Conning maintains a stable outlook for U.S. states in 2026, reflecting continued balance sheet strength and generally prudent fiscal management across, even as operating conditions become more challenging.

South Dakota rose 17 places to first overall, while Utah maintained its second place position. Tennessee took third place over North Carolina this year, while Texas moved up six places to fifth overall. These states benefit from sustained in-migration, competitive cost of living profiles, and comparatively strong balance sheets on the liability side, supporting greater fiscal flexibility. South Dakota additionally benefited from strong personal income, GDP, and house price index (HPI) growth.

Fundamentals continue to favor the Plains, Mountain West, and parts of the South, where our rankings benefit from population inflows, diversified economic growth, comparatively low cost structures, and strong balance sheets. Top states are outperforming due to a combination of disciplined fiscal management, resilient labor markets, and favorable demographic trends.

By contrast, performance is more mixed in the Northeast and parts of the West Coast, where higher costs, slower population growth, and other structural pressures weigh on relative rankings. The lowest‑ranked states are similarly clustered among those facing persistent demographic, economic, and fiscal constraints, including weak population trends, elevated cost burdens, and limited fiscal flexibility.

While year-to-year movement occurs, the bottom tier continues to reflect longer-term structural pressures or cyclical weakness. West Virginia fell three spots to 50th overall. Maryland (49th), California (48th), and Rhode Island (47th) each experienced declines of 27, 17, and 21 places, respectively. Louisiana improved modestly to 46th place, up four spots in this year's report.

Ranking movements were more pronounced in 2026 than in our 2025 report. A total of 23 states moved 10 or more positions year‑over‑year (YoY), compared with 21 last year, and 11 states saw a shift of more than 20 positions versus just six in the prior period, despite no changes to our methodology.

Our framework, updated last year, incorporates cost of living and catastrophe losses per capita to better capture affordability pressures and climate-related risks. The wider dispersion observed in 2026 reflects the fading of the post‑pandemic period of broadly strong performance, as revenue growth moderates, migration slows, and widening differences in cost structures and balance sheet positioning translate more directly into relative ranking outcomes.

For the full report from Conning, click here.

With major companies such as Meta and Amazon announcing huge layoffs that they tie to the efficiencies AI is making possible, you might be thinking that you, too, should be looking to cut head count. 

Maybe belay that thought, at least for the time being.

Many of the layoffs actually stem from a whole series of factors, even if companies choose to use AI as the blanket explanation. Meanwhile, economic data suggests that AI has not led to widespread job cuts, and companies are increasingly reporting great successes by using AI to make employees' jobs easier and to enhance customer service.

Insurance, as the ultimate digital industry, stands to benefit from generative AI's efficiencies as much as any, but let's look at what's realistic and what isn't.

Elon Musk said earlier this year that AI will take on so much work that in 10 to 20 years jobs will be optional. If you don't want to work, you can just sit at home and let the AI and the money it generates take care of you. But Musk says a lot of things, including that he would land humans on Mars by 2025 and have them start building a colony for 1 million people and that he would have 1 million fully autonomous robotaxis on the road in 2020. (The total currently stands at two or three dozen.) 

Meanwhile, OpenAI CEO Sam Altman has retracted his claim that generative AI will massively reduce the number of entry-level jobs, and Anthropic CEO Dario Amodei has withdrawn his prediction that AI would eliminate 50% of white-collar jobs. Amodei now says, “If you automate 90% of the job, then everyone does the 10% of the job. And the 10% kind of expands to be 100% of what people do and kind of 10-times their productivity.”

In "A Reality Check on the AI Jobs Hysteria," the MIT Technology Review reports that "there’s scant evidence that AI has yet had any large-scale impact on the US labor market. Analysis of the data gathered for the US Bureau of Labor Statistics (BLS) shows that the unemployment rate for the jobs potentially most affected by AI is actually lower than that for occupations less exposed to the technology. And, critically in the mind of economists, there are no signs that large numbers of people are shifting from jobs threatened by AI to supposedly safer ones, such as those involving mostly manual labor."

Why, then, are so many companies announcing job cuts related to efficiencies they're achieving with AI? In my experience, companies will always glom on to a convenient excuse when announcing bad news. Even a flimsy excuse is better than saying, "We screwed up." 

But many companies did screw up. Lots hired too freely as the economy rebounded post-COVID. Some are just poorly managed, because there are always companies that are poorly managed. 

An article in the New York Times, "Is AI Replacing Tech Workers or Providing an Excuse for Job Cuts?", provides any number of examples of companies using AI as a cover story for layoffs. Meta is my favorite. As a columnist in the Times put it, "From 2021 to 2026, [CEO Mark Zuckerberg] poured $80 billion into the Metaverse in the firm belief that we would all want to don headsets and hang out in a virtual world populated by legless avatars." That flop [predicted here, in 2021, I note immodestly] is why he's having to cut 10% of his work force — but he hopes to save a little face by citing AI.

None of this is to say generative AI isn't have a massive impact. It is. Legendary venture capitalist John Doerr recently told the Wall Street Journal that AI is "underhyped." Doerr, 74 years old, said AI is the biggest of all the tech tsunamis he's seen in his long career.

The gains are, at least as of now, showing up in improvements in efficiency and service — and even in new types of jobs. 

Box, which makes software for storing and managing data, created 13 new kinds of roles, with titles such as AI architect, AI solutions manager and AI platform leader. The New York Times reports: "With the proliferation of these positions, Box expects to have more than 3,000 employees by early next year, up from 2,900 at the start of this year."

Schneider Electric made its call centers and manufacturing facilities more productive, without eliminating workers. Costco, Delta and IBM did much the same.

Insurers are already seeing major opportunities for efficiencies in gathering documents and triaging cases for claims representatives and underwriters, and companies are moving toward AIs that provide recommendations and can act as agents, or at least produce drafts of communications and reports for humans to review. Insurers are seeing great opportunities for AI to handle routine inquiries from customers and to provide at least rudimentary service during off hours. And much more.

Insurers should continue to pursue all available opportunities and think big about what breakthroughs might be out there via AI but, at least for now, should focus on removing the burdens on employees and enhancing customer service and not on reducing head count.

Cheers,

Paul

P.S. After writing this commentary last night, I woke this morning to find that the New York Times had done a long analysis of all the promises Musk has made over the years and failed to meet, including the ones I mentioned about colonizing Mars and filling our streets with his robotaxis. They counted 602 goals he set and found he achieved them less than a fifth of the time.

P.P.S. Finally, here's a wild AI story from the You Can't Make This Stuff Up Department: "Book on Truth in the Age of A.I. Contains Quotes Made Up by A.I."

FROM THE EDITOR

Telematics and Trust: The UBI Revolution by Henry Kowal, Matteo Carbone Adoption of auto telematics has increased 28% a year in the U.S. since 2018. Usage-based insurance is no longer a niche. It is a mainstream strategy. Read More Key IoT Trends in 2026 by Marina Zudina From AI-based IoT to digital twins, five transformative technologies are making IoT deployments smarter, faster, and more secure in 2026. Read More

Why Prevention Is the New Protection by Daniel Grimwood-Bird Rather than inferring exposure solely from historical outcomes, commercial auto underwriters can now access leading indicators of attentiveness, distraction, and behavioral discipline. Read More Can Insurtech Fix Homeowners Insurance? by Matteo Carbone Even a 20% gain in operational efficiency doesn't move the needle enough. The real opportunity is in "connect and protect. Read More

Telematics Drives Shift in Commercial Insurance by Shammi Thakur Commercial insurance is evolving from reactive risk transfer to continuous prevention through real-time telematics and behavioral data. Read More Insurance Risks Being Left Behind by Dominique Roudaut Widening protection gaps demand insurers transform from risk transfer to resilience providers or face existential decline. Read More

Paul Carroll

I’m often intrigued by how far back the roots of innovation go. For instance, we know that automobiles trace back to at least the 1770s, because the first auto accident was recorded in 1771; a Frenchman hooked a steam engine to a cart and crashed into a wall. 

As for the Internet of Things, our ITL Focus topic for this month: Some students at Carnegie Mellon rigged a vending machine in 1982 so they could monitor whether it had sodas available and whether they were cold. In 1993, students at the University of Cambridge pointed a camera at a coffee pot and got it to send a live feed to their computers so they could see if coffee was available without having to walk over to the coffee station.

Those IoT examples say something about how clever students can be about being lazy, but I’m more interested in what they say about adoption curves. The IoT is avant garde these days, yet its roots trace back more than four decades.

You’ve become the poster child for the Predict & Prevent model for insurers, based on your innovations with the IoT that led to the Ting sensor that plugs into a wall outlet and detects electrical problems. You’ve done the research and shown that you can prevent so many fires that dozens of major carriers are giving Ting to customers at no cost. But you still face headwinds.

What can you tell us about the headwinds that IoT innovation faces and about how you’re overcoming them?

Bob Marshall

I would probably qualify the notion of headwinds or challenges. We're distributing roughly 50,000 Ting sensors a month, and by many measures that would be considered extraordinarily successful.

We want to go faster—not just for business reasons but because we're trying to have an impact on homes, families, communities, and society. If we can get Ting into tens of millions of homes, we will be able to help prevent electrical fires, protect families and prevent loss at an even greater scale. Still, we’re approaching 1.4 million homes with Ting today. 

What's driven the success, I would say, is the clarity of the mission. Customers worry about fire. They don't want their house and their family to ever have to deal with a fire. When we can communicate Ting’s value proposition to the homeowner, they adopt it enthusiastically.

The challenge when it comes to Ting is that it's a new category. It's a device and a service that nobody's ever had before. 

People recognize that you need multiple smoke detectors in your house, you've got to replace the batteries, and every 10 years you're supposed to replace the smoke detectors. But when we first describe Ting to a customer, it’s not something they know they need. So we have to build that awareness.

When we do a brand awareness survey of the U.S. population and ask if they're familiar with Ting, this thing that can prevent electrical fires in your home, less than 2% of people are going to say they're aware of it.

This year, we're going to make a pretty substantial investment to build brand awareness of Ting. When one of our carrier partners sends an email campaign to their homeowners asking them to get Ting, we want people to be more likely to say, "Oh yeah, I've heard of that, and I want it."  

Paul Carroll

Simplicity is a significant advantage for Whisker Labs. I’ve seen lots of innovations fail because companies assumed that the benefits they delivered were so great that consumers would be willing to jump through a few hoops. They almost never will. But when I got my Ting, I just plugged it into the wall and scanned a QR code to download the Ting app and enable notifications if you ever detect problems. Took me maybe a minute… and I’m technologically challenged.

Bob Marshall

100%. We've been focused on that from day one. The connected home and IoT became very significant about 15 years ago, but a lot of the early IoT sensors required too many devices, too many batteries, and the apps and UX were clunky. Anything that is not simple for the homeowner is not going to scale. It just won't.

If you go back 10 years or so, even if somebody signed up for an IoT device, only maybe 30% of people installed it. The economics were broken. Somebody paid for all that hardware to get shipped, and 70% of devices never got plugged in, so there's little loss prevention.

Batteries are an issue, too. If you power a device with a battery, you have to replace it after a few years. Are people going to remember to replace the battery? If they don't, the economics get all wrecked. We paid for getting hardware in, and the service has stopped.

By contrast, 85% to 90% of Ting sensors get installed, and there's no battery. The sensor will last 15 years or longer without needing anything to be replaced.

Paul Carroll

Where did you get the idea for Whisker Labs?

Bob Marshall

The impetus for the idea came from Earth Networks, where I was a cofounder. We were an IoT sensor company deploying weather and climate sensors, connecting them to the internet, and collecting massive amounts of data. We provided that data to NASA, NOAA, utilities, and insurance companies. We had a lightning detection network.

Then my sister-in-law's house burned down in 2015. They lost their entire house and a pet to an electrical fire. I didn't know anything about electrical fires at the time, but when you research them you learn they start from loose connections and damaged wires that arc and spark.

Lightning is essentially a big spark in the sky, and we had really sophisticated sensors that could measure every lightning strike on the planet. So I challenged our chief scientist, chief technology officer, and lead engineer: Why can't we take that global lightning detection technology, miniaturize it, and detect these tiny sparks that cause home fires?

We started work in 2016. It took us the better part of two years, incubated as a skunkworks project inside the old company. We incorporated Whisker Labs in September 2017 after we'd figured out a technical solution.

This is a life safety product, so it had to be completely proven. We did substantial testing in thousands and thousands of homes before we made Ting available to the public in early 2020.

Paul Carroll 

There’s a seminal book, “Crossing the Chasm,” by Geoffrey Moore, that is still much-read in Silicon Valley even though it came out in the early 1990s. It notes that lots of companies attract early adopters who are fans, maybe even fanatics, but never cross the chasm to a mass market. When you thought about crossing the chasm, did you target insurance companies, go direct to consumers, or do both?

Bob Marshall

Insurance only. There have been a couple of successes that took the direct-to-consumer approach—Nest and Ring—but then you've probably got 100 companies that failed. It takes too much money to market a new product to consumers and get the widespread adoption needed to make the business work.

So we elected to go insurance first, particularly given that fire is an important issue for carriers. It's a big loss category.

Obviously, carriers wouldn't deploy the Ting technology until they had seen full testing of it. So we deployed with a number of insurance companies. We went to employees. We went to agents. We deployed to their labs, and we did a couple years of extensive testing before our insurance carriers would offer it to their actual customers.

Paul Carroll

You've now commissioned research that demonstrates that the savings from fire prevention are significantly greater than the cost of deploying Ting. But in the early days, how did you move beyond test projects with insurance companies?

Bob Marshall

Insurance companies are obviously very conservative by nature, and actuaries understandably want comprehensive historical data before they're comfortable endorsing new technologies. So how do you get there? We were fortunate to have partnerships in place with people who believed in what we were trying to do and the mission. They were very much committed to the mindset of moving toward Predict & Prevent, and they were willing to make the investments to make that happen.

Now we've got ample data to document the performance of the technology. We know that customers love it. We know that customers stay with their insurance carriers longer when they're provided Ting. We cobrand the experience, and we know engagement is high. So we check all the boxes.

And the way we've structured our partnerships—I think this is super important—is something that wasn't done in the earliest days of IoT in insurance. Early on, the business models weren’t aligned. Companies selling prevention technology to insurance carriers were satisfied with selling the hardware. If you got an insurance carrier to buy 100,000 water sensors, that was a big win. The sellers didn't necessarily care whether customers plugged them in or not or prevented any loss.

Our partnership model is solely focused on the service, as opposed to the hardware. We only get paid if the sensors are plugged in and providing the service. It's on us and the partnership to make sure customers are plugging in Ting, it's staying online, it's preventing fires and doing good. 

It's critical to make sure the interests of all parties in our partnership are aligned.

Paul Carroll

How do you get from 1.3 million or 1.4 million homes to the tens of millions you want to reach?

Bob Marshall

We launched the product commercially in March 2020, which was terrible timing, as COVID shut everything down for a year or two, but progress is now pretty steady.

Nationwide is a good example. They were already at 80,000 homes and recently committed to putting Ting in 500,000. That's a sizable percentage of their home book. And that's because they know it works, they know it prevents losses, and their customers love it.

We’re working with almost 40 carriers today, and they probably cover 60 million to 70 million homes in the U.S. If we got every one of our insurance carriers to put Ting in just 25% of their book, that’s at least 15 million homes.

That's why we're investing in marketing to build brand awareness and make it easier for carriers to get their customers to adopt Ting. We're working on incentive programs to engage agents so it's not just the corporate part of the carrier motivated to get it out.

Paul Carroll

Thanks, Bob. I always feel better after we talk.

About Bob Marshall

Robert Marshall is the founder and CEO of Whisker Labs. Whisker Labs, a spinout of Earth Networks, delivers next-generation home energy intelligence technology to realize the full potential of the connected home.

In 1992, Marshall co-founded AWS Convergence Technologies, the company that would become Earth Networks, by pioneering the networking of weather sensors and cameras using the internet. By developing groundbreaking technology to find "signals" — valuable, meaningful intelligence — in big-data "noise," Marshall improves people's lives and protects their livelihoods. 

He has appeared on CNN, BBC World News and ABC Nightly News and has been quoted in major news outlets that include the New York Times, the Washington Post, Nature and Scientific American.

Insurance has never lacked ambition when it comes to modernization. Most carriers recognize the pressures reshaping the industry. Risk is becoming more volatile, customers expect faster and more personalized experiences, and legacy operating models are making it harder to respond at the pace the market now demands. AI gives insurers an opportunity to close that gap by improving how decisions are made across the insurance journey. The challenge is to turn that intelligence into governed action fast enough to make a difference.

Across climate-exposed regions, carriers are reassessing where they write business, how they renew policies, and what levels of catastrophe exposure they can responsibly carry. In cyber insurance, threat vectors evolve faster than historical loss experience can reliably inform pricing and underwriting. Litigation trends, inflation, geopolitical disruption, supply chain instability, and specialty market complexity are all changing portfolio dynamics in ways that affect pricing adequacy, underwriting appetite, claims severity, capital allocation, and customer behavior at the same time.

In my opinion, the issue is no longer whether insurers recognize the need to adapt. The real challenge is whether their operating models can convert signals, models, rules, and human judgment into production decisions quickly enough to keep up with these changing conditions.

Risk is outrunning episodic decision cycles

Insurance operating models were largely built around periodic adjustment. Rate changes, underwriting rule updates, product modifications, compliance reviews, and distribution decisions often move through sequential processes. Those processes were rational in a market where risk signals developed more slowly and decision cycles could afford to be measured in months, but that environment is fading.

When market conditions shift faster than execution cycles, the consequences become real. Delayed rate action can weaken pricing discipline and expose margin before carriers fully see adverse selection building inside the book. Slow underwriting appetite changes create another form of exposure, especially when business continues to be written against assumptions that no longer reflect the carrier's strategy. Even customer signals lose value when they remain disconnected from pricing, product, and retention logic, leaving profitable relationships exposed.

Legacy systems are part of this tension, although they are not the villain. Policy administration systems, claims platforms, billing systems, and rating infrastructure remain essential systems of record. The problem is that many are being asked to support adaptive decision making work they were never designed to handle. Systems built to store, administer, and transact are now being pushed to sense, decide, govern, and adapt continuously.

The bottleneck is not the model

Boards and executive teams are investing in AI for good reasons. AI can accelerate analysis, automate repetitive tasks, improve modeling precision, and help teams process more complex data than traditional workflows allow. Working with customers, I see why that investment makes sense. The industry needs more speed, more precision, and better use of scarce expertise.

Yet many AI initiatives lose momentum once they move beyond experimentation. A pricing model can sharpen analytical precision without making the enterprise more adaptive if underwriting still moves through disconnected workflows, claims signals never reach product and portfolio decisions, and customer engagement tools improve outreach without connecting to the logic that determines risk, profitability, and retention.

The issue is not just model performance, but the ability to connect data, models, business rules, workflows, governance, and human oversight so AI can support real underwriting, pricing, claims, and customer decisions in production.

Insurance decisions carry financial, regulatory, and social consequences. They must be explainable, auditable, repeatable, and aligned with underwriting discipline and capital management. Horizontal AI tools can improve productivity, but insurance-grade decision making requires domain depth, governance, and operational context from the start.

Decision making needs an operating layer

Many insurers have made real progress inside individual functions, especially in pricing, underwriting, and claims. The problem is that local improvement does not automatically create enterprise agility. A stronger pricing model has limited strategic value if underwriting cannot act on the same intelligence, claims signals do not inform portfolio decisions, and customer engagement remains disconnected from risk and profitability. The deeper issue is not whether intelligence exists inside the business, but whether it can move across the business in time to change the outcome.

Insurers need governed decision making to work above and across existing systems. That layer should allow carriers to preserve operational stability while enabling intelligence to move across pricing, underwriting, claims, compliance, distribution, and customer engagement.

The aim is to reduce the distance between insight and action, giving carriers a more consistent way to test changes, understand likely impacts, govern approvals, deploy updates, and monitor outcomes as AI moves from experimentation to operational capability.

Governance makes speed deployable

Speed only strengthens resilience when it is matched by control. In insurance, faster decisions only create value when they remain explainable, auditable, and aligned with regulatory and business discipline.

This is where governance becomes a deployment advantage. Carriers that cannot explain how decisions are made will struggle to scale AI into production. Teams may trust a model in a pilot environment, but production use requires traceability, bias monitoring, approval workflows, performance monitoring, and clear human accountability.

That does not mean slowing the business down. It means building guardrails into the way intelligence operates. Pricing optimization, underwriting evaluation, portfolio steering, compliance validation, claims triage, and customer retention each require the right form of AI, the right level of automation, and the right degree of human involvement.

The New Operating Discipline for Insurance

Insurers need a new operating standard: one that connects intelligence across the policy lifecycle and gives carriers the speed, adaptability, and control to respond as conditions change.

The next phase of insurance transformation is as much about operating design as it is about AI. AI creates value when it is embedded deeply enough into the business to support faster, more disciplined, and more accountable decisions. That gives carriers a better way to recalibrate pricing, refine underwriting appetite, identify portfolio drift, support compliance, and respond to customer signals before opportunities or exposures have already moved.

AI capability alone will not close the insurance execution gap. The real advantage will belong to carriers that can make intelligence operational, connecting models, data, workflows, rules, and governance into decisions that keep protection available, profitable, and resilient.

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

Across many wine-producing regions, wildfire seasons are increasingly overlapping with key points in the growing and harvest cycle, creating a broader category of risk for wineries and their insurers. A vineyard or winery may avoid direct fire damage and still face critical economic loss if smoke exposure affects grape quality during harvest operations or while harvested fruit is awaiting processing. As a result, smoke exposure is becoming a growing risk issue not only for viticulture and winemaking teams but also for insurance brokers, adjusters, and coverage counsel evaluating when a claimed loss occurred and how the condition of the product can be established.

Wildfire season often overlaps with harvest, which can create a heightened risk of smoke exposure for wineries. When smoke taint is suspected after grapes are picked but before fermentation, first-party property coverage questions tend to revolve around timing, documentation, and the quality of the testing record as much as they do the winemaking science. Wineries can put themselves in a stronger position by keeping their focus on objective facts that show when the fruit was harvested, how it was handled, and what the available data indicates about the condition of the product.

One common issue that often leads to disagreements between a winery and its property insurer is whether smoke taint occurred. A winery may believe smoke exposure or absorption took place after harvest during transport, staging, or short-term storage, while the insurer may contend the impact occurred while grapes were still on the vine. This distinction can matter under many property policies because coverage for growing crops and harvested stock can be treated differently depending on the policy language. The most practical way to reduce timing disputes is to treat post-harvest handling like a chain of custody process and maintain clear records that match how the fruit moved through the winery's system.

Helpful documentation typically includes harvest dates, vineyard block identifiers, bin or tote identifiers, weigh tags, receiving logs, transport routes and times, staging locations and conditions, crush and press schedules, and tank or barrel assignments by lot. Notes about observable conditions, such as smoke density, odors, or ash deposition, can also provide useful context when paired with the operational timeline. The goal is not to turn harvest into a dispute, but to present a coherent lot-specific narrative that reflects the winery's real-world handling and supports a clear understanding of when the condition likely developed.

Another recurring issue is an insurer's position that testing does not support taint. Differences can arise based on what was tested, when it was tested, how representative the samples were, and how results were interpreted in light of varietal and site conditions. Wineries can help by using reputable laboratories, documenting sampling methods, preserving samples where feasible, and combining analytical results with consistent sensory evaluation and controlled comparisons across lots. When results are mixed or early results are non-detect, follow-up testing at later stages may be appropriate because smoke impacts can evolve during fermentation and aging.

In the United States, smoke taint testing historically leaned heavily on guaiacol as a primary marker associated with smoky and ashy aromas. Today, many wineries are increasingly focused on panels that include phenols and, in particular, phenolic glycosides, because smoke compounds can be present in a bound form that may not show up clearly in early volatile testing and may become more apparent later as the wine develops. Another practical reason to avoid relying on guaiacol alone is that it can appear for reasons unrelated to wildfire smoke in certain production contexts, which can complicate interpretation. For wineries, a balanced and practical approach is staged testing tied to how the fruit and lots are handled: test representative samples by block and lot at harvest or receiving, consider additional testing after pressing and during or after fermentation when warranted, and keep the testing record aligned with lot segregation and production decisions. That combination of good science and good records helps the winery make better operational choices and, if an insurance claim arises, supports a clear and fair discussion of what the data shows.

Considering everything above, smoke taint claims are easier to evaluate and resolve when the winery can present a clear timeline, a consistent testing plan, and organized lot-level records that connect the science to real operational decisions. By documenting post-harvest handling with a heightened level of care and by using staged testing that includes both traditional markers and phenolic glycosides when appropriate, wineries can create a straightforward record for any coverage discussion.

Every few years, the insurance industry watches a new distribution channel open up and takes too long to walk through it.

It happened with auto telematics. It happened with embedded travel coverage. It happened, most painfully, with cyber — a line we hesitated on for a decade while insurtechs and specialist managing general agents built the playbook we now have to license back.

A similar window is opening right now. And almost no one in traditional insurance is talking about it.

OpenAI confirmed 50 million paying subscribers across all tiers in its April 2026 announcement. Anthropic confirmed that paid subscriptions to its Claude AI platform more than doubled in 2026. Google's Gemini is scaling through Android, Workspace, and Search. Microsoft Copilot is being purchased seat-by-seat across enterprises of every size. Add Perplexity, Grok, Mistral, and a long tail of specialist AI tools, and you have something the insurance industry has not seen in a generation: a brand-new category of paying users — most of them business users — being created at unprecedented speed.

The question I keep coming back to is simple. These users are taking on real, novel professional and digital exposure every time they use these tools. Who is going to insure them, and how?

I think the answer is embedded insurance — sold at the same moment they click "Subscribe to Pro."

Why This Moment Is Different

Embedded insurance is not a new idea. We've discussed it for years in the context of auto OEMs, travel platforms, and e-commerce. What's different about AI platforms is the intensity of the exposure being created relative to the price of the underlying product.

A small business owner who subscribes to ChatGPT Plus or Claude Pro and uses it to draft client deliverables, write production code, advise customers, or build autonomous agents is generating a brand-new risk surface every day — one that no existing policy was priced for.

The industry has already started reacting defensively, by introducing new AI-related exclusions.

But that standalone market is being built the traditional way — broker-led, application-heavy, aimed at mid-market and up. Meanwhile, the actual users of AI tools — millions of freelancers, consultants, small firms, and individual professionals — are buying their subscription in 30 seconds and getting straight to work. They will never call a broker. But they would absolutely tick a box for $10–$15 a month that protects them against the very tool they are using.

That is the embedded insurance opportunity.

What an Embedded AI Coverage Could Look Like

Imagine a world where:

• A user upgrading to a paid AI plan sees a single optional add-on: AI Use Protection.
• For an individual professional, the coverage bundles AI errors and omissions, cyber and privacy protection, deepfake and reputational harm response, and IP infringement defense.
• For a small business, the same product scales up by seat, with broader limits and incident response services.
• For an enterprise, the embedded layer feeds into an existing master policy with usage-based premium adjustments at renewal.
• Underwriting signals come directly from the platform: account type, industry, usage volume, integrations enabled, agent autonomy level, and governance controls.
• Pricing, binding, and endorsement happen instantly, through the same checkout flow as the subscription itself.

This is not a futuristic sketch. The pieces already exist. With AI-driven underwriting and instant pricing, carriers can now confidently offer coverage in context — at the point of need, and for the duration required. What is missing is the partnership — a carrier or insurtech sitting down with a foundation model company and building it.

Why Insurers Tend to Miss These Windows

There are three patterns that explain why insurance keeps arriving late to opportunities like this one, and they are worth naming honestly.

The first is that we wait for credible loss data before we move. Underwriters want triangles. Actuaries want credibility. By the time we have either, the insurtechs and specialist MGAs have already built the wordings, the distribution, and the brand recognition. Cyber between roughly 2010 and 2018 is the case study every carrier should re-read this year.

The second is that we instinctively treat new technology as a risk to exclude rather than a customer base to serve. Look at the carrier behavior above — exclusions, carve-outs, regulatory filings to remove coverage. These are all defensive moves. Very few carriers are asking the offensive question: if 50 million people are now generating new exposure every day, who is selling them an appropriate product?

The third is that we are not yet good at partnering with non-insurance platforms. Carriers know how to work with brokers, agents, and program administrators. Partnering with a foundation model company — meeting their API standards, their UX expectations, their speed of iteration — is a different operating muscle, and most carriers have not built it.

The Window Is Narrower Than It Looks

Embedded auto insurance took roughly a decade to mature. Embedded travel coverage, similar. But the AI subscription market is growing at a pace neither category ever saw. The platforms that will define the next decade of distribution are being chosen right now, in 2026.

The next great embedded insurance product is unlikely to come from an automaker or an airline. It is more likely to appear next to a "Subscribe to Pro" button, sold to a freelancer who never knew they needed it until the moment it was offered.

The risk is here. The exposure is here. The customers are here. The only real question is which insurers stop excluding the future and start underwriting it.

Every great distribution channel in insurance was obvious in hindsight and invisible in the moment. AI subscriptions are simply the next one.

The investment landscape is becoming ever more unpredictable, driven by economic uncertainty, geopolitical risks and evolving regulations putting a strain on traditional asset portfolio optimization techniques.

These techniques are becoming less effective in addressing the rapidly evolving financial environment, and insurers are facing the challenge of struggling to balance complex regulatory and financial objectives using tools and techniques that were designed for a simpler, more stable era.

Shortcomings of traditional portfolio optimization

For decades, investors have relied on techniques rooted in linear relationships such as mean-variance optimization, which seeks to balance expected return against risk. These closed form approaches offer clear frameworks for decision-making but require simplified approximations of insurer-specific objectives.

Insurance companies face objectives far more complex than simply maximizing return for a given level of risk. They must also account for objectives such as solvency capital requirements, regulatory compliance and liquidity management. Traditional optimization approaches struggle to accommodate these objectives, particularly when constraints are non-linear and when conflicting goals must be considered simultaneously.

To overcome this challenge, insurers had to resort to trial-and-error or brute-force methods, manually generating portfolios until one fits the desired criteria. While this approach can work, it is inefficient and offers no assurance of optimality. The time and resources expended in this process can be considerable and the resulting portfolios may still fall short of meeting the required objectives.

Scenario-based machine learning - a new approach

Scenario-based machine learning (SBML) represents a paradigm shift in portfolio optimization, enabling users to evaluate any combination of objectives within a stochastic scenario framework. Unlike traditional methods, SBML embraces the full complexity of the real world, allowing for non-linear objectives and the simultaneous optimization of multiple competing goals.

The key to SBML is its ability to learn from vast data sets of generated balance sheet projections driven by a stochastic real-world scenario generator. Machine learning algorithms train on these projections, identifying patterns and relationships between the complex objectives and constraints. This learning process identifies asset portfolios that best meet the objectives and constraints defined in the optimization exercise creating an efficient frontier of suitable portfolios.

Targeting balance sheet metrics

One of the defining features of using SBML tools for strategic asset allocation (SAA) optimization is the capacity to target the balance sheet metrics that matter most to insurers, leading to a targeted SAA approach.

Let's take solvency capital as an example. By and large, for all insurance regulatory frameworks globally, the amount of capital held is directly influenced by the risk profile of the investments held. Regulatory frameworks, such as Solvency II in Europe, impose strict standards on insurers, requiring them to maintain sufficient capital to cover the risks of running asset portfolios. SBML enables insurers to directly incorporate these considerations into the optimization process maximizing returns or surplus while minimizing solvency capital and imposing a constraint on the amount of capital required.

Insurers that embrace tools that use AI and machine learning for portfolio optimization will be best positioned to achieve their goals, adapt to new challenges, and secure their place in the evolving landscape of global finance.

Commercial auto rates have been climbing. Every market participant knows this. The standard explanation involves nuclear verdicts, social inflation, and litigation funding. Those factors are real.

What gets less discussion is the operational side of the loss equation. Not the litigation. Not the verdict environment. The claims management practices that run between first notice of loss and final settlement, and what those practices cost on a book-level basis when commercial trucking is handled like any other commercial auto line.

It's a different animal. The industry broadly acknowledges this. But acknowledgment hasn't produced widespread changes in how these claims get handled.

The Supplement Rate as a Performance Indicator

Supplement rates on commercial trucking and heavy equipment claims average between 20% and 25% industry-wide. A supplement is a revised repair estimate — the initial figure gets approved, disassembly begins, and the shop returns with a higher number.

A 20% to 25% rate tells you something specific. It tells you the first estimate was wrong at a high frequency. That frequency isn't random. It reflects a systematic gap between the complexity of the equipment being assessed and the expertise of the person writing the first estimate.

A general auto adjuster reassigned to a Class 8 truck or a piece of construction equipment doesn't know what to look for. A specialist does. The operations using appraisers with dedicated heavy equipment expertise consistently hold supplement rates between 10% and 14%. That 10-point gap on a large commercial trucking book represents a material dollars-and-cents difference in indemnity spending. It shows up directly in loss ratios.

Most program administrators and MGA executives can't tell you their supplement rate on trucking claims.

Towing and Storage as Indemnity Leakage

Towing and storage on commercial vehicles is a significant and largely unmanaged cost category on most trucking programs. Storage fees of $125 to $200 per day accrue from the moment a vehicle is taken to a yard. Claims that sit unworked for 30 to 60 days generate thousands in storage exposure before a single repair decision is made.

The towing invoice itself is a second problem. Inflated mileage, charges for equipment that was dispatched but not deployed, fees for services not rendered. These line items go on the invoice and, in most cases, get paid without challenge because the adjusting operation doesn't have the market knowledge to identify what a reasonable commercial tow should cost.

One carrier reviewing its annual towing spending found it had overpaid by more than $650,000 in a single year. That's not an outlier. That's what happens when commercial vehicle towing invoices go through a general claims operation that doesn't specialize in this exposure.

On a book of any meaningful size, towing and storage leakage is a line item that belongs in loss ratio conversations. It rarely appears there because nobody is measuring it separately.

Subrogation Recovery as Underpriced Leverage

Commercial trucking subrogation is a specialty within a specialty. The values are high, liability is typically contested, and the file has to be built correctly from day one of the incident. When it is, win rates above 80% are achievable on eligible files.

Most general TPA operations don't run dedicated commercial trucking subrogation programs. The case complexity is high relative to the volume they handle in that category. Recovery rates on trucking subrogation through general programs reflect that mismatch.

For MGAs and program administrators with meaningful trucking exposure, subrogation recovery represents a straightforward improvement to the economics of the book. It doesn't require renegotiating terms. It requires routing eligible files to a team that knows what it's doing with them.

What the 2026 Claims Conversation Is Missing

The industry's attention in 2026 is rightly focused on AI-assisted claims processing, faster FNOL response, and data-driven loss analytics. The consensus view entering 2026 was that commercial auto rates would continue rising while claims automation would begin generating measurable efficiency gains. That framing is correct as far as it goes.

What it misses is that technology-assisted claims handling applied to a general adjusting model doesn't solve the expertise problem on specialized equipment. A faster general adjuster writing estimates on a crane or a loaded semi is still a general adjuster writing estimates on a crane or a loaded semi. Speed doesn't compensate for the knowledge gap that produces 22% supplement rates.

The gap between strategic intent and claims execution is where loss ratios on commercial trucking programs get made or broken. The intent to manage this exposure well is almost universal. The execution requires domain expertise that most general operations don't have and can't develop at a sufficient depth for an exposure this specialized.

The Program Design Question

For MGAs building or managing commercial trucking programs, the TPA selection question deserves the same analytical rigor as rate adequacy or reinsurance structure. The right question isn't which TPA can handle the claims. It's which TPA has the specific expertise to handle these claims at the supplement rates, towing spending, and subrogation recovery rates that a profitable book requires.

The specialty exists because general operations don't produce the outcomes this exposure demands.

The performance data from specialty operations — the supplement rates, towing savings, subrogation win rates — is publicly available for comparison. The loss ratio improvement potential is real and measurable. The question is whether program design conversations are treating claims expertise as a first-order variable or an afterthought.

For most trucking programs, it's still the latter.

Other Resources From Insurance Thought Leadership

1. "Insurance 2026: Progress Via Technology, Collaboration" (Jan. 8, 2026): "The consensus view entering 2026 was that commercial auto rates would continue rising while claims automation would begin generating measurable efficiency gains."
2. "4 Key Trends Reshaping P&C Insurance" (Feb. 5, 2026): "The gap between strategic intent and claims execution"