems, farm management, public service delivery: everything can be transformed, and I am seeing this happen in very poor settings. In India, you can go to the most remote rural village and every vegetable vendor has a QR code for online payment. I am involved in projects putting AI into the hands of high-school-educated health workers and nutrition support workers in villages, and it works extremely well.

The fundamental redesign of development strategy is now at hand. An African country is no longer going to sell unskilled labor. It is going to sell strategic minerals, downstream battery supply chains, commodity-based industrialization in metallurgy or specialty steels, agro-industry, tourism, the creative economy, online services. But it is going to have to be skilled. And that creates a very different set of priorities. I am working on exactly this with a number of governments and with the African Union right now. On the whole, this is a huge plus, because suddenly you have a very information-rich environment in a low-income setting, which was not the case before.

C-Suite: The U.S. has essentially dismantled its development aid programs. What is the realistic trajectory for Sub-Saharan Africa and other developing regions, and what role, if any, does aid play going forward?

I spent the first twenty or twenty-five years of my career promoting these programs, and I am proud of some of it. I believe I was the progenitor of the Global Fund to Fight AIDS, TB, and Malaria, and that has saved millions of lives and proved the point I made at the time; that there are low-cost interventions that could have a huge benefit and that were simply not getting done otherwise. It worked.

I always thought a little generosity could go a long way, and I liked the idea that the rich world should give 1% of its income to the poor. It turned out to be impossible. The United States came closest to that target in the Marshall Plan era, approaching 1% of GDP, before falling to around half a percent through the 1950s and declining from there. Aid collapsed further when the Soviet Union ended, because a lot of it had been motivated by competing for hearts and minds with the Soviet threat. We reached about 0.1% of GDP, and now we have eliminated development aid altogether.

Since I do not like pounding my head against a wall, I have largely stopped dealing with aid as such over the last ten years – not because it was wrong, but because it was a losing approach. I was not making headway. So now I spend most of my time trying to make the financial system work better for poor countries. Most of what is needed in development can be done through market finance, just not five-year euro bonds. Development is a twenty-five-year process, not a five-year process. If you borrow at five-year maturity for a twenty-five-year investment and you are a developing country, you are going to get into a debt crisis before your investment pays off. The case for maturity matching in development finance is fundamental and has never been seriously addressed.

As for aid today, it is not even a topic of polite conversation in Washington. It is simply not on the table. The task now is to build development finance mechanisms that do not depend on political generosity that never reliably materializes.

C-Suite: If you had to name one thing that most C-suite leaders are systematically getting wrong about the world right now, what would it be?

I want to be fair to business leaders here. In general, they are smart, practical people trying to make their businesses work. Where I see the real delusions are in Washington, not in the boardroom. Business leaders are trying to figure out what to do in a world they did not make and cannot fully control.

But what they should know is this: there is a big world out there, and it is a very sophisticated world. There are places with excellent technology and highly capable people everywhere you look, including places that American foreign policy has caricatured. The world doesn’t go away because we say so. It does not follow U.S. demands simply because we demand it.

The United States should continue to try to be a competitive, active, outward-looking, and optimistic part of the world. That is our best hope and our heritage. The great American strength has been our capacity to draw talent from all over the world, and to understand that science and technology are at the base of our long-term strength. Both of those are currently under self-attack; through the treatment of our universities, through the signals we are sending to foreign students and researchers who are now genuinely afraid to cross the U.S. border.

I can tell you that all of my foreign students are afraid right now. Every one of them, when they cross the border, does not know whether they will be coming back, whether they will be pulled aside for questioning at the airport, whether their phones will be confiscated, whether their social media will be read. A lot of students who would normally have come to the United States are now looking to study elsewhere. These are not mistakes of C-suite executives. But they are things that C-suite executives should know, and should factor into how they think about talent, innovation, and the long-term vitality of the American economy.

We do best when we are optimistic, outward-looking participants in a big, diverse world. We do worse when we view that world with fear and try to close up. That is not merely a political observation, it is an economic one.

Welcome to the April issue of The Briefing Table. This month’s edition covers four forces that defined the past few weeks and that will likely shape the strategic environment through the rest of 2026: the Iran war and its consequences beyond the oil price, the Supreme Court’s landmark tariff ruling, a U.S. economy absorbing both shocks simultaneously, and an AI governance gap that is closing in on its first major regulatory deadlines.

1) The Iran War: Beyond the Oil Price

The most significant development of the past week is the collision of simultaneous signals that point in opposite directions and remained unresolved after last night’s presidential address. Thousands of additional U.S. troops have arrived in the region, reportedly positioned for a potential seizure of Kharg Island or Iranian nuclear stockpiles. The administration has explicitly threatened to withdraw from NATO if European allies do not take a greater share of the burden in protecting Hormuz, which suggests a willingness to use the alliance itself as a bargaining chip. The economic consequences of the war were barely mentioned. Markets responded with oil prices up and futures down. Whether all this represents a strategy or the absence of one is genuinely unclear. What is clear is that resolution, when it comes, is likely to be abrupt, and, unlikely to restore the conditions that existed before the war began. (Readers looking for the full conflict scenario framework will find it in our recent special briefing; this section focuses on what has changed and on two exposure dimensions receiving insufficient attention.)

The first underappreciated exposure is competitive rather than operational. This shock is not landing symmetrically around the world. China holds crude reserves estimated at roughly 120 days of imports, its domestic pricing mechanisms insulate manufacturers from immediate pass-through inflation, and its twenty-year electrification bet is paying off precisely when its Western rivals are most exposed. Chinese manufacturers may actually gain a cost advantage in energy-intensive sectors as this shock persists, echoing what happened after the Russia-Ukraine war in 2022. McKinsey’s March 2026 Global Survey confirms the asymmetry in executive sentiment: respondents in Asia-Pacific, Greater China, and India lean more positive than negative about near-term conditions, while those in Europe and North America are the most likely to report worsening conditions and the least likely to expect improvement. The deeper irony: with China controlling more than 80% of global solar, wind turbine, and battery production capacity, the supply chain for the energy transition this conflict is accelerating runs predominantly through Beijing. For executives in energy-intensive industries, this is a present-tense competitiveness question, not a future-tense strategic concern.

The second is the semiconductor and AI infrastructure exposure. Qatar’s Ras Laffan shutdown has closed off a major source of helium, essential for chip fabrication, fiber optics, and MRI machines. The Gulf region supplies roughly half of global sulfur exports, the feedstock for the high-purity sulfuric acid required to manufacture clean silicon wafers. Iranian drone strikes have already damaged two AWS data centers in the UAE and a facility in Bahrain. Gulf sovereign wealth funds, which have been among the largest investors in AI infrastructure globally, are redirecting capital toward domestic reconstruction. The conflict is simultaneously disrupting the physical inputs, the cloud infrastructure, and the investment capital underpinning most companies’ AI buildout assumptions. Few strategy teams have modeled all three.

Bottom Line: Most energy cost exposure has now been identified. The less-modeled risks are China gaining ground in energy-intensive industries while Western rivals absorb the shock and supply chain disruption running through semiconductor inputs and AI infrastructure. Both warrant immediate audit. On scenario planning: do not treat a sudden ceasefire as a return to normal. Iran has demonstrated it can close the Strait; that capability does not disappear with a peace agreement. Real shipping and oil market disruptions would persist for weeks after any ceasefire, Iranian leverage over the waterway is now an established fact rather than a theoretical risk, and the regional power balance has shifted in ways that will outlast the conflict. The status quo ante is gone. Plan for a new baseline, not a restoration.

2) The IEEPA Ruling: The Tariff Wars Enter a New Phase

On February 20th, the Supreme Court ruled 6 to 3 that IEEPA does not authorize the President to impose tariffs. Chief Justice Roberts held that tariff authority is “very clearly a branch of the taxing power” reserved for Congress. The decision invalidated the reciprocal tariffs and most of the tariff architecture built since Liberation Day. As we previewed in December, the ruling has created nearly as much new uncertainty as it resolved.

The administration moved within hours. Section 122 of the Trade Act of 1974 now imposes a 10% global tariff pushed toward the 15% ceiling, expiring July 24th unless Congress extends acts to extend the measure. New Section 301 investigations were launched targeting 16 economies, including China, the EU, Japan, Mexico, and Vietnam, for structural excess capacity. An additional 60 investigations were launched for failures to enforce forced-labor bans. Section 232 tariffs on steel, aluminum, and autos remain fully intact. As a result, the effective tariff rate, at approximately 10.5%, remains at its highest level since 1943. The paradox of the ruling is that it has replaced a fast, legally fragile instrument with slower ones that are harder to challenge and longer-lasting in their effects. IEEPA tariffs could be imposed overnight and litigated immediately. Section 301 findings take 12 to 18 months to reach conclusions which are far more durable once they do.

Two pressure points deserve attention. Penn Wharton projects up to $175 billion in potential IEEPA refunds, but the Court left the mechanism to the Court of International Trade, which is managing nearly 2,000 pending cases; CBP cannot begin processing until mid-April at the earliest. Large importers are sitting on a potential windfall wrapped in procedural uncertainty, arriving in the middle of an energy crisis. Separately, the July 1st USMCA review deadline, overshadowed by the IEEPA drama, may prove more consequential for North American supply chains than the ruling itself. Canada has recently signaled a willingness to explore a North American “fortress” approach to energy and raw materials, but that posture has not softened Washington’s negotiation position. The realistic outcomes range from managed renewal with modified terms, to collapse into separate bilateral deals, to large unilateral actions against Canada, which remains more exposed than Mexico given its energy and resource dependencies. One further signal worth registering: McKinsey’s March 2026 Global Survey finds that geopolitical instability has now overtaken trade policy as the most-cited threat to company growth for the first time since March 2025. The legal and trade architecture remains unsettled, but executive attention has already moved on.

Bottom Line: Three simultaneous actions: file or preserve IEEPA refund claims before procedural windows close; map your supply base against the 16 Section 301 investigation targets; and begin USMCA scenario planning before July 1st forces a reactive posture. The tariff environment has not stabilized. It has restructured around a less predictable legal architecture, and this restructuring is arriving into an economy simultaneously absorbing an oil shock and a monetary policy dilemma.

3) The U.S. Economy: Stagflation Lite Is No Longer a Forecast

In November, we identified two Gray Rhinos preparing to charge: tariff pass-through to consumers and the risk of an AI investment bubble. The Iran war now adds a third. What unites all three is a common structure: their costs were largely deferred in 2025 through inventory pre-stocking, energy price normalization, and AI investment not yet faced with a demand test. They are now arriving simultaneously, however, and without the buffers that cushioned last year’s shocks.

The headline numbers still look passable: GDP growth projected around 2%, unemployment at 4.4%, and markets volatile but intact. The structure beneath those numbers is less reassuring. The wealthiest 10% of households now generate nearly half of all consumer spending, with Deloitte estimating 20-25% of that driven by the wealth effect from rising asset prices. That support disappears quickly in a sharp equity correction. Bank of America Institute data shows younger Gen Z and Millennials consumers, who had finally begun outspending older cohorts on the back of easing rents and strong wage growth, have disproportionately high gasoline spending relative to their discretionary budgets. A sustained fuel shock will reverse recent momentum in retail, restaurants, and consumer electronics precisely in the demographics driving it.

McKinsey’s March 2026 Global Survey on Economic Conditions captures how quickly the picture changed. Executives surveyed before February 28th were as optimistic as they had been in December, the most upbeat quarter of 2025. From February 28th onward, the share indicating global conditions had worsened nearly doubled, and forward expectations flipped from net positive to net negative within 72 hours. The share citing supply chain disruptions as a top risk doubled in the same window. This is not a gradual deterioration. It is the kind of sentiment shift that arrives faster than quarterly planning cycles can accommodate, which is why the contingency planning argument in this issue is not about managing disruption but about matching its speed. There is, however, a complicating signal: at the company level, expectations remain primarily optimistic. Just over half of private sector respondents still expect demand for their products and services to increase in the next six months, and about six in ten expect profits to grow. That gap between a sharply deteriorating macro-outlook and continued company-level optimism, is a pattern worth watching. It tends to resolve in one direction only, and rarely does the macro picture turn out to be wrong.

The monetary policy picture has deteriorated materially. The Fed held rates steady on March 18th and penciled in at most one cut for 2026, down from the two cuts markets had been pricing before the war. Several analysts suggest no cuts will be delivered this year. The ECB, facing a more severe energy exposure, has already postponed planned reductions and warned of stagflation risk in Germany and Italy. The Fed cannot address both sides of this dilemma at once: holding rates to contain inflation risks tipping a softening labor market into something worse, while cutting to support growth risks entrenching energy-driven price pressures. Any capital allocation plan built on an assumption of two or more rate cuts in 2026 needs to be revisited. Chairman Powell’s term expires in May. As we analyzed last July, a successor perceived as politically accommodative could push long-term bond yields higher even as short-term rates fall, precisely the wrong environment for capital-intensive businesses.

Bottom Line: Goldman Sachs has raised its 12-month U.S. recession probability to 30%; Moody’s Analytics sits at 42%. Review capital expenditure plans and debt covenants. Build variable cost flexibility into the operating model. But the most important action may be the simplest: put your macro assumptions and your company forecast in the same room and ask whether they are consistent. McKinsey’s survey suggests most executives will find that they are not. That gap does not stay open. It closes and it is rarely the macro picture that moves.

4) Agentic AI: The Governance Gap Is Now a Board-Level Liability

In December, we cautioned against the tendency to go “all-in” on emerging technologies before the enabling infrastructure is in place, a pattern repeated across EVs, autonomous vehicles, fuel cells, and telematics. That warning applies with even greater urgency to agentic AI, with one important difference: the automotive industry’s overcommitments primarily affected capital budgets. AI governance failures will affect customers, regulators, and reputations simultaneously, and the regulatory deadlines are no longer abstract.

The shift happening in 2026 is not simply more AI. Generative AI assistants merely make recommendations. Agentic systems act on them, initiating transactions, sending communications, modifying data, and orchestrating workflows across enterprise systems without human review at each step. Only 21% of enterprise leaders report having a mature governance model for autonomous agents. Meanwhile, 65% of AI tools inside enterprises operate without IT oversight, adding an average of $670,000 to the cost of a data breach. The AI running inside your organization today is almost certainly not fully visible to the people responsible for managing its risks.

The governance gap persists not because executives are unaware of it but because the standard response - approving AI tools through procurement and IT review - is structurally mismatched to the actual risk. Approving a tool is not the same as governing its execution. With generative AI assistants, that distinction was manageable: the human read the output and decided what to do with it. With agentic systems, the human who authorized the agent is typically nowhere near the workflow when the agent acts. The accountability gap is not a process failure. It is an architectural one.

Three regulatory deadlines are now inside any responsible planning horizon. The EU AI Act reaches full general application August 2nd. Colorado’s AI Act takes effect June 30, with specific obligations around algorithmic discrimination. California’s generative AI transparency requirements are already active. The FTC’s “Operation AI Comply” in 2025 established that regulators expect documented controls and technical safeguards, not aspirational ethics statements. The governance question boards should be asking is not whether to govern AI but at what level. Tool-level governance, meaning the approval of AI systems, misses the risk. The risk lies in what agents do, not what they are, which means governance must be built into the workflows agents operate within, with clear accountability for every action they can take.

Bottom Line: The governance gap in enterprise AI today is where corporate cybersecurity governance was in 2003: widely acknowledged, but rarely addressed with appropriate seriousness, until high-profile failures made inaction untenable. The firms that built the infrastructure before the breaches gained lasting competitive and reputational advantages.

Key Take-Aways

• Model the full exposure, not just oil prices. Map every input that transits the Strait of Hormuz or originates in the Gulf, including indirect dependencies like helium, sulfur, and industrial gases. Most companies have done this for crude oil. Few have done it for the second- and third-order inputs that are quietly closing semiconductor and AI supply chains.

• The tariff regime has restructured, not stabilized. File IEEPA refund claims promptly, map your supplier base against the 16 Section 301 targets, and begin USMCA planning before the July 1st deadline. The effective tariff rate remains near an 83-year high, and the administration has demonstrated it will find new instruments whenever old ones are removed.

• Your macro assumptions and your company forecast are probably inconsistent. McKinsey’s March survey finds executives remain primarily optimistic about their own firms’ demand and profits even as their macro outlook has deteriorated sharply. That gap tends to close in one direction. Audit your company-level plan against the macro picture explicitly, not as a separate exercise but in the same room, with the same set of assumptions, at the same time.

• Govern AI at the workflow level, not the tool level. The risk is in what agents do, not what they are. Commission a governance review that covers agentic systems specifically, maps accountability to individual agent actions, and meets the EU AI Act, Colorado, and FTC standards. The August deadline is the forcing function.

• Build contingency plans in both directions. A rapid negotiated off-ramp is as plausible as further escalation, and it could arrive with virtually no warning, as last Monday’s 14% oil price swing demonstrated in both directions inside a single trading session. Scenario planning should be explicit about both endpoints and the speed at which transitions can occur. Companies that prepare only for continued disruption will be as wrong-footed by a sudden normalization as those that failed to prepare for the disruption itself.

The markets initially seemed to shrug this off as a short, decisive operation with a defined endpoint. That framing was wrong, and the longer executives plan around it, the more costly the mistake. Three questions will determine what comes next: How is this conflict likely to evolve, given the diverging interests and decision-making constraints of the key players? What are the most plausible scenarios over the coming weeks and months? And, most importantly, what should U.S. companies actually do?

The answers run contrary to the conventional wisdom. The disruption is worse than markets currently reflect. The path to resolution is narrower than it appears. And the business implications extend well beyond the energy sector into areas, including semiconductors, AI infrastructure, and monetary policy, that few strategy and risk management teams have begun to model.

Three Players, Three Constraint Sets

The trajectory of this conflict is best understood not as a coherent campaign with a defined endpoint, but as the product of three actors whose interests only partially overlap and whose room to maneuver is more constrained than their public postures suggest.

1. The United States: Military Success, Strategic Uncertainty

Militarily, the U.S. has clearly had the better of the early exchanges. Iran’s senior leadership has been decapitated, its missile launchers have been destroyed, and the U.S. has achieved air supremacy with minimal American losses. What has proven far more elusive is a strategic objective that is both achievable and politically sustainable.

The closure of the Strait of Hormuz was an entirely predictable Iranian response to a major military campaign. Iranian officials had telegraphed it for years, and energy analysts had long priced it as an obvious contingency. Whether or not it was adequately planned for, the U.S. now faces a situation in which the most painful consequence of the conflict, a global energy crisis landing hardest on American consumers, was widely foreseen.

Recent U.S. moves point in two directions simultaneously. The un-sanctioning of Iranian oil exports, functionally the reversal of the first-term maximum pressure posture, signals that off-ramps are being actively explored. The simultaneous deployment of U.S. paratroopers and a Marine expeditionary unit to the region signals that escalatory options are being kept open. The tension between those two signals is the defining feature of the current moment.

2. Israel: Different War, Different Objectives

Israel entered this conflict with its own logic and its own definition of success: the permanent degradation of Iranian nuclear capability. Its strikes on Iranian gas facilities, conducted according to Israeli officials with U.S. awareness, have generated visible friction with Washington even while advancing Israeli objectives. The divergence matters for business scenario planning: Israel’s interests in a swift resolution are considerably more limited than America’s. A prolonged conflict that keeps Iran militarily degraded and its nuclear program set back by years is not, from Tel Aviv’s perspective, an unfavorable outcome, regardless of the cost to the global economy. Any negotiated endgame will have to resolve this structural misalignment between the two allies, and there is no obvious mechanism for doing so, at least not quickly.

3. Iran: Severe Losses, Escalatory Ceiling Not Yet Reached

Iran has sustained severe military losses and has seen much of its conventional deterrent capacity destroyed. And yet it has not come close to deploying its full asymmetric arsenal.

The Strait of Hormuz closure, severe as it has been, is not Iran’s ceiling. Iran retains an extensive sea mine capability that has barely been used, a fast-attack boat fleet capable of threatening U.S. naval vessels, and the ability to expand attacks to Gulf desalinization plants and other civilian critical infrastructure that sustains human life across the region. The officials now making these decisions are, by definition, those who survived a targeted decapitation campaign. Their incentives are not necessarily aligned with de-escalation.

The market, to the extent it is pricing the current disruption as the likely endpoint of Iranian escalation, is almost certainly wrong. Companies stress-testing their exposure should model a scenario in which the current level of disruption is not the floor but the baseline, with meaningful probability of further deterioration.

Three Scenarios and Why None Is Reassuring

Based on the constraints above, three broad scenarios are plausible.

Scenario 1: Declare Victory and Wrap It Up

Back-channel negotiations produce a ceasefire framework. The U.S. declares victory based on military results achieved, while Iran halts Strait disruptions in exchange for sanctions relief and security guarantees. The Strait reopens partially within weeks, fully within months.

The preconditions are currently absent. There is no agreed U.S. definition of victory significant Israeli resistance to any outcome that leaves Iranian nuclear infrastructure intact, and there may not even be a functioning Iranian interlocutor with authority to make binding commitments. The scenario is nonetheless worth modeling and preparing for, because it could materialize abruptly if domestic political pressure intensifies. Companies that have not prepared for a rapid normalization will be as wrong-footed as those that did not prepare for the disruption itself.

Scenario 2: Escalate to De-escalate, the Dangerous Middle

This is the current trajectory. Periodic U.S. escalation, strikes on new Iranian targets, is intended to signal resolve and create pressure for negotiation. In theory, it produces an off-ramp. In practice, it has so far produced the opposite: Iranian counter-escalation and a visible split between the U.S. and Israel.

The deeper problem is credibility. The rational Iranian inference from recent U.S. moves, specifically the disavowal of certain strikes shortly after they occurred and the reversal on oil sanctions, is that Washington is more sensitive to energy prices than it is committed to continued escalation. An escalate-to-de-escalate strategy is particularly dangerous when the adversary has correctly assessed that the escalatory threat is not fully credible.

Scenario 3: Go Big, Force the Strait Open

The third option involves committing to a decisive military campaign: forcing the Strait open by naval and ground operation, potentially including the seizure of Iran’s primary oil export terminal at Kharg Island. Marines and paratroopers being deployed to the region are the relevant capability; boots on the ground have not been taken off the table.

The appeal of this option is real and should not be dismissed: it would, in theory, resolve the energy crisis by removing its source. Its problems are equally real. The Strait was open before the war started; making its reopening the war’s central objective means the U.S. is now fighting to undo the most predictable consequence of a war it chose to start. U.S. forces have already struck more than 90 targets on Kharg Island while deliberately preserving the oil infrastructure there, but holding the island against continuous Iranian attack from the mainland is a different proposition entirely: an indefinite occupation of a contested asset with no clear exit, subject to the full weight of Iranian countermeasures not yet deployed.

Most Likely Path: Extended Disruption, Abrupt Resolution

The near-term base case is Scenario 2, continued escalation and stalemate, with a non-trivial probability of migration toward either Scenario 1 or Scenario 3 depending on two variables: the trajectory of U.S. gasoline prices and the administration’s read of Iranian credibility. Watch both as leading indicators.

The synchronized global demand data published this week provides the first hard evidence that the economic damage is no longer confined to the energy sector. Every composite PMI index released on Tuesday, covering the U.S., Eurozone, UK, Japan, India, and Australia, came in below expectations. The American reading showed business activity expanding at its slowest pace in nearly a year. A gauge of prices paid for inputs jumped to the highest since May. What began as a supply shock is now registering as a demand shock as well. Goldman Sachs has raised its 12-month U.S. recession probability to 30% as a result of the oil price surge, and projects unemployment rising to 4.6% by year-end.

Four things can be stated with reasonable confidence:

• The Strait of Hormuz will not reopen meaningfully in the next 30 to 60 days under any scenario short of a comprehensive ceasefire. Strategic petroleum reserve releases, Jones Act waivers, and rerouting around the Cape of Good Hope provide marginal relief; they do not address the core problem: there is no alternative route capable of moving the volume of oil and gas currently stranded in the Middle East.

• Qatar’s Ras Laffan LNG facility is offline for the duration of active hostilities. The global LNG market is structurally altered for the medium term. European and Asian consumers are not returning to pre-war supply conditions on any near-term timeline.

• The current level of Iranian escalation is not the ceiling. The risk of further deterioration, expanded mine warfare in the Strait, attacks on Gulf desalinization plants, broader civilian infrastructure targeting, is real and underpriced in most business planning models.

• Resolution, when it comes, is likely to be abrupt. A single presidential social media post on Monday sent oil down 14% in minutes, then Iran’s denial of any talks sent it back up. Companies need contingency plans in both directions, and they need to assume they will have very little warning when the direction changes.

Implications for U.S. Companies

The Exposure You’ve Modeled, and the Exposure You Haven’t

The direct energy cost impact is the most visible and has received the most attention. Gasoline approaching $4 a gallon nationally, up more than a dollar since the conflict began, elevated industrial energy costs, compressed margins in energy-intensive operations: executive teams have been running these numbers. Two things are worth adding to that analysis.

First, Bank of America Institute data published this week shows that Gen Z and Millennial consumers, who had finally begun outspending older cohorts buoyed by easing rents and strong wage growth, have disproportionately high gasoline spending relative to their discretionary budgets. A sustained fuel shock is likely to reverse recent momentum in retail, restaurants, and consumer electronics spending precisely in the demographics that had been driving it. Consumer-facing companies should be revising their near-term demand models now.

Second, the supply chain disruptions extend into inputs few companies had thought to track. Qatar is one of the world’s largest suppliers of helium, a byproduct of natural gas extraction essential for semiconductor manufacturing, fiber optics, and MRI machines. The Gulf region supplies approximately half of global sulfur exports, which produce the high-purity sulfuric acid required to manufacture clean silicon wafers. The same conflict raising gasoline prices is quietly creating a serious bottleneck in semiconductor manufacturing that will ripple through the technology supply chain for months. Companies with exposure to chips, which is to say virtually every company, should be auditing this now.

Third, executives with European operations or customers are facing a harder version of this shock than the U.S. is. European natural gas prices have nearly doubled since the conflict began. Chemical and steel manufacturers across the EU and UK have already imposed input cost surcharges of up to 30%, and analysts are using the word “deindustrialization” not as a long-term structural concern but as a near-term operational risk in Germany and Italy. Unlike the Russia-Ukraine shock of 2022, the tools Europe used then, rerouting LNG, substitution, demand destruction, are largely unavailable here: Qatari LNG is offline, and Cape of Good Hope rerouting adds cost and time but cannot replace stranded Gulf supply. Companies with European exposure should not be benchmarking this disruption against 2022. The structural situation is materially worse.

The Rate Environment Has Changed, Whether or Not Your Plans Have

The war has created the macroeconomic environment that central bankers specifically dread: inflation rising while growth weakens simultaneously. The Fed held rates steady on March 18 and penciled in at most one cut for 2026, down from the two cuts markets had been pricing before the war began. Several analysts now think no cuts will be delivered this year. The ECB, facing a more severe energy exposure, has already postponed its planned rate reductions and warned of stagflation risk in Germany and Italy.

The Fed cannot address both sides of this dilemma at once. Holding or raising rates to contain inflation risks tipping an already softening labor market into something worse; cutting to support growth risks entrenching energy-driven price pressures. This is precisely the type of shock that monetary policy is not designed to address. Any capital allocation plan built on an assumption of two or more rate cuts in 2026 needs to be revisited. Companies carrying floating-rate debt, considering leveraged acquisitions, or planning major capital expenditures on the basis of continued accommodative conditions are operating with a planning assumption that has become materially less reliable in the past four weeks.

There is an additional layer that belongs in the CFO’s conversation. A single presidential social media post on Monday moved the oil price 14% in minutes. Unusual trading activity in oil futures roughly 15 minutes before that post was published has drawn scrutiny from market analysts and regulators. Whether or not that investigation produces findings, the practical consequence is significant: when price-moving information reaches some market participants before it is public, conventional hedging programs based on publicly available signals lose much of their effectiveness. Treasury teams should be reviewing whether their existing instruments are still fit for purpose in an environment where policy announcements, not supply-and-demand fundamentals, are the dominant price driver.

The AI Buildout Has a Gulf Problem

Gulf sovereign wealth funds have been among the primary drivers of AI infrastructure investment globally, funding data centers, semiconductor fabs, and the broader technology buildout that underpins most companies’ medium-term productivity assumptions. These funds now face a fundamentally different set of priorities: rebuilding domestic energy infrastructure, managing food security (the Gulf imports billions in cereals and produce by sea, and hundreds of dry bulk carriers are currently avoiding the region), and managing the political consequences of a conflict they did not choose. The redirection of Gulf capital away from AI and technology investment is not a theoretical scenario; it is already underway.

There is a more immediate dimension as well. Iranian drone strikes have already damaged two AWS data centers in the UAE and a facility in Bahrain. The fact that the world’s largest cloud computing provider has had physical infrastructure struck in this conflict changes the risk profile for any company with significant workloads running through Gulf-based data centers and raises questions about digital infrastructure vulnerability that most business continuity plans have not confronted. Companies whose medium-term plans assume continued aggressive AI buildout should be pressure-testing that assumption against a scenario in which both the capital enabling it and the infrastructure supporting it have been simultaneously disrupted.

China Is Not Absorbing the Same Shock You Are

While U.S. and European companies absorb higher energy costs, Chinese manufacturers face a structurally different picture. China has made a sustained 20-year bet on electrification, holds crude reserves estimated at roughly 120 days of imports, and its domestic pricing mechanisms provide significant insulation from immediate pass-through inflation. Chinese manufacturers may actually gain a cost advantage relative to rivals in energy-intensive industries as this shock persists, echoing the dynamic seen after the Russia-Ukraine war in 2022, when energy shocks disproportionately weakened Western production compared to Chinese exporters.

The clean energy dimension compounds this asymmetry with China controlling more than 80% of global solar manufacturing, wind turbine, and battery production capacity. As the conflict accelerates the energy transition argument in boardrooms across the U.S. and Europe, the supply chain for that transition runs predominantly through China. Companies accelerating their own renewable energy investments in response to this shock will find that the path to energy independence runs, for now, through Chinese suppliers. For companies competing globally in energy-intensive sectors, this is a present-tense competitiveness question, not a future-tense strategic concern.

What Companies Should Actually Do

Three concrete actions are warranted now, regardless of how the conflict ultimately resolves:

• Audit the full supply chain exposure, not just energy costs. Map every input that transits the Strait of Hormuz or originates in the Gulf region, including indirect dependencies like helium, sulfur, and other industrial inputs. Most companies have done this for oil; few have done it for second- and third-order inputs. The companies that did this work after COVID will have an advantage; those that did not should start immediately.

• Stress-test capital allocation and financing assumptions. Any plan premised on two or more Fed rate cuts in 2026, continued AI infrastructure buildout, or stable Middle Eastern supply chains was built on assumptions that no longer hold. This means specific attention to floating-rate exposure, leveraged positions, and whether existing hedging instruments are still fit for purpose when policy signals, not market fundamentals, are setting the price.

• Build contingency plans in both directions. A rapid negotiated off-ramp is as plausible as further escalation, and it could arrive with virtually no warning. Companies that prepare only for continued disruption will be as wrong-footed by a sudden normalization as those that failed to prepare for the disruption itself. The scenario planning exercise should be explicit about both endpoints and the speed at which transitions can occur.

Conclusion

Four weeks into this war, the business implications are running well ahead of most companies’ strategic responses. The executives who will navigate this best are not those who wait for the conflict to resolve before adjusting their strategies. They are those who recognize that the disruption already underway, combined with the escalatory risk that has not yet materialized and a monetary policy environment that has shifted materially against them, represents a structural change in business conditions rather than a temporary shock.

The most important number to watch is not the oil price. It is the gap between what the market is pricing as Iran’s escalatory ceiling and what Iran has actually demonstrated it is willing and able to do. Right now, that gap is large. Executives who close it in their planning before markets do will be substantially better positioned than those who do not.

C-Suite: You’ve labeled today’s workforce “Generation Numb.” Why?

Cardoz: We think it explains what’s going on in the world now. The modern workforce is undergoing a fundamental shift. Global upheaval, economic instability, the after-effects of the pandemic, and digital saturation are shaping a workforce that is numb. They are increasingly cynical, stuck, longing for connection, and hunting for a renewed sense of identity at work. People are still showing up. They’re still doing their jobs. But they’re operating in survival mode.

C-Suite: What evidence do you see of this?

Cardoz: The signals are everywhere. Nearly 60% of workers say they’re overwhelmed by the pace of change. There’s been a 36% drop in willingness to support change. Twenty percent experience loneliness daily. Half are worried they can’t pay their bills. Those numbers matter because they sit underneath every transformation program leaders are trying to execute, shaping how change lands, how quickly it’s adopted, and whether discretionary effort ever materializes.

C-Suite: What’s driving this numbness?

Cardoz: People are navigating a polycrisis. Recession, war, climate anxiety, polarization, rising inequality, all at once. At the same time, digital adoption now outpaces human adaptation. Each new technology reaches mass use faster than the last. We’re still recovering from the pandemic and adapting to hybrid work. What does it mean to be connected to colleagues but never meet them? What does it mean to enter the workforce from the same bedroom where you wrote your college papers? And then you add AI, which can be the biggest boost to your career but could also take your job tomorrow. The pressures are cumulative and relentless.

Haywood: Our diagnosis was triggered by what we were seeing with clients. We deliver large programs for major organizations, and these issues were affecting success. We drew on more than 40 third-party reports and conducted proprietary research across U.S. employees in large organizations, testing this concept of “numbness” across age cohorts. Feeling overwhelmed isn’t new. But what we’re hearing is, “I’ve been doing this for ten years and now I’m numb.”

C-Suite: How is numbness different from burnout and why does that distinction matter to business leaders?

Haywood: Most of the rhetoric is about burnout or mental health crisis. That describes the extreme end of negative emotion. What we found instead was that 60% of people describe themselves as “okay” or “indifferent.” That’s fundamentally different from being burnt out. It’s a person who waits in the wings to be energized, if leadership can unlock them. The danger is that numbness looks like “okay.” It doesn’t trigger alarms. But it steadily erodes the extra effort transformation depends on. It’s not quiet quitting. It’s not rejection. It’s numbness against more demands coming daily. It shows up in employees ignoring initiatives or not really engaging with them.

Cardoz: For a C-suite executive, that creates a dangerous illusion. You can be in the middle of a transformation - launching initiatives, deploying content, tracking milestones - while your execution capacity is quietly shrinking beneath you. There’s dissonance between the executive vision and where your people actually are. It’s related to Maslow’s hierarchy of needs. It’s hard to get excited about customers when you’re worried about the price of bread.

C-Suite: So this becomes a strategy execution issue, not just an engagement issue?

Haywood: Absolutely. Companies are making major investments in transformation, particularly AI, but they’re using standard procedures to drive that change: high-level corporate communications followed by content deployment. In this environment, those standard methods will fail, not because the strategy is flawed, but because the human energy required to absorb it has been depleted. You need to change the energy and focus of the workforce before you can change behavior. Otherwise, you’re investing in transformation on top of shrinking execution capacity.

C-Suite: Is this primarily a white-collar phenomenon?

Cardoz: No. Numbness affects every organization and every team in distinct ways. Every large organization has change fatigue. The erosion of wonder in work, being tied to value and purpose, is common across white- and blue-collar employees. Even CEOs are feeling isolated. In a hybrid world, many don’t truly know their employees. Some are endorsing AI investments without a coherent AI strategy because of external pressure. This isn’t just an employee engagement issue. It’s a systemic constraint on performance and one that affects frontline workers, knowledge professionals, and executives alike.

C-Suite: What is your remedy?

Haywood: It’s sequential. Historically, organizations leap to what we call the “It” stage: this is what needs to change, these are the new capabilities, this is what you must do differently. But in Generation Numb, change is often heard as code for future layoffs. Organizations need to start with the “Me” stage, connecting change to personal priorities. What does this mean for me? Why should I engage? The key isn’t removing fear. CEOs can’t guarantee there won’t be layoffs. But they can create enough relevancy and meaning to build trust. One practical example is being explicit: if you develop these skills, you become more valuable — whether here or elsewhere. Then comes the “Us” stage, restoring connection and community. People are longing for that. Only after “Me” and “Us” can you effectively move to “It” and the initiative itself. Skip the sequence, and change may appear to move forward, but adoption will be shallow and fragile.

C-Suite: What does leadership need to do differently?

Cardoz: Authenticity is critical. There’s honesty in saying: we are in extraordinary change times. We’re changing today, and we’ll need to change tomorrow. Let’s talk about why. Have an adult-to-adult discussion. The other key is agency. Give people as much agency as feasible to define what that change looks like for them. When people have agency, they re-engage.

C-Suite: You’ve emphasized analog experiences in a digital age. Why?

Cardoz: We have a strong legacy in digital solutions. But clients have pushed us to experiment more with analog delivery. We ran a three-day workshop with a Fortune 100 brand entirely without technology - no laptops, no slides. One participant said, “I’ve forgotten that real work can happen outside a laptop.” Another said, “I’ve forgotten what it means to connect with peers in an authentic way.” We even took them to an aquarium and asked them to walk through in assigned customer personas. It unlocked empathy and imagination. These methods were tried and tested. They work precisely because the workforce is overly immersed in technology. In a digitally saturated environment, analog experiences can restore imagination, empathy, and human connection - the very ingredients transformation depends on. Of course, the C-suite has to consider budgets and scale. But we are finding that analog can be a powerful antidote to numbness.

C-Suite: Do you have any final advice for executives pursuing transformation?

Cardoz: Every organization says, “We want our people here,” and it’s usually a place of high performance. Leaders are trying to extract the final 10–15% of performance that becomes competitive advantage. They want to jump to “It” because of urgency and pressure. But their people just aren’t there yet. If you proceed along the Me–Us–It path, and recognize the stage your people are in, you can begin rebuilding them to a place where work becomes a badge of honor again, because they’re connected to their team and to the value of the work. That’s how you unlock the performance you’ve been chasing.

This was not a narrow statutory ruling. It redraws the boundary between presidential and congressional authority in trade policy and forces a recalibration of the Trump 47 administration’s geopolitical and economic strategy.

What the Court Decided and Why It Matters

IEEPA was enacted in 1977 to allow presidents to respond swiftly to national emergencies stemming from foreign threats. Historically, it has been used to freeze assets, block transactions, and impose sanctions. The statute nowhere explicitly mentions tariffs. The administration argued that its broad authority to “regulate” economic transactions included the power to levy import taxes. The Court disagreed, reasoning that tariffs are economically and politically consequential instruments that require clear congressional authorization.

The immediate consequence is clear: the administration’s fastest and most flexible tariff mechanism is no longer available, absent new legislation. The deeper consequence is structural. Emergency authority can no longer serve as a shortcut around the trade policy process. Tariffs remain very much in the policy arsenal, but their deployment will likely be slower, more procedurally constrained, and more legally contestable.

Why This Reshapes U.S. Trade Power

For the administration, rapid tariff announcements signaled resolve and created leverage. Without IEEPA, that shock capability is constrained. Trade actions will likely become more targeted, more bureaucratic, and more legally durable, but also slower.

For companies, the key takeaway is not that tariff risk has disappeared. It is that tariff risk has become more complex. The instrument has changed; the geopolitical tensions driving it have not.

There is also a fiscal dimension. If courts ultimately require reimbursement of tariffs collected under IEEPA, the federal government’s exposure could be substantial. The process would almost certainly be contested, slow, and administratively burdensome. Resolution could take years. In the interim, uncertainty will sit on corporate balance sheets and in federal budget projections alike.

Beyond the fiscal implications, the ruling has important consequences for the structure of North American trade.

USMCA Implications

For companies, the ruling’s potential implications for USMCA are particularly significant. North American supply chains are deeply integrated and rely heavily on the agreement’s rules of origin, tariff preferences, and dispute-settlement framework.

Conceptually, the decision does not materially increase the likelihood of USMCA’s dissolution. North American supply chains, especially in automotive, are trilateral by design, making a return to purely bilateral structures operationally disruptive. Moreover, replacing USMCA would require statutory authority and likely congressional involvement. A wholesale restructuring would impose substantial costs on U.S. firms.

Rather than abandoning USMCA altogether in favor of separate bilateral agreements, the more plausible path is incremental layering: sector-specific measures, side letters, tighter enforcement, and managed-trade adjustments. The agreement would formally remain intact, but operational complexity would rise. For companies operating across North America, that distinction matters.

Four Paths Forward

From a C-Suite perspective, four plausible scenarios emerge:

1) Compliance and Recalibration
The administration unwinds IEEPA-based tariffs and pivots to more defensible statutory authorities. Trade friction continues, but through slower and more procedural channels. Institutional credibility improves, but escalation capacity diminishes.

2) Reimposition Under Alternative Authority
Other statutes are invoked to reimpose tariffs in modified form. Foreign governments adopt a wait-and-see posture, calculating that U.S. measures could again be constrained by the courts. Legal challenges resume. Volatility persists.

3) Targeted, Industry-Specific Escalation
Rather than broad-based tariffs, the focus shifts to specific sectors, including automotive, steel, and semiconductors, under national security or trade enforcement authorities. Economic pressure becomes concentrated. Bargaining leverage increases, but so do retaliation risks and domestic political friction.

4) Legal Escalation and Institutional Conflict
Narrow compliance or aggressive reinterpretation triggers injunctions and prolonged legal confrontation. This is the least stable path. Markets respond quickly to signs of constitutional strain, and capital investment pauses follow.

The most coherent, and therefore most likely, outcome is a hybrid of the first and third paths: formal compliance with the ruling followed by targeted, sector-specific actions under clearer statutory authority. Tariffs will likely persist, but they will become narrower, more structured, and more legally grounded.

Why Automotive Sits at the Epicenter

Few industries illustrate the stakes more clearly than automotive. Modern auto supply chains are deeply integrated with components crossing borders multiple times before final assembly. Electric vehicle production adds further exposure through batteries and critical minerals. Just-in-time logistics magnify even minor disruptions.

Under a compliance-and-recalibration scenario, automakers may pursue tariff reimbursement claims while cautiously restoring cross-border efficiencies. Under sectoral escalation, autos and parts could become direct targets. Input costs would rise. Supply chains linked to Mexico and Canada would come under renewed pressure. Pricing strategies would require adjustment. Investment decisions on plant location and sourcing could accelerate.

In either case, the risk environment remains structurally elevated. The difference lies in duration and concentration. Broad volatility affects many sectors moderately. Sectoral targeting affects fewer players intensely.

How Senior Leaders Should Respond

The Supreme Court’s decision did not end the era of contested trade policy. It changed its operating system. Emergency-based tariff shocks are less viable. Structured, legally grounded trade actions are more likely. Near-term volatility will persist as policymakers recalibrate and litigants test the limits.

For senior executives, the imperative is not to forecast the next headline. It is to build strategic, financial, and operational resilience. The right question is not “Will tariffs return?” but “How resilient is our operating model across multiple tariff regimes?”

Companies should map exposure across products and markets; stress-test margins under varying tariff levels and durations; sequence capital allocation to preserve optionality; and engage proactively in coordinated policy advocacy where sectoral risks are rising.

Trade policy will remain a strategic variable. It will now unfold through more formal channels, over longer timelines, and with greater institutional friction. The phase of emergency tariff shocks may be narrowing, but the phase of structural trade tension is not.

• The agreement modestly narrows, but does not eliminate, the labor cost gap between VW Chattanooga and the Detroit 3.

• Lengthy negotiations and tangible gains bolster UAW credibility with VW workers.

• The contract follows standard UAW patterns; there is no Works Council–style governance or evidence of coordination with IG Metall.

• Volkswagen’s refusal to align contract expiration with May 1, 2028 weakens UAW ambitions for coordinated national action.

• Foreign-owned OEMs may raise wages preemptively; rapid unionization momentum remains unlikely.

I. What Changes

UAW credibility at foreign-owned plants. The agreement was hard-fought. After Volkswagen cut a shift in response to soft EV demand, the UAW filed an unfair labor practice charge, rejected a “best and final” offer, and secured strike authorization. The resulting deal includes stronger job protections, bonuses, and other gains beyond Volkswagen’s final offer. These outcomes strengthen the UAW’s standing with Chattanooga workers and validate Fain’s post-2023 organizing strategy.

Marginal labor cost convergence. The contract includes a roughly 20% wage increase, COLA, bonuses, and reduced healthcare costs. Still, top pay for VW workers will remain under $40/hour even in 2030, compared with $43/hour at the Detroit 3 by 2028, and VW workers will take longer than those at the Detroit 3 to reach that level. Bonuses lag recent Detroit 3 profit-sharing checks, and the all-in labor cost gap may remain around $20/hour. This narrows, but does not erase, the structural cost advantage of Volkswagen relative to the Detroit 3 and opens a gap between VW and other foreign OEMs.

II. What Doesn’t Change

Governance and decision rights. The agreement appears conventional, with standard language on work rules, grievances, outsourcing, and job security. These terms constrain management decision-making and help explain why foreign OEMs, like many other employers, resist unionization. However, there is no indication of union participation in operational decision-making akin to a Works Council. Nor is there evidence of coordination with IG Metall during bargaining; notably, the UAW did not publicly express solidarity when Volkswagen announced restructuring in Germany.

National coordination of labor action. The UAW sought to align the Volkswagen contract expiration with the Detroit 3 on May 1, 2028, a key element of Fain’s ambition for coordinated national action. Volkswagen refused, and the agreement now runs to 2030. Aside from limited signals from the SEIU, there is little evidence that a cross-union alignment strategy is taking hold.

III. Implications for unionization in the South

When the UAW won the Chattanooga vote, two strategic paths for further unionization gains emerged:

1. The “Dominoes Game”: building momentum plant by plant through successive organizing wins.

2. The “Contract Game”: using a strong Volkswagen agreement to persuade workers elsewhere that unionization delivers tangible gains.

The Dominoes strategy stalled after UAW losses at Mercedes plants in the South in the spring of 2024. Whether the UAW now invests heavily in the Contract Game remains unclear. The prolonged Volkswagen negotiations, a less union-friendly NLRB under the Trump Administration, and internal UAW distractions may limit appetite for an aggressive push. Fain may instead declare victory, focus on reelection, and conserve resources for 2028.

That said, the agreement will not go unnoticed. As after the 2023 Detroit 3 contracts, foreign automakers may choose to raise wages proactively to reduce union risk.

IV. The bottom line

The Volkswagen-UAW agreement represents a symbolic and political win for the UAW, but merely a limited structural shift for the industry. It modestly compresses labor cost differentials, clarifies the bounds of union influence at foreign-owned plants, and improves Shawn Fain’s chances of reelection. If Fain remains in office, executives should expect a renewed, and potentially disruptive, push at the Detroit 3 in 2028 - likely involving escalated strike tactics aimed at reversing long-standing retirement concessions. Planning for that risk should begin now.

A year ago, we argued that the events of 2024 had effectively answered seven major questions - each a lens into the political, economic, social, and technological forces shaping the global business environment. Those answers did not bring clarity or stability. Instead, they set the conditions for the five most consequential business developments of 2025, which we review here as our annual recap of the year’s defining trends.

2025: A Surprising Year That Shouldn’t Have Been

2025 was, by any measure, another eventful year. Uncertainty deepened, volatility intensified, and periods of apparent stability were repeatedly punctured by disruption and, at times, outright chaos. The single most consequential event was, of course, the re-election of Donald Trump as President of the United States, an outcome whose implications extended far beyond U.S. borders. The new administration’s markedly different perspective on trade, regulation, climate policy, immigration, and the global order reverberated across industries and geographies.

Many companies were initially caught off guard but still responded effectively, drawing on crisis playbooks refined through years of disruption from the first U.S.–China trade war and the pandemic to the semiconductor shortage. They built inventory buffers, rerouted supply chains, curtailed discretionary spending, and postponed high-commitment decisions. Pricing discipline largely held, though at the expense of margins. But as the year progressed and shocks accumulated rather than dissipated, many leadership teams found themselves uncertain about what to do next. Tactical resilience proved easier to master than strategic direction.

None of this should have come as a surprise. In a C-suite article we published in July 2024 -well before President Biden withdrew from the election - we examined the implications of a potential Republican sweep. That analysis anticipated the core dynamics that came to define 2025: an intensified “America First” posture, rising trade barriers, continued U.S.–China tensions, regulatory rollbacks (particularly on environmental issues), tighter labor markets driven by immigration policy, and renewed pressure on global institutions as the U.S. implicitly enabled a more multipolar world order. We also outlined the second-order effects: inflation volatility, supply-chain realignment, and uneven sectoral outcomes, especially in green technologies and AI.

Importantly, these were not predictions in the conventional sense. They emerged from a disciplined examination of underlying drivers and incentives - an approach that often produces outcomes that align more closely with reality than formal forecasts do.

Against that backdrop, we turn to our perspective on the five most important business developments of 2025: why they mattered and what they imply for C-suite leaders looking ahead.

1. Geopolitics and the New World Trade Order

The defining macroeconomic reality of 2025 was not recession or recovery, but fragmentation. As we wrote previously, “with trade increasingly seen as a tool of industrial and foreign policy, global supply chains face massive disruption.” In 2025, that disruption moved decisively from theory to operating reality.

The Trump administration’s sweeping tariffs pushed the U.S. effective import tax rate to levels not seen since the 1930s. Tariffs became less about trade balances and more about leverage, forcing companies to reassess not only where they source and manufacture, but which markets they can reliably serve at all.

A direct confrontation with China exposed a structural asymmetry at the heart of the global economy. Washington’s tariff threats - ranging from 25% to well above 100% on selected imports - were met not with capitulation but with targeted retaliation. Beijing’s response, including export controls on critical rare-earth materials and abrupt halts to U.S. agricultural purchases, underscored that geopolitical leverage increasingly rests on control of bottlenecks, not just trade volumes.

Crucially, the fallout did not remain bilateral. As we warned earlier, “if a tariff spiral does ensue, the indirect spillover effects would likely be significant as well.” Reduced Chinese access to the U.S. market flooded the EU and parts of Latin America with excess capacity, triggering defensive measures across industries such as steel, chemicals, EV components, and industrial machinery. The result was an acceleration toward a more transactional, regionalized trade system.

Geopolitical instability compounded these pressures. Iran’s sharply weakened position, following Israeli military actions and the collapse of much of its proxy network, reshaped Middle Eastern power dynamics. As we noted previously, this raised the risk of broader regional escalation involving the U.S. and other Western powers. For global businesses, the impact was immediate: renewed energy market volatility, higher insurance and logistics costs, and persistent uncertainty around shipping routes and regional investments.

At the same time, the multilateral institutions required for managing systemic risk appeared increasingly fragile. “NATO and the EU, weakened by recent elections, lack cohesion,” limiting Europe’s ability to act as a stabilizing force in security or economic policy. For multinational firms, this meant navigating a patchwork of national responses rather than a coordinated transatlantic approach, particularly in industrial subsidies, defense spending, and trade enforcement. Europe increasingly combined regulatory weight with diminishing growth momentum, an uncomfortable mix for global firms.

Importantly, strain was not confined to emerging markets. “Even North America feels the strain,” as hardline trade and immigration policies amplified uncertainty across U.S.–Mexico–Canada supply chains and intensified labor shortages in logistics, agriculture, and manufacturing.

For the C-suite, the lesson was clear: assumptions of a stable, rules-based global trade system no longer hold. Competitive advantage now depends less on global optimization and more on geopolitical resilience – i.e., supply-chain redundancy, regional manufacturing strategies, and the ability to pivot quickly as trade and foreign policy collide.

2. Economic Resilience Masks a Deepening K-Shaped Recovery

Despite trade turbulence and geopolitical shocks, the U.S. economy proved remarkably resilient. Beneath the surface, however, a K-shaped recovery deepened. Higher-income households continued to spend, while lower-income households faced persistent cost-of-living pressures. In the short term, this divergence sustained aggregate demand. Over the long term, it raises more troubling social and economic questions.

The labor market tells the story. Headline employment remained stable, but hiring slowed markedly, creating a “low-hire, low-fire” environment. Job prospects deteriorated sharply for new college graduates as firms delayed expansion and AI reduced demand for entry-level roles.

At the other end of the spectrum, labor shortages intensified in manufacturing, construction, healthcare, logistics, and agriculture. These shortages were not cyclical but structural, driven by reduced immigration flows, large-scale deportations, and underinvestment in vocational training and reskilling programs.

More concerning still was the erosion of the U.S. as a global talent magnet. Visa crackdowns, higher fees, longer processing times, and a more hostile political climate made the U.S. less attractive to STEM professionals and researchers. For decades, innovation leadership rested on this advantage. In 2025, it began to weaken visibly as top candidates increasingly chose Europe, Canada, or parts of Asia.

For business leaders, the implication is clear: workforce strategy is no longer an HR concern. It is a core element of competitive positioning, productivity, and long-term innovation capacity.

3. AI’s Breakout Year—and Why Value Remained Elusive

2025 was AI’s breakout year. Adoption accelerated, investment surged, and AI moved from experimentation to deployment. Yet for most companies, results fell well short of expectations as multiple studies suggested that as many as 90–95% of corporate AI initiatives failed to deliver meaningful business impact.

History offers a parallel. During the dot-com boom, digital technologies diffused rapidly, but productivity gains lagged for years – i.e., the Solow Paradox. The lesson was not technological but organizational. As we have written previously, organizational change and public policy ultimately shaped the trajectory of past technological revolutions far more than the technologies themselves.

Most AI failures share a common root cause: attempts to layer AI onto legacy structures, processes, and incentives. AI teams often operate in isolation, disconnected from business units, lacking decision rights, clean data, or sustained executive sponsorship.

Public policy adds another layer of complexity. Governments oscillate between promoting AI as a growth engine and constraining it due to concerns over privacy, bias, labor displacement, and national security. As we have noted before, “the future of digital transformation depends on what happens inside organizations and in the external policy environment, not just on the technology itself.”

The implication for executives is clear: AI advantage will accrue not to the early adopters, but to firms willing to redesign how work gets done.

4. The EV Reversal, Energy Policy Whiplash, and the China Question

After years of regulatory momentum, 2025 marked a clear inflection point in the U.S. energy transition. Rollbacks of EV subsidies and renewed support for fossil fuels reshaped investment economics almost overnight. Automakers, utilities, and suppliers were forced to reassess demand trajectories, infrastructure plans, and capital allocation.

The slowdown in EV adoption also reflected a familiar adoption “chasm.” Early adopters were largely saturated, while the early majority remained hesitant due to cost, infrastructure gaps, and resale concerns. This domestic hesitation intersects with a more strategic issue: the rise of Chinese EV manufacturers. As we argued earlier, the threat hinges on two variables: U.S. EV adoption and Chinese market access. In 2025, both became clearer. Firms like BYD demonstrated that they are not theoretical competitors but proven global players, combining vertical integration with aggressive cost advantages.

Mexico emerged as a strategic wildcard. By leveraging USMCA provisions, Chinese OEMs positioned themselves to serve price-sensitive segments, initially with ICE and hybrid vehicles, and potentially EVs later. The risk for U.S. incumbents is not immediate collapse, but strategic drift, leaving them underprepared if adoption reaccelerates and market access widens simultaneously.

The broader lesson extends beyond autos. Energy transitions are no longer linear or globally synchronized. They are politicized, uneven, and competitively disruptive.

5. Populism, Governance, and the Limits of the Old Order

2025 confirmed that populism is no longer a reaction - it has become a governing force. As populist movements transitioned from opposition to administration, internal contradictions became visible. Governing requires compromise, prioritization, and administrative capacity - qualities often at odds with populism’s mobilizing logic.

Across advanced economies, these tensions surfaced repeatedly. Coalitions forged in opposition proved difficult to sustain once policy choices imposed real costs. Protectionism collided with inflation control; immigration crackdowns worsened labor shortages; fiscal populism ran into debt constraints. For businesses, this represents a structural increase in political risk across markets once considered stable. The challenge is no longer how to wait out populism, but how to operate in an environment of fragmented legitimacy and uneven governance capacity.

Strategic Takeaways for C-Suite Executives

If 2025 demonstrated anything, it is that volatility is no longer an aberration - it is the new baseline.

First, enterprise risk must be mapped globally and dynamically. JIT supply chains optimized for efficiency have become structurally fragile.

Second, executives must plan for divergent consumer segments and labor realities shaped by uneven adoption, constrained talent supply, and shifting migration patterns.

Third, technology creates value only when paired with organizational change and governance. Tools alone are insufficient.

Fourth, energy and climate policy uncertainty must be incorporated into capital planning. Linear transition assumptions are no longer viable.

Finally, populism has moved from protest to power without fully replacing the old order, creating regulatory fragmentation that demands rigorous stress-testing.

If 2025 was the year companies absorbed this reality, 2026 will be about translating reactive resilience into proactive foresight and strategy. Ultimately, the organizations that succeed will stop waiting for clarity and instead build the organizational capabilities and processes needed to perform consistently across volatility and uncertainty.

References

Are Chinese EV Makers an Existential Threat to U.S. Legacy Automakers?, Jul ‘25

The Rare-Earth Chokehold, Jun ‘25

Seeing Around Corners, Mar ‘25

2024 Questions Revisited - Looking Back and Looking Ahead, Jan ‘25

Preparing for Trade Disruptions, Dec ‘24

Prestige vs. Dominance: Leadership Matters in the U.S. Presidential Election, Nov ‘24

The Electric Vehicle Slowdown: A Lesson in Technology Diffusion and Saddle Patterns, Aug ‘24

Strategic Planning for U.S. Election Scenarios, Jul ‘24

2024 – The Year Questions Become Answers, Feb ‘24

Turbulence Ahead: US-China Relations in a Shifting Landscape, Sep ‘23

Scenarios for the Future of AI and Digital Transformation, Jun ‘23

Learning from the History of Technological Revolutions, May ‘23

Eric Gertler has led organizations across the media landscape, from the New York Daily News to U.S. News & World Report, during one of the most volatile periods in the industry’s history. Under his leadership, U.S. News has undergone a profound transformation from a money-losing print magazine into a highly profitable digital platform. We spoke with Gertler about what has driven that transformation and the lessons it holds for executives in any industry. (Gertler has also held high level leadership roles in government and philanthropy.)

C-Suite: Eric, U.S. News has changed dramatically under your tenure. What has guided your decisions and what lessons might apply beyond media?

I have had the privilege of leading a brand that has been around for over 90 years. Of course, the world has changed a lot over those 90 years and so we have had to evolve ourselves accordingly.

We have gone from a money-losing print magazine to a highly profitable, robust digital marketing platform. To do that, we have had to understand and stay true to our values, starting with a clear North Star: helping our consumers make the most important decisions in their lives. It doesn’t really matter whether we are doing that in print or any other distribution format.

Equally important has been integrity about what we produce, write, and distribute. We have maintained a high quality level of the editorial content we produce while ensuring that our editorial team operates free from business interference, which reinforces the high degree of credibility that we enjoy. And that credibility has been essential as we expanded into areas like education, healthcare, business, money, autos, and travel. Today, we focus heavily on important consumer news that affects people’s lives – it is critical that people know that they can trust the information we provide, which reinforces the trust in our brand; that trust has been the foundation for our growth.

We have also transformed into a data and information business and that is not obvious at all for a journalism company. We receive a lot of respect for our rankings of colleges as well as for our rankings on best hospitals. And that is impressive, as, in one sense, there can only be one winner in a category. But we are able to stand behind our rankings because of the rigor, transparency, and integrity of our methodology. Competitors may try to replicate them, but our commitment to accuracy and clarity really does set us apart.

C-Suite: What is your leadership style in that context? Has it evolved as you’ve navigated the structural shifts?

My leadership style has definitely evolved, though probably more from moving back and forth between the public sector and the private sector than from managing a private company alone.

Having led in the private sector first made me a better leader in public service because I understood what really matters there: execution and getting things done right. Public service, on the other hand, requires much more collaboration. You are operating in three dimensions and constantly factoring in public opinion. When I returned to the private sector, I brought that mindset with me and became a more collaborative CEO.

Another big lesson from public service is scale. The projects are so large that you have to learn to focus on what truly matters and avoid micromanaging. That is especially true when you are transforming a media company in the middle of significant turmoil. You need the right people in the right seats, moving in the same direction, and you simply cannot manage everything yourself. There is too much going on.

I have learned to trust and empower my team. If there is a crisis or a conflict between two areas of the company, I get involved. Otherwise, you need to set a direction and ensure you have the right people to execute well and use good judgment, because no single person can do it all.

C-Suite: AI is transforming the entire media landscape. What parts of media will AI fundamentally reshape and which parts will remain human for longer than people expect?

We are probably still in the very first inning of AI’s impact. AI is already having a dramatic effect on businesses, both because of what it is actually doing and because of how leaders perceive what it will do.

Google has historically been the main funnel driving traffic to digital media companies. But as it increasingly relies on AI-generated overviews that aggregate content, fewer users are clicking through to individual sites. That fundamentally undermines the economics of a media business: fewer eyeballs, fewer consumers, and less value for advertisers or performance marketing. If you look at large media companies that depend heavily on Google, many have seen traffic declines of 25% to 50% over the past year. That’s dramatic.

On the flip side, there is the real, constructive impact of AI. We, and many others, are using AI to improve products so that when users do come to the site, they get a much more valuable experience.

The big question, of course, is whether AI replaces journalists and the content media companies produce. The answer is not yet, but it is coming faster than most people are prepared to accept. We are already seeing AI used in parts of the process: helping journalists with research, background, and editing. Still, we have our journalists reviewing any part of our AI usage.

Then there is what I would call the perceived effect. That occurs when leaders start changing their businesses based on what they think AI will be able to do, even though the technology is not actually there yet. You end up making big decisions without fully understanding what the real impact will be.

There’s also great irony here. Many of the AI systems were trained on the journalism and content created by media companies. Yet some of those same media companies may not exist in the future, while these AI companies will still need that content to keep learning and improving. I think the AI companies are starting to recognize that, which is why you’re now seeing so many content deals happening. In many cases, it’s been a matter of “better to beg for forgiveness than ask for permission,” because without that content, these systems never would have reached their current level of intelligence.

But, I assume every part of our business will be affected either positively or negatively. What I have emphasized internally is that, through this transformation, when consumers see our brand, they need to trust what we are doing. Whether it is AI-enabled products or intelligent bots on our site, people should know that when they see the U.S. News logo, they can rely on what we put in front of them.

C-Suite: What are the most difficult transformation decisions?

The hardest decisions always involve people. Everything else is just working through a problem. With people, there are emotional attachments and long-standing relationships. If, as a CEO, you no longer feel emotion when you are letting people go, then you have probably stayed too long.

Even in a world of increasing technology, you have to respect the individuals in your organization. The real challenge is deciding who to empower to lead the transformation, who can be reskilled or redirected into growth areas, and who ultimately has to be let go. We recently let someone go who had been with the company for decades. Those are incredibly difficult decisions.

We try to handle those moments with dignity, because what matters most to me is respecting the people who have given their time and energy to the company. And, if you have a talented person on your team and they leave for another opportunity, that is a failure as a CEO.

C-Suite: You’ve mentioned that rankings are a major differentiator for U.S. News. In the age of AI, how do you protect that asset?

You can see differentiations today in how journalism, in a broad sense, is perceived today. A media company like ours or The Wall Street Journal, for example, has built its brand on integrity and high reporting standards. At the same time, many young people get their news from TikTok, meaning content with no context, questionable accuracy, and little or no sourcing. People may turn to either social media or traditional media, but the hope is they understand there is a greater level of trust, context, and reliability with a brand like ours or the Journal.

The same applies to rankings. You can ask an AI tool for a “top” list and get an answer, but you do not know the context or the methodology. Can you trust it? Has it been reviewed? Is it right for you?

We are still at a point where people want a credible brand standing behind the rankings and a thoughtful evaluation process they can understand. And rankings are not simply a list. Consumers can slice and dice them based on important criteria like, for our education rankings, debt levels, best schools for veterans, or HBCUs. There is also an element of accountability for the institutions themselves. The institutions must understand the process and see it as credible.

We may be living in a new technology-driven world, but when people see our brand, they know the serious work that is behind anything that we publish. They know there is a methodology they can read and evaluate for themselves. When you are making consequential decisions, about hospitals, higher education, or more, the source really does matter.

C-Suite: This has been a turbulent year for higher education, including increased politicization. How are you dealing with that at U.S. News?

We have stayed focused on what we do best: outcome-driven rankings. We ask fundamental questions. Is the university graduating its students? Are students graduating with manageable debt? Those are the two most important questions facing higher education today.

There are thousands of universities, and for decades they promised that a degree would lead to a better, more prosperous life. For many people, that has not happened over the last 15 years. That is why trust in universities has dropped from around 70% to the mid 30% range. It also explains why enrollment has declined. Today, we have roughly 40 million Americans with some college education, no degree, and student debt. Many feel the system has failed them.

The real workhorses of higher education are public universities and community colleges. What gets most of the attention, though, are the elite institutions, which I believe have shifted in some cases from being purely educational to partly ideological. They have moved away, in part, from teaching students how to think toward telling students what to think.

I have had public disagreements with some of the institutions at the top of our rankings. I have consistently said two things. First, when colleges charge this high level of tuition, they owe students full transparency so families can make informed decisions. Second, colleges must be accountable. Many institutions, especially elite ones, have operated behind ivory towers and grown disconnected from broader society. There has been a lot of good in that, but there has also been a loss of accountability.

Parents and governments are now saying they do not want to continue to provide funding without clear expectations being met. Whether those expectations are right or wrong, we live in a world where transparency and accountability matter, and that applies to everyone.

C-Suite: We’ve covered a lot of ground. What lessons would you want to share with C-suite leaders in other industries?

I am always looking for lessons from other leaders myself. When I think about what has driven the success of U.S. News, a big part of it is that we have been able to build businesses across multiple categories: news, health, education, money and insurance, small business, travel, autos, and product reviews. That diversity has allowed us to grow and become very profitable.

The first piece of advice is to prioritize areas that offer the greatest potential return and are fully aligned with your brand. In a time of transformation, you have to ask: where do you get the biggest bang for the buck? You simply cannot do everything.

The second lesson is to put execution ahead of strategy. Strategy gives you direction, but real learning happens through execution. You have to be willing to launch, test, make mistakes, and learn from the market.

Third, CEOs need to stop thinking they have all the answers. We have moved from a top-down world to one driven by bottom-up learning. Organizations perform best when ideas come from everywhere, especially from people on the front lines or those new to the company. If you can create mechanisms to surface and act on those ideas, the company performs infinitely better.

Finally, as a CEO, give the credit away to others. Even better, do it publicly. It motivates your employees and the company. You benefit anyway if others perform well.

Welcome to the second issue of our economic and business roundup. Each month we focus on the top 3–5 forces shaping markets, geopolitics, and corporate decision-making. This month, the common thread is policy divergence and structural uncertainty, creating a more volatile and less predictable operating environment.

1) The U.S. Economy: Soft Landing, Tight Supply, and Policy Volatility

The U.S. economic growth is increasingly uneven (i.e., “K-shaped”) with higher‑income groups advancing while lower‑income workers fall further behind. Consumer sentiment remains below prior levels as households grapple with high prices and labor‑market uncertainty. Nevertheless, overall consumer spending and services are holding up for now while inflation seems to be easing toward the Fed’s 2% target. Interest rates remain elevated, but further rate cuts are a near-certainty after Powell departs next year.

Key structural pressures:

• Labor constraints: Economic growth slowed by talent shortages across sectors (e.g., logistics, construction, healthcare), compounded by a net outflow of immigrants and the retirement of baby boomers

• Capital costs: Borrowing remains expensive, limiting investment

• Fiscal trajectory: High deficits introduce uncertainty for interest rates, bond markets, and public-sector contracts

• Policy volatility: Upcoming elections and regulatory shifts could affect taxes, industrial policies, immigration, and environmental compliance

Bottom Line: While the economy appears relatively stable on the surface, C-Suite executives should anticipate intermittent shocks, a tightening labor supply, and persistent volatility in fiscal and regulatory policy, alongside the looming risk of asset bubbles deflating across stocks, crypto, AI, and housing.

2) The Supreme Court Tariff Case: Potential Realignment of U.S. Trade Authority

The Supreme Court is set to rule on Presidential tariff authority. This decision will shape whether the White House retains unilateral control or whether Congress reclaims a significant role.

Potential outcomes and second-order effects:

• Limited authority: More predictable tariffs, slower deployment, fewer surprises

• Expanded authority: Accelerated tariffs, retaliatory measures, inflation pass-through, and supply chain disruptions

Retroactive implications: If SCOTUS rules against the Administration and tariff payments already made must be refunded, companies could face a temporary windfall, but also substantial operational uncertainty as customs authorities determine how refunds are processed. Large retailers and importers may need to manage complex accounting adjustments, potentially impacting cash flow and quarterly results. At the same time, the ruling could trigger pressure to reinstate tariffs under a new framework, creating further decision-making complexity.

Bottom Line: Tariffs will remain a major source of policy uncertainty. Companies should scenario-plan for sudden shifts in sourcing, pricing, and logistics now to avoid scrambling for operational or financial adjustments later.

3) Climate Policy Divergence: Fragmentation, Governance, and Capital Allocation

Global climate policy is diverging rather than converging, with the U.S., EU, and China moving on different trajectories that create regulatory and operational complexity: The U.S. is implementing a (temporary?) policy reversal, the EU is facing implementation delays, and China is pressing ahead at full speed.

Key dynamics:

• U.S.: Litigation and political pressure complicate SEC climate disclosure and emissions standards; California continues to enforce stricter rules

• EU: Aggressive emissions targets, carbon border adjustments, and CSRD reporting requirements continue but with increasing resistance

• China: Focused on competitiveness and standards-setting in EVs, batteries, and solar

Business implications:

• Boards face increasing fiduciary responsibilities around climate disclosure

• Capital investment and insurance costs are rising due to extreme weather, infrastructure constraints, and retreating coverage

• Multi-jurisdictional compliance is costly and will persist for the next decade

Bottom Line: Climate is no longer a long-term horizon issue but a medium-term operational and strategic constraint.

4) Japan–China Tensions: Economic Rivalry, Strategic Risk, and Supply Chain Exposure

Tensions in East Asia are intensifying not only due to Japan’s military modernization and China’s assertive regional posture, but also because of the role of Japan’s new Prime Minister Sanae Takaichi, whose hardline stance on Taiwan and accelerated defense agenda have further sharpened regional frictions.

Key points:

• Red lines remain fragile: Taiwan independence continues to be China’s ultimate non-negotiable red line

• Alliance dynamics: U.S. guarantees shape Japanese actions and influence regional signaling

• Operational exposure: Semiconductors, battery materials, precision machinery, and shipping lanes are vulnerable to disruption

Bottom Line: Geopolitical risk is unlikely to escalate to conflict in the short term, but supply chain and trade disruptions are increasingly probable. Scenario analysis remains essential.

5) Autonomous Vehicles: The Shift from Hype to Practical Deployment?

The AV landscape appears to be moving from hype to selective, practical deployment. Funding and deployment are now concentrated among a smaller set of players such as Waymo, Tesla, and key Chinese operators.

Key dynamics:

• Safety outcomes: Recent high-quality studies suggest significant safety benefits for Waymo AVs compared with human drivers in deployed areas

• Economics: Robotaxi unit economics remain challenging; middle-mile and freight applications show clearer ROI

• Regulatory fragmentation: Different U.S. states impose varying safety, testing, and liability requirements

• AI oversight: 2025 executive orders increase scrutiny on safety-critical AI systems

Caution: The automotive industry has a long-standing tendency to go “all-in” on emerging trends (e.g., EVs, AVs, fuel cells, telematics, M&A) only to encounter setbacks when reality invariably catches up with the hype. AVs are especially sensitive, subject to intense scrutiny and widespread media attention, where even a single high‑profile incident can derail public acceptance and regulatory progress, impacting not just individual companies but the entire sector (e.g., Cruise, Tesla, Uber).

Strategic implication: A more thoughtful, scenario-based approach is essential. Companies should consider a range of plausible outcomes rather than relying on most-likely or best-guess forecasts, especially for AVs where technological, regulatory, and public perception risks remain high.

Bottom Line: AV adoption is real but incremental. Executives should invest selectively, partner strategically, and avoid overcommitting capital until the sector demonstrates sustained, scalable performance.

Key Take-Aways

• Expect divergence, not convergence. Macro, trade, climate, and geopolitical policies are moving in different directions across regions

• Scenario analysis is critical. Model multiple economic, policy, and supply chain scenarios to maintain optionality

• Build operational resilience. Labor, energy, insurance, and capital constraints require flexible contracts and adaptive sourcing

• Strategic capital allocation: Prioritize investments with optionality, resilience, or early-mover advantages, while avoiding overcommitment to high-risk, unproven technologies

• Geopolitical vigilance: East Asia, trade enforcement, and climate regulations remain the highest-consequence risk zones

Each episode begins with an introductory meeting between Ramsay and the star-struck restaurant owner and his staff. Following an initial exchange of pleasantries - where the hapless restaurateur explains his misfortune and gratefully pledges to place the future of his business in the experienced hands of our intrepid chef - the fun begins, as Ramsay sits down to enjoy his first meal. Whether through dumb luck, sheer brilliance, or, more likely, clever pre-screening by his production team, Ramsay invariably selects the one dish the restaurant’s own chef doesn’t know how to prepare, and, with that, our first “moment of truth” has arrived; nervous waitresses and a completely befuddled chef begin to panic as an obviously perturbed Ramsay takes a few bites, refuses to eat any further, and orders something else from the menu instead - usually with equally disastrous results.

Having established that the restaurant’s poor financial results may just be a function of the owner and staff’s own incompetence - instead of “market forces” beyond their control - chef Ramsay proceeds to create the proverbial “burning platform” for fundamental change. This is usually by far the most entertaining part of the show, when, spoiling for a good fight, Ramsay lets loose in a foul-mouthed tirade designed to positively scare the restaurant crew out of their collective wits - while simultaneously serving up a generous portion of “schadenfreude” to the television audience at home - but also with the desired effect of shaking the organization out of its ingrained bad habits: invariably the food is absolutely terrible, the ingredients artificial, the menu a joke, the restaurant concept incomprehensible, processes non-existent, and cleanliness so poor that, in the words of our chef, “people may die here tonight!”

With the initial Diagnostic phase completed, and the “need for change” firmly established, Ramsay now sets out by himself to do some market and customer research. He walks around the neighborhood, checking out “latent demand” for potential new restaurant concepts. Once a potentially more competitive “value proposition” has been identified, Ramsay and his team completely remodel the restaurant overnight to “shock and awe” the ecstatic owner and his staff. The new repositioning concept is explained. We get a tour of the newly remodeled restaurant and anxiously await opening night. This is usually still touch-and-go, as the restaurant staff valiantly try to adapt to their new environment, but, with in-kitchen and on-the-floor help from the good chef himself, all ends well, and the show concludes with lots of hugs, kisses, and a tearful goodbye.

So there you have it. All the ingredients for a successful business turnaround are there: fresh market and customer research as the main ingredients, a main course of successfully repositioned products, served in newly refurbished and rebranded facilities, all lovingly prepared on a clear mandate for fundamental change, and, of course, deliciously entertaining. Bon appétit and Happy Thanksgiving!