When allies of President Trump went on television over the weekend to say that a peace agreement between the U.S. and Iran was 95% complete, I was reminded of a truism among software developers in the 1990s: "The first 80% of a project takes 90% of the time, and the final 20% takes 90% of the time."

Comments on social media noted that even if negotiators had made it through 95% of the issues on their checklists, that progress meant that the U.S. and Iran still had to agree on what to do about Iran's stockpile of enriched uranium, about reopening the Strait of Hormuz and about U.S.-led economic sanctions against Iran. You know, the little stuff.

While there have been more reports today on potential progress, I'm ready to call it. Having covered an international mess or two in my time, I think the situation in Iran has all the markings of a long-term impasse. I'm convinced Iran will be an open wound for many years to come. The best we can hope for, I believe, is the sort of "ceasefire war" occurring in Gaza, where the fighting has officially been ended but the underlying conflict still festers.

Insurers need to prepare themselves. 

I'll still hold out hope that President Trump can achieve the kind of agreement with Iran he's been promising: reopening the Strait of Hormuz to any and all traffic, without tolls, while removing Iran's ability to ever build a nuclear weapon. But I no longer think any such resolution is likely. I don't even think a clean, lasting agreement is possible.

The clincher for me came this morning, when I read a column in the Washington Post by a former colleague of mine from the Wall Street Journal, Karen Elliott House, who is as plugged in to the Middle East as any journalist could possibly be. Karen won a Pulitzer Prize for her coverage of the Middle East in the 1980s and has stayed plugged in to the point that last year she published the definitive biography of Saudi Crown Prince Mohammed bin Salman. 

She wrote: "When the U.S. and Israel unleashed a blistering bombing campaign striking more than 10,000 Iranian targets, and Tehran’s response included attacks on Saudi oil installations and military bases, the Saudi air force initially struck back. But as President Donald Trump tolerated a ceasefire longer than the war itself, and repeatedly threatened to resume hostilities only to back off, the crown prince concluded that he must live with a hostile regime in Tehran. His focus now will be on placating Iran to protect Saudi."

If the Saudis have switched to appeasement — and I believe Karen implicitly — they leave Trump with almost no choice. Even if he felt he could ignore the need for congressional approval of a conflict lasting more than 60 days, under the War Powers Act, or could win approval from a Republican Congress increasingly nervous about an unpopular war as we head into the mid-term elections, Trump isn't going to resume hostilities without support from this major Middle Eastern ally. That means he's left having to negotiate with an extremist, theocratic regime that, by all accounts, thinks it has won the war.

Whatever deal ensues, Trump will surely claim a major victory, but Iran will remain volatile. Having shown the world that it can close the Strait of Hormuz, even while under attack by the world's greatest military power, Iran will keep shippers on edge, thus keep oil markets nervous. Iran will surely retain enough of a pathway to nuclear weapons that there will be the prospect of additional air strikes like the one the U.S. and Israel carried out on Iran last summer. If the U.S. eases economic sanctions, as Iran is demanding, Iran will surely funnel some of that money to its proxies throughout the Middle East as they try to destabilize Israel, Iraq, Lebanon, and Yemen. 

Because the Middle East is so unlikely to return to the conditions before the war, insurers should assume that conditions today will persist for at least many quarters and probably many years.

Shipping patterns will adjust to the higher risks in the Strait of Hormuz, and insurers will have to adjust to those new patterns. Global supply chains for all sorts of goods will change, keeping replacement parts for cars hard to get and limiting access to housing materials, so pressure on premiums will continue. 

Gasoline prices will drop somewhat but remain steep, keeping a lid on traffic and, thus, traffic accidents. People will fly less, in the face of increased air fares, reducing the demand for travel insurance. With gas prices driving inflation, interest rates are likely to stay elevated; the housing market is already a disaster, and sales will stay depressed, reducing opportunities for homeowners insurance companies to attract new customers. 

And so on. 

There will surely be secondary effects, too, though those are obviously harder to predict. The big question, for me, relates to the mid-term elections. With Trump's approval ratings already at record lows, and with Iran looking like a strategic error, the Republican party will almost certainly lose control of the House of Representatives and perhaps even the Senate. All those investigations that the Democrats have talked about wanting to launch into Trump administration actions could become reality next year. Democrats may be a bit cautious because some of the investigations they launched in the lead-up to the 2024 election backfired and let Trump generate support — or they may not. The federal government could pretty much shut down until the 2028 presidential election as Democrats and Republicans scream at each other. Meanwhile, issues that are important to the insurance industry, such as the fates of FEMA and the National Flood Insurance Plan, would be set aside.

My second biggest question relates to Taiwan. Might China decide that now is a good time to try to retake control of the island, with the U.S. looking weak and having used up so much of its weapons stockpile in Iran? What a catastrophe that would be for the whole global economy.

But now I'm getting really speculative. We'll have to wait and see how the secondary and tertiary effects unfold. For now, I really just wanted to note that I don't believe we'll have a clean resolution of the U.S.-Israel conflict with Iran and that we are likely going to be dealing with lingering effects for a long time.

Cheers,

Paul

In April, something significant happened in the auto industry: Tesla confirmed that production had begun on its Cybercab, a fully autonomous vehicle with no steering wheel, no pedals, and no human in the loop. Until now, the conversation has focused on what this means for Uber and Lyft and on whether robotaxis are going mainstream.

But perhaps there's an equally consequential question. What happens to the insurance industry once the driver has gone the way of the Edsel? Unfortunately, the industry has not seriously tried to answer it.

The Model Was Built Around the Human

Commercial auto insurance was designed around a single variable: the person behind the wheel. That is why insurance prices reflect driving behavior; liability follows whoever was driving, and policy language assumes a human making decisions on the road in real time. The full architecture of risk assessment, premium calculation, and claims resolution rests on the assumption that human judgment is what gets priced.

Open almost any commercial auto policy today, and the human driver as the unit of risk appears on nearly every page. But remove the driver, and pricing assumptions, liability triggers, and claims logic all rest on a human variable that no longer exists. So the language built for that world has to be rewritten.

Autonomous vehicles are no longer theoretical. From Level 3 consumer vehicles to more than 700,000 weekly robotaxi rides globally, deployment is moving faster than the regulatory frameworks meant to govern it. With that comes an even deeper anxiety the industry rarely discusses openly - autonomous vehicles are much safer than vehicles with human drivers. Research in Traffic Injury Prevention found Waymo cut injury-causing crashes by 79%, with intersection crashes down 96%. Tesla reports Full Self-Driving (Supervised) improves U.S. road safety by over 80%.

On its face, all of this is nothing but good news. But for an industry where roughly half of all premiums are tied to auto, those numbers describe an existential shift. Fewer claims are indeed good for society, but they also represent a fundamental challenge for a business model never redesigned to reflect it.

The Transition Is the Real Challenge

The most challenging chapter is perhaps underway, in the chaotic middle ground before full autonomy becomes the norm.

Waymo's current operating model shows how messy this can be. In Austin, it has partnered with Uber, while in San Francisco it competes directly against Uber and Lyft. In both markets, it works with maintenance fleets including Hertz, Avis, and new AV service companies. Each raises different insurance questions.

Once a Waymo comes off the road and a human driver takes it in for service, there is no settled answer for what is being insured. These vehicles can be worth hundreds of thousands of dollars due to their embedded sensors and software. If a maintenance technician damages a radar unit and that vehicle later causes an accident, is the resulting liability an auto insurance issue or product liability? Current policies do not offer a clean answer.

Mixed-fleet operations carry that ambiguity: overlapping liability, unclear ownership of risk, and policy language written for a world that no longer exists. The work ahead, therefore, is a fundamental redesign of how liability gets assigned in multi-party autonomous operations. When something goes wrong, the question of responsibility, whether the OEM, the platform, the maintenance fleet, or the software provider, has no clean answer.

Data is the starting point, and fleets like Waymo and Tesla are sitting on enormous amounts of operational data that could reshape how risk is understood and priced. But that means insurers need access to that data, and the frameworks to build products around how these vehicles actually operate.

Regulators have a significant role to play, too, because the state-by-state patchwork that just about worked for rideshare will not scale for autonomous vehicles. Federal coordination on liability standards and minimum insurance requirements for AVs would give the industry a target to build against.

The Window to Get Ahead Is Narrower Than It Looks

The rideshare era offers a partial template. When Uber arrived, insurance took years to catch up, but the industry muddled through. However, the trajectory this time looks faster. Nevertheless, unlike the rideshare era, the industry already knows how to build insurance products for markets without a rulebook.

But the scale is different, the liability questions more complex, and the next major AV incident will create enormous pressure to fix things quickly, in public, under scrutiny. Waiting for that moment is the wrong strategy.

Insurance has to shift from static to dynamic, using real-time data to map how risk is distributed across platforms, fleets, maintenance partners, and technology providers. Liability has to follow that data through every link in the chain.

Adapting will not be enough, because a model that priced human behavior for a century is finished. What replaces it will look almost nothing like today's commercial auto insurance. Carriers treating this as a rebuild will define the next era of mobility risk. Everyone else will be left writing policies for a road that no longer exists.

American household budgets are facing pressure from every direction. Grocery bills remain stubbornly high. Gas prices have shot up—and face further surges as politically volatile oil-producing regions continue to roil.

Meanwhile, layoffs across technology, retail, and financial services sectors have put millions on uncertain footing—many of them "white-collar" members of the homeownership class. In response, consumers are putting every line of their monthly budget under a microscope. As families cut out food delivery and forgo or downgrade streaming services and other niceties, a four-figure annual insurance premium is no longer the kind of expense people renew reflexively.

Together, pricing pressures and income instability combine to drastically change insurance shopping behavior. This puts carriers in a race to understand—and hopefully prevent or at least forestall—what looks like a retention crisis. (It's not the first time we've been here: the post-9/11 hard market of 2001-2003 triggered a similar wave of shopping and switching as carriers raised rates sharply across nearly every line, and the mid-1980s hard market produced comparable consumer flight before conditions softened.) The carriers that "crack the code" to curb inflation through efficiency will provide needed breathing room for their customers, while creating competitive advantages with a potentially long tail.

The Numbers Reflecting a Stressed Consumer

The percentage of U.S. consumers shopping around for a new auto insurance carrier reached a record 57% in 2025, up from 49% in 2024, and about 29% switched carriers outright, according to the J.D. Power 2025 U.S. Auto Insurance study survey. Progressive CEO Tricia Griffith assertively underscored what's driving this dynamic on a 2025 earnings call: "I think it's just easier to shop. And I think with all the other inflationary items out there, people are looking to figure out a way to save money."

This is not simply a market anomaly or part of a business cycle. It's evidence of a financially stressed customer base doing exactly what financially stressed people do: seek relief wherever they can find it.

For many households, reducing insurance costs is the rare large recurring expense that responds to user effort. When a family is already shopping in-house brands at the supermarket and delaying purchases, saving several hundred dollars on an auto renewal is a meaningful win.

Carriers that recognize the emotional and financial context behind that shopping behavior (hint: it's not a simple matter of competitive comparison shopping; it's born of necessity) will approach this moment via innovation and empathy.

Raising the Ceiling by Focusing on the High-Value Customer

Not all shopping activity carries equal risk. Many consumers most actively reconsidering their policies right now also happen to be the ones with the greatest profit potential. One-third of customers shopping in 2024 were seeking auto and home insurance bundles, according to the latest J.D. Power Insurance Shopping Study. These are multi-policy, long-tenured households, precisely the customers who anchor a carrier's book.

Winning one bundled household is worth multiples of a single-line acquisition. It's why insurance brands lean so hard into bundling offers and messaging. Carriers building strategies targeting this specific segment will see outsize returns. The opportunity lies not in chasing after new customers from a depleted pool, but from reaching the ideal existing customers at precisely the moment they are open to having constructive conversations about finding economies through scaling the relationship with their insurer.

Maximizing the Value of Every Touchpoint

To do this, your playbook doesn't need to be more complex, but your tactics need to be more intentional. Research consistently demonstrates that insurers who reach out to policyholders before renewal, with plain-language explanations tied to real cost drivers, see stronger results than those who respond only after a customer complains about a rate increase.

A customer who just paid more for ground beef, gas, and a car repair is not well-positioned to absorb a renewal increase without being told why. The same customer, reached proactively with a clear explanation and a conversation about coverage options, feels "seen" rather than squeezed. That distinction drives decisions more reliably than any pricing adjustment alone.

Reaching the customer before they open a comparison tool changes the entire dynamic. It signals that their relationship with you matters, which is exactly what a financially pressured household needs to hear.

Remaking Traditional Workflows

Seventy-six percent of carriers now deploy AI in at least one underwriting or pricing function, according to industry data. The carriers positioned to win are the ones who use it thoughtfully: "how will this AI-enabled workflow help us reach our [financial performance/customer service/NPS] targets consistently?" Surprisingly, this philosophy is not as common among insurers as one would hope. Carriers that get this right understand a critical distinction: the goal is rethinking how work gets done, not how they can reduce the number of people doing it. AI doesn't replace an underwriter's judgment or an agent's relationship with their client—it removes the friction that keeps both from doing their best work. McKinsey's research on AI in insurance further underscores this point, noting that the highest-performing carriers treat AI as a workflow redesign challenge, not a headcount equation.

Use AI to flag households where a proactive coverage conversation can strengthen relationships, rather than give competitors a foot in the door. AI deployment of this sort builds an advantage that compounds over time, making every renewal a trust-building touchpoint, rather than creating potential pricing negotiation standoffs.

The Open Window

Market disruption creates winners and losers—only now this happens at, well, the speed of AI. The carriers gaining the most ground in the next three years will not be those that waited for customers to leave before responding. They will be the ones who anticipate and respond to a record-size shopping market driven by "kitchen table" financial stresses as an opportunity to demonstrate why their policy is the one worth keeping.

The carriers who view this moment as an inflection point created by decades of shifting macroeconomic factors (wage stagnation, globalization, etc.), rather than a discrete trend to watch, will look back on 2026 as the year they separated themselves from a crowded field. The real choice is not whether to compete for customers who are shopping. It is acting with intent to keep your customers while giving consumers good reason to choose you over your less responsive competitors.

International insurance broking operates across multi-actor systems without a structured method for reading the connectivity between them. Complexity becomes concrete when renewals stall, when claims escalate without warning, when regulation forces last-minute adjustments. Pressure concentrates in certain places, travels along some pathways, and dissipates in others. 

The geometry of these movements is what I call adjacency: the measure of how tightly actors are bound to one another, and how their ties carry or absorb pressure. The concept draws on network theory's insight that structure shapes behavior, and on systems thinking's recognition that interdependence produces non-linear effects. What adjacency mapping adds is an operational instrument calibrated to the specific architecture of international insurance programs, one that translates structural insight into practitioner decisions.

An international program is not a set of bilateral relationships. It is a system in which master clients, local clients, brokers, and insurers connect continuously, and in which a shift in one part alters conditions across the rest. A disputed claim at the local level can reverberate upward until it unsettles the master layer. A regulatory delay in one jurisdiction will delay the entire renewal cycle. When negotiations falter between a master broker and a local insurer, expectations unsettle across several markets simultaneously. The system propagates pressure because its ties differ in weight, consequence, and resilience.

The structure begins with the system's elements. Six actors form the state vector of any program:

Here, Smc denotes the master client, Slc the local clients, Smb the master broker, Slb the local brokers, Smi the master insurer, and Sli the local insurers. The notation names the nodes that matter. The model captures structural connectivity. It measures the presence, intensity, and resilience of operational ties, not the informal influence, cultural distance, or reputational history that also shape relationships. Understanding how the system functions requires capturing how strongly these actors are tied to one another.

The adjacency matrix A fulfils this function. It represents the interaction weights between stakeholders: each element wij indicates the presence and intensity of the relationship between stakeholder i and stakeholder j. The matrix is first constructed in abstract form, mapping the position of each interaction within the system:

The abstract form locates each relationship within the system. The subscripts identify the two stakeholders involved; the element wij denotes the weight of their tie. The purpose of this construction is to formalize the network so that the system can be analyzed as a structure rather than through accumulated observation. Once defined, weights are assigned on a 0 to 1 scale. On this scale, 0 denotes the absence of adjacency; 0.3 indicates a weak tie with limited interactivity; 0.6 represents strong adjacency with effective coordination; and 1 signals optimal alignment. High adjacency is a marker of capability: two stakeholders are tightly coupled, mutually responsive, and able to sustain efficient workflows. Low adjacency signals fragmentation and the structural risk of disconnection. The weights are practitioner judgements. Their value lies in making an assessment explicit that experience tends to leave implicit. A broker who has managed the same program for a decade carries a mental map of its connectivity. The adjacency matrix makes that map visible, comparable, and open to revision.

Construction begins with a structured assessment across all active relationships in the program. The broker assigns an initial weight to each tie by asking three questions: how often do these actors interact operationally, how reliably does information move between them, and how quickly does the tie transmit pressure when the program is under strain. These criteria are observable without measurement instruments. They are the qualities experienced brokers already assess informally. The matrix makes that assessment formal, consistent, and transferable across programs and teams.

A populated matrix takes the following form:

The matrix is a map of the system's connective capacity. A weight of 0.6 between master and local clients reflects strong alignment: headquarters and subsidiaries adjust to one another with speed. A 0.3 between master clients and master brokers indicates a weaker tie, where coordination exists but is less intensive and more susceptible to friction. A 0.2 between master clients and master insurers signals low adjacency: limited interactivity risks disconnection unless brokers actively mediate. A 0.6 between master brokers and local insurers, by contrast, marks a high-value link, one where workflow is active and system coordination is at its strongest. High adjacency marks the ties through which decisions travel, alignment is secured, and operations proceed without friction. Low adjacency marks the fracture lines where interactivity is minimal, silos form, and misalignment compounds.

Adjacency mapping derives its analytical value from the fact that connectivity is never static. Strong ties allow programs to move with speed and coherence. When master and local brokers hold a 0.6 adjacency, coordination is tight and workflow advances without resistance. When a claim escalates across a 0.6 link between local and master insurers, the system responds rapidly. Weak ties do the opposite: they isolate segments of the program, delay decisions, and erode effectiveness.

The architect's objective is to sustain ties at 0.6, the threshold at which alignment holds, coordination costs nothing, and the program moves with structural coherence.

Three patterns govern how pressure moves through the system. Concentration forms where multiple strong ties converge, typically around master brokers holding 0.6+ adjacencies with both local brokers and master insurers. These nodes become coordination hubs, capable of synchronizing decisions across jurisdictional boundaries. Propagation measures the efficiency with which decisions travel. The difference between a 0.6 and a 0.3 tie is the difference between transmission and friction. A 0.6 link between master and local insurers ensures a claim escalates without delay; a 0.3 tie ensures it stalls, and the broker must compensate manually for what the tie fails to carry. Absorption occurs at weak adjacencies of 0.3 or below, where pressure dissipates rather than transmits. Occasionally this buffers noise; more often it marks a structural disconnection that prevents system-wide coordination. These patterns do not operate independently. A weak tie between master broker and local insurer becomes more consequential when the master client to master broker tie is also degraded. Compound weakness across adjacent nodes accelerates fragmentation in ways that no single tie, read in isolation, would predict.

Because ties shift, the map must be kept current. A static diagram decays. A weak link can be reinforced into a strong adjacency by deliberate effort; a strong tie will weaken if neglected. Four events should prompt a reassessment. First, personnel change at any node, meaning the tie shifts with the person. Second, a regulatory change in any jurisdiction covered by the program. Third, a claims event that escalated beyond its expected path. Fourth, the approach of renewal, which is always a structural stress test. Each signals that the weight of at least one tie may have moved without the broker noticing. Adjacency maps are instruments that require periodic review and active maintenance. Brokers who update them see the system. Those who rely on experience alone see only what the system once was.

During renewals, adjacency maps identify which ties sustain workflow and which must be reinforced before they become bottlenecks. In claims, they reveal which relationships enable rapid escalation and which will stall it. Consider a master broker to local insurer tie that registers 0.6 in stable conditions but drops to 0.3 during renewal following personnel turnover at the local level. The map makes this degradation visible in advance. The broker can then rebuild the tie through intensified communication, workflow realignment, or deliberate relationship investment before claims season converts a weak link into a coordination failure. The same logic applies during a major claims event. A local insurer holding a 0.6 adjacency with the master insurer will escalate rapidly and with precision. One holding a 0.3 will delay, misframe, or absorb the claim at the local level, forcing the master broker to intervene manually at precisely the moment when speed matters most. The map identifies this vulnerability before the claim arrives. In regulatory matters, the map shows where connectivity must be strengthened to secure compliance. In each case, the broker acts before disruption, reinforcing the ties the system depends on rather than repairing them under pressure.

The broker who monitors adjacency, reassesses ties under pressure, and rebuilds degraded links before they become failures is sustaining program coherence. That is what rigorous servicing looks like in practice.

The central proposition of adjacency mapping is that program performance correlates with the aggregate strength of ties between its actors. The broker whose counterpart is responsive, informed, and quick to act is not simply lucky in his relationships. He is operating across a tie with high adjacency. When that tie degrades, the program follows, regardless of how well the individuals involved know each other. This is a testable claim. Brokers who map their programs over time will find that degradation in tie strength precedes operational failure, and that deliberate investment in adjacency produces measurable improvements in renewal speed, claims resolution, and regulatory compliance. Together they provide foresight into where the system is strong, where it is fragile, and where investment in interactivity will deliver the greatest return. The program, read this way, becomes a structure with legible geometry.

International insurance broking will always be exposed to uncertainty. Renewals will clash with shifting regulation, claims will appear at awkward times, and timelines will compress under pressure. But complexity is not chaos. By treating programs as systems and adjacency maps as diagnostic instruments, brokers can anticipate rather than endure, and reinforce rather than repair. Pressure still moves through the system. Adjacency maps tell you in advance where it will concentrate, where it will stall, and where it will dissipate unnoticed. In a system this complex, that is the only form of control that holds.

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As long as everyone has been telling their Ted Turner stories in the wake of his recent death, I thought I'd tell mine, before getting on to this week's business: what I see as a growing backlash among younger generations toward AI that business leaders need to contend with.

My story comes from my friend Marc (a former managing director at Marsh McLennan, as it happens). He was at the helm in a multi-day sailboat race around Long Island in the 1980s and timed the start almost perfectly. In the chaotic way that these races start, you don't know when the horn will blow, so you circle as you try to be at full speed with a clear path to the starting line when the horn sounds. Marc had succeeded — but Ted Turner was bearing down on him, aiming for the same spot on the line that Marc was going to cross. 

Marc had the right of way, but this was Ted Turner, recent winner of the America's Cup, in a much bigger, faster boat, with a world class, steely glare as he steered his boat on a collision course with Marc. 

Marc never wavered, and at the last possible moment Turner bore off, did a 360, and crossed the starting line a minute or so later. Turner won the class among the biggest boats, while Marc and his crew just did well in his class of smaller ones. Finishing late at night, he and his crewmates headed to a bar to decompress. At 1am, they were getting ready to call it a night, when the bartender set a round of drinks in front of them and said they were sent with the compliments of the gentleman at the door. The gentleman was Ted Turner. He nodded respectfully in their direction. Then he gave them the finger with both hands and stormed out.

The bartender told Marc that Turner said he'd been scouring every bar on the waterfront in search of Marc and his friends. Whatever else you want to say about Turner, the man had style.

Now on to the backlash against AI that we all should be watching. 

I use my daughters, aged 32 and 29, as my antennae about attitudes among Millennials and Gen Z, and they started bristling about AI months ago. Initially, they complained about the huge amounts of water required for cooling. If I ever mentioned using an AI for something, one of them might make a snide remark — they're given to snide remarks with their father — like, “I guess the real prompt is: ‘Hey ChatGPT, could you please drain another reservoir for me?’”

Hyperscalers' wild need for electricity for their gen AI data centers led to concerns about what AI was doing to the environment. That my daughters' electric bills were climbing didn't help matters.

More recently, they've resonated with the concerns of those facing the prospect of having data centers built near them, each spanning perhaps tens of thousands of acres. To top it all off, my older daughter lost her writing job to an AI, as I mentioned last week. The girls have told me to turn off the AI summary that Google Search now offers.

A recent New York Times article reports on a Gallup survey that found Gen Z's attitude toward AI souring, and for reasons that go well beyond the sorts of environmental concerns that initially triggered my daughters. 

"Many respondents did acknowledge that A.I. might make them more efficient in school and the workplace," the article said. "But they were concerned about how the technology would affect their creativity and critical thinking skills.

"Young adults in the work force were especially skeptical. Close to half of those surveyed said the risks of artificial intelligence outweighed its potential benefits in the workplace, an 11-point jump from the previous year. Only 15 percent said they saw A.I. as a net benefit."

The Times also reported on a viral video (that my daughters had already made sure I saw) of a woman giving a commencement speech in which she declared that "the rise of artificial intelligence is the next Industrial Revolution" — only to be roundly booed by the students. 

“'What happened?' [she] stammered, looking over her shoulder, as if searching for an escape hatch," the Times reported.

She continued: 

"'Only a few years ago, A.I. was not a factor in our lives.

"The crowd erupted in cheers.

“'And now, A.I. capabilities are in the palm of our hands.' Boooooooooo.

"One might call it a 'read the room' moment."

Eric Schmidt, former CEO of Google, got booed even harder when talking about AI in his commencement address at the University of Arizona on Friday.

I'm not saying dissatisfaction among younger generations will stop the adoption of generative AI, any more than concerns by earlier generations could stop the internet or the smartphone. I'm also not saying Millennials and Gen Z are Luddites; they're extremely sophisticated about technology. 

What I'm saying is that younger generations seem to be taking a warier approach than those of us of a certain age, who've not only been through a few technology revolutions and have accepted their inevitability but whose views are perhaps softened by what all the AI investments are doing for our retirement accounts. 

And those younger generations get a vote. The discussions among business leaders may be about use cases for AI, about how to implement AI most effectively, about how to demonstrate ROI to shareholders, and so on, but your employees are going to be doing that implementing. If a big chunk of your work force dislikes or distrusts AI, they can provide a lot of silent resistance that may surprise you if you haven't made the effort to understand their concerns and to work with your employees to address them.

Cheers,

Paul

P.S. After writing this commentary last night, I wake up today to find that I'm not the only one thinking about the AI backlash. A New York Times columnist wrote: Why College Grads Are Booing Their Commencement Speakers. The Wall Street Journal led its website with: The American Rebellion Against AI Is Gaining Steam. Their reporting/reasoning differs a bit from mine, but my conclusion remains the same: Proceed with caution. 

P.P.S. It is with great sadness that I note the passing of Stephen Applebaum at age 81. Stephen was one of the earliest and dearest friends of ITL and was generous not just with me but with everyone he met in his decades of work in the insurance industry. I looked back through the 80-some articles Stephen wrote or co-wrote for us over the years to see if I might single out a few, but there are just too many sharp insights. I will point to one, which he wrote a year ago with his business partner, Alan Demers, because it's not only very smart but because Stephen always struck me as an empathetic man: "Re(Defining Empathy in Insurance." 

Here is a link to a brief obituary, to the funeral arrangements and to a way to donate to the Dragonfly Foundation, a favorite of Stephen's that focuses on pediatric cancer care.

May his memory be a blessing.

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Finding a route through extreme uncertainty from climate tipping points is now urgent for organizations. A pragmatic mindset and proven techniques can uncover your path.

Climate systems are moving toward abrupt, irreversible shifts. These tipping points include the collapse of the Atlantic Meridional Overturning Circulation (AMOC), the system of ocean currents in the Atlantic that plays a crucial role in regulating Earth's climate by transporting heat from the tropics northward.

The AMOC is likely weakening. Should it reach a tipping point, the impacts could challenge the historical wisdom that climate change unfolds gradually. Regional conditions could now flip quickly, bringing severe cooling to northern Europe, forcing storm tracks into new positions, shifting monsoons and altering coastlines.

The most recent Nordic Tipping Week saw researchers and policymakers treat AMOC tipping as a realistic planning case. When science moves from questioning if a tipping point might happen to focusing on when, an organization's planning expectations need to change.

It is critical that organizations and their stakeholders lift themselves out of the climate catastrophizing that tipping points may prompt. That's because doom can shut down action.

Instead, it is important to view tipping points through a constructive lens that focuses on "earthshots," not "moonshots." We're not talking here of hope over expectation but deploying disciplined techniques in which risk professionals are already well-versed.

Tipping‑point‑aware tools and techniques, such as enhanced scenario planning and strategic planning with advanced risk identification, can help you replace passive dread with active preparation. Incorporating tipping points into an organization's risk management and strategic planning will also help maintain credibility with the regulators, investors and insurers we can expect to ask tougher questions on business's readiness for extreme disorder.

The impact of AMOC tipping points

Should the AMOC cross its tipping point, we could see rapid and irreversible climate shifts, including:

• Severe winter cooling across northern Europe
• More intense storms and altered storm track behavior
• Long-term agricultural disruption
• Major changes in water and food availability.

Given measured weakening and converging scientific warnings, the most prudent approach for businesses, particularly those with UK or European exposure, is to incorporate the possibility of AMOC weakening into their risk register and scenario testing. The time has come to view AMOC tipping as a credible tail risk to which businesses need to prepare.

When a system approaches a tipping point, a business should prize preparedness over precision. An organization's role here isn't about beating scientists to pinpoint the exact moment of a shift but strengthening its ability to remain stable when uncertainty accelerates.

Organizations should start by identifying those areas where they are most dependent on climate stability; think agricultural inputs, logistics routes or water availability. How would abrupt cooling, extreme storms or rainfall shifts put pressure on the most climate-reliant nodes of their operations and supply chains?

Enhanced scenario planning for effective adaptation

Organizations can no longer assume risk mitigation will keep climate risk within more familiar limits. Ice melt, freshwater dilution in the North Atlantic, and shifts in rainfall belts are already building momentum, with some impacts locked in for decades. That means businesses should consider tipping-point-aware adaptation as part of their strategic decision‑making, calling on scenarios that extend beyond traditional pathways.

Severe-but-plausible scenarios, such as sudden cooling in Europe or major shifts in precipitation zones, will provide a clearer understanding of the future operating environment. Updated scenarios should be able to test the full chain of consequences, rather than the most familiar ones, investigating how physical risks and supplier reliability might change under tipping‑point conditions.

These scenarios may well feel uncomfortable, but they are also far from improbable.

My older daughter just lost a writing job to an AI (that she had to train to replace her), so I don't currently have the kindest thoughts about where AI is headed, but the technology is going to keep barreling forward whether we like it or not, and we all have to adapt.

So let's take a look at the smartest pieces I've seen recently about where generative AI is headed. We'll look at the "fog of AI," which is making it so very hard to make investment decisions. We'll look at the insurance industry's quandary about how to handle all the data centers being built (maybe). We'll look at lessons learned from early attempts at scaling AI, to see what separates the winners from the losers. 

But let's start with a piece that contradicts the conventional wisdom that AI agents should be treated as employees.

An article in Harvard Business Review says: 

"Leaders assume that anthropomorphizing AI will make the technology feel less foreign to workers or that it will signal the company’s AI ambitions to investors, customers, or internal stakeholders. But it turns out that treating AI as an employee is not so straightforward. 

"In a randomized experiment, we found that humanizing AI can shift accountability away from individuals, increase escalation, reduce review quality, and erode professional identity and trust. What’s more, it doesn’t meaningfully increase people’s intent to adopt the technology and integrate it into workflows—which remain the key obstacle to capturing AI’s enormous value creation promise."

The most striking findings to me were that AIs treated as an employee, rather than a tool, were more likely to lead to humans sloughing off responsibility for any problems that occurred and to more often asking their managers for additional review. The article doesn't argue for slowing down implementation of AI, by any means, but does make a case for changing how many of us think about describing their role.

Another HBR article, titled "The Future Is Shrouded in an AI Fog," offers some comfort for those of us confused about how to proceed with implementing AI. The piece says we pretty much have to be paralyzed by indecision because of the "extreme opacity" about the future of AI:

"Given all the things that might change because of AI, it feels like a fog has descended that occludes our ability to see the future. And right now, that’s its most important—and perhaps most underappreciated—economic effect.... This extreme uncertainty challenges the criteria we use to commit to forward-looking investments."

The opacity doesn't just affect businesses, either. It also hits us as individuals. The article asks, for instance, why smart kids would want to spend a decade training to be a doctor when it's not clear what being a doctor will mean in the age of AI.

Again, self-pity isn't allowed, at least not for very long. The article lays out an approach designed to help us sense change sooner and react with more agility, then tells us to get on it.

Mick Moloney of Oliver Wyman articulates a question I've heard lots of insurance executives pondering lately: How should insurers handle the hundreds of billions of dollars of data centers being built to accommodate the AI rush?

As Mick puts it:

"The six largest AI data center projects currently under construction or formally committed in the United States represent a combined investment of over $120 billion and a combined power capacity target of more than 10 gigawatts — deployed not over decades, as comparable infrastructure has always been, but over three to five years. They are being built by technology companies, AI laboratories, and private equity platforms that have never operated infrastructure at this scale. And they are being financed with instruments that did not exist eighteen months ago."

He doesn't have a silver bullet, but he does offer keen insights into how insurers should think about these six projects based on their power strategy, their financing structures and the risk management capabilities (or, more likely, the lack thereof) of the builders.

The insurance industry will be wrestling with the data center issue for years, but Mick's piece is a good start.

Finally, McKinsey published "The AI Transformation Manifesto," with a dozen observations about what separates the winners from the losers in the age of AI. For instance:

• Technology alone doesn’t create advantage; enduring capabilities do. Who are the early winners at AI? The same companies that have been winning before by building capabilities that allow them to harness any technology effectively.... When these new capabilities are built—and they take time to build—the company accelerates its business transformation with technology and outperforms its peers. The capabilities become the competitive advantage....
• Economic leverage points are your best focal points. Any business model has a few key economic leverage points that provide the biggest impact when improved with AI. In mining, for example, process yield and throughput is a key economic leverage point, and that’s where Freeport-McMoRan achieved game-changing impact. In automotive, supply chain integration is a key leverage point, and that’s where Toyota had its AI breakthrough. Most companies have long lists of use cases. Successful ones focus on achieving deep business transformation in the few areas that matter strategically. That’s where they double down to build AI systems....
• Building the tech and AI muscle of your senior business leaders should be a top priority. We don’t have a single success story where senior business leaders were not in the driver’s seat. IT leaders can support the transformation, of course, but it’s business leaders who need to drive it.

Again, I don't see a silver bullet, but we're learning....

Cheers,

Paul

Budgets have grown, pilots have multiplied, and AI is now a fixture in virtually every P&C strategic plan. Yet 42% of insurers track no AI metrics at all, which means they have no way to validate what works, no playbook to scale it, and no mechanism to stop what doesn't work. Insurers' investment pattern confirms that this is an organizational constraint, rather than a technology one: on average, 72% of AI spending goes to technology and only 28% to change management.

Technology creates capability. But change management determines whether that capability becomes performance. That imbalance is the first signal of what Capgemini identifies in the 19th edition of its 2026 World Property and Casualty Insurance Report as an "architecture mismatch." This is a structural gap that runs deeper than the technology stack, and that no amount of additional AI investment will close on its own.

Three dimensions, one ceiling

The first dimension is a strategy and talent gap. Among the top 20 global P&C insurers, only 35% have explicitly linked their AI strategy to business outcomes beyond efficiency. That narrow framing has consequences: Strategy tends to direct investment toward quick wins rather than the capabilities AI needs to grow over time. In most cases, the result is an incomplete strategy that optimizes the present while leaving the future underbuilt.

The second dimension is technical constraints. Legacy architectures fragment data across functions, making it harder for AI to reason across underwriting judgments, claims assessments, and distribution decisions that depend on context-rich, unstructured information. The barrier is less about the AI itself and more about the environment it must operate in – one that was not designed with AI in mind and does not easily accommodate it.

The third – and arguably most decisive – dimension is organizational. Over half (55%) of insurers cite unclear ownership of AI initiatives as a key constraint. Without clear accountability, programs stay dependent on individual champions rather than building institutional capability. And despite all the work underway, 47% of employees report no meaningful change in their day-to-day work after 18 months of using AI. That points less to a deployment failure than a design flaw.

The problem with fixing one thing at a time

These three dimensions are entangled, which is precisely what makes the conventional response insufficient. Assess, prioritize, sequence: Fix strategy first, then technology, then organization. In practice, addressing one while leaving the others untouched tends to limit progress, rather than unlock it.

Our research identifies the emergence of intelligence trailblazers – the top 10% of P&C insurers – who treat AI as a core operating capability rather than a program to be managed, aligning strategy, technology, and organizational adoption in tandem. Over three years, trailblazers have achieved 21% higher revenue growth and 51% greater share price increases compared with the rest of the industry.

Despite their growth, this group has also not fully solved the problem. AI still largely operates at the task level, workflows remain built for human execution, and the organizational model that closes those gaps – one where human expertise and synthetic execution are deliberately organized around where each creates the most value – is still being built. The opportunity to redesign is real. But it remains an opportunity, not yet an achievement, even for those furthest ahead.

The harder conversation

An uncomfortable question to raise is why this is so difficult to change, even for organizations that understand the problem.

The answer is that the architecture mismatch was not built through bad decisions. Legacy systems were the right investment at the time. Prioritizing technology over change management made sense when AI was unproven, and the organizational implications were unclear. It is not evidence of poor judgment, but the accumulated consequence of individually rational choices made in a different context.

Moving forward requires asking a more challenging question: Do the investments already made, and the ones being considered now, still pay back on the original terms? Most organizations haven't asked that question systematically, because who defines success, who is accountable for outcomes, and how progress is measured beyond deployment were all designed for a time when decisions were quintessentially human. And until that question gets asked, the architecture underneath the pilots stays unchanged – regardless of how many new tools are deployed on top of it.

Trailblazers are not ahead because they have solved the problem or because they run better pilots. They are ahead because they made a different decision earlier: to address the architecture underneath the pilots, not just the pilots in isolation. The next decision is harder: to redesign the organization itself. That decision has not yet been fully made by anyone. But the insurers who make it first will define what competitive advantage looks like in the intelligence era.