We conceived of Meanwhile three years ago, between Thanksgiving and Christmas 2021. In January 2022, we raised our first seed round from Sam Altman and Lachy Groom. We knew very little about long-term insurance; now, we know a lot and have a licensed and regulated life insurer. That's part of the entrepreneurial journey. 

I use the phrase long-term insurance because “life insurance” encompasses many things—it can include protection, savings, retirement products, insurance bonds, tax and estate planning, investment accounts wrapped in insurance products, etc.

To me, the space is endlessly fascinating. Ambition and actuarial science don’t naturally go hand-in-hand, however. Max and I understood early, though, right from that Holiday season, that this space could keep our curiosity and attention for decades to come. This is an ancient financial service. The world is underinsured and our goal is to serve a billion people. Every double-click led to more places to explore. Every topic is a rabbit hole. 

To many people, though, long-term insurance is opaque and, worse, dull. What about it keeps my interest? It’s partially the simple humanity of it — living and dying, uncertainty, and risk. It’s a philosophical study of how we all have an asset we don’t often think about: our own mortality and longevity. Turn that around; we all have a liability we don’t manage — the slight chance we’ll die before we should or live longer than we expect with our savings. 

Actuarial science may seem dry, but there is riveting pure math in estimating mortality, probability distributions of our demise, discounting and present values of the far future, estimating returns on investments, liquidity management, and the impact — oh, the impacts — of simple assumptions, both right and wrong. 

Underwriting is the complex question of estimating someone’s longevity given the information received and a struggle against adverse selection. On one level, insurance product designs are a game-theoretic competition between the insurer and the insured, and on another, they are a simple and critical financial service provided to policyholders. 

Principal-agent problems are everywhere. Policyholders want both prudence and returns from insurers. Insurers want to make money (even the mutuals). Regulators want to protect policyholders but depend on insurers for their financial stability and economic impact. Insurers want policies sold carefully (adverse selection!), and agents (the people who sell policies) want them bought for commissions no matter what. The agent is literally an agent against the principal of the policyholder and will sometimes sell the highest commission products instead of the best one for the user’s needs. 

Insurance is good for society, so governments give it tax incentives, leading to another game of cat and mouse to be navigated and managed. 

Life insurers are permanent capital, free to invest with long time horizons (see my post on life insurance and abundance). 

Then, there are insurers' pure tech and operations— well, okay, maybe that only excites me. 

The point is that there is just SO MUCH there. Without writing a textbook, this post attempts to explain the basics with an eye toward the United States. I go over why users buy life insurance and annuities, how regulators think about life insurance, and how insurers conceptualize and operate their business. 

In the future, I’ll write about the problems in the space, and how and why we use digital money. 

The job to be done by a life insurer

Life insurance is a critical financial service. Life insurers have unique ways to support users in managing the financial risks associated with major life events, saving for their or their beneficiaries’ futures, and optimizing investments for tax purposes.

Why users buy life insurance

None of us knows when we will die, but we can be confident that we will. 

Policyholders buy protection from the uncertain timing and impact of their mortality through a contract with life insurance companies that promises a payout upon death. When the insured dies, the policy's beneficiaries receive the payout. A policyholder might be of prime working age with young children, so although they have a low statistical likelihood of death, that event would be catastrophic to the family. Insurance protects the people they love. 

Governments have decided that adequately insuring individuals against their mortality risk is good for society. This has led most countries with developed tax regimes to offer generous tax benefits to insurance products. These tax benefits themselves become a reason to buy life insurance contracts for tax and estate planning purposes, even for users who are wealthy enough that they do not need to provide financial protection for their families through insurance.

In the United States, for example, death benefits to beneficiaries are income tax-free, and policies' (cash) surrender value grows tax-deferred. Policy loans taken from (cash) surrender values are also tax-free. 

Why users buy annuities

The flip side of mortality risk is longevity risk: none of us knows if we will live longer than expected. An individual might outlive their savings. Like mortality, though, predicting aggregate longevity becomes easy as the number of individuals increases. Annuities are contracts that provide fixed payouts for life based on an initial investment. 

Governments have decided that supporting individuals in adequately saving for retirement is good for society. This has led most countries with developed tax regimes to offer generous tax benefits to annuities. So much so that many annuities are used as retirement accounts and are never actually annuitized—users do not elect to transform them into fixed payouts for the remainder of their lives and keep them as tax-efficient retirement accounts. 

For example, in the United States, money invested in an annuity grows tax-deferred before payouts begin. If a potential user has maxed out their other retirement product options — like an IRA or a 401(k) — an annuity is a tax-advantaged structure for their retirement savings. 

The business of life insurance

Life insurers make long-term promises to their policyholders. The life insurance business is about accurately modeling the underwriting risks taken in products, distributing those (profitable) products widely and efficiently, and managing the balance sheet prudently for appropriate returns. 

Long-term liabilities come with long-term assets to support them. In recent years, many asset managers have realized the opportunity to manage an insurance company's long-term permanent capital. Apollo, Brookfield, KKR, Blue Owl, and others have set up life reinsurers or direct insurers for this purpose. 

How life insurers make money

Life insurers make money in three ways: charging premiums that exceed the actuarial cost of mortality or longevity coverage (underwriting income), investing assets for returns that exceed guarantees and promises made (investment income), or through other fees. 

Underwriting income

When a life insurer designs a product, they must decide how much to charge for the protection they provide. Pricing differs for applicants of various ages, sexes, and risk classes (due to health or smoker status, for example). Accounted within that pricing is a range of assumptions around the acquisition cost of the policy, operations and technology expense, capital and reserve requirements, lapses (a user surrendering their policy before they die), and much more. After all those assumptions are set, any pricing that exceeds those expenses is underwriting profit. Products are sometimes sold at an underwriting loss, with investment gains making up the difference. 

Investment income

Insurers invest the premiums they receive. Returns above their promises and guarantees are investment income (analogous to net interest income in banking). For example, in a fixed deferred-annuity product, the life insurer might promise a crediting rate of 5% but design its investment portfolio to support those promises at 7% returns. The 2% difference is the margin to the company.

Fee income

Life insurers charge fees and expenses for some products. Annuities, for example, frequently have a management fee or expenses load. Universal life policies might have an administrative fee above and beyond the cost of insurance. 

How life insurers operate

Life insurers are contractually required to meet their promises to policyholders. To do that prudently, they must understand the risks they are taking. Actuarial science is concerned with carefully managing insurance liabilities and the reserve set aside from premiums to pay future claims. 

Insurance companies and their actuaries also carefully model investment risks. Regulators closely oversee them, setting strict guidelines on the types of investments they can make and the amount of capital they must maintain and hold. 

Insurers manage many trillion dollars globally and have long-term liabilities to match long-term assets. There is nothing like demand deposits in insurance; without that promise of liquidity, insurers are not subject to “run risk” like banks are (in theory, there can be “mass lapses” which insurers have to model and protect against). 

Reserves

Reserves are the portion of premiums an insurer sets aside for future claims. For a rough approximation, the reserve amount equals the present value of the expected value of potential future payouts subtracted from the present value of the expected value of possible future premiums. 

Capital

Insurance companies hold capital in addition to their reserves to account for risks beyond payout liabilities. Such risks can include investment performance, interest rate shocks, and the stickiness of the capital base itself. Those risks and their probabilistic potential impact on the balance sheet are calculated, and a capital requirement is calculated given the assets (investments) and liabilities (guarantees to policyholders). If capital exceeds regulatory requirements, given all the risks, the company is solvent. Otherwise, it is insolvent, and regulators will act.

Governance, risk, and compliance

Insurance companies are tightly regulated due to the long-term financial guarantee made to policyholders and the opportunity for investment and operational mismanagement. This leads to various regulatory requirements around board and internal governance, enterprise risk (including underwriting, financial, credit, financial crime, operational, and other risks), and compliance with relevant reporting and due diligence regulations and laws. 

Reinsurance

Reinsurers provide insurance to insurers. They can take on all or only part of a carrier's obligations. Life insurers can use reinsurance to protect against excess individual and collective risks or to free up regulatory capital. 

Insurance carriers can also operate in the same group as reinsurers, typically consolidating risk across carriers and centralizing the balance sheet into regulatory jurisdictions with favorable capital and investment rules (like Bermuda). 

How insurers distribute their product

In many jurisdictions, including the United States, licensed agents must sell life insurance. Life insurers can either distribute through a captive agency (as New York Life and Northwestern Mutual primarily do) or work with third-party distribution. 

Third-party distributors can be independent or closely affiliated with a bank or wealth management company. Independent third-party distributors include brokerages (including MGAs and BGAs), independent marketing organizations (IMOs), and field marketing organizations. 

Whether captive or third-party, agents are frequently held at arm’s length with little to no practical support, tooling, or software to make their jobs easier. 

No one small can hope to win the infra layer in AI (NVIDIA) or the model layer (OpenAI, Anthropic, Meta). The opportunity is to compete at the application layer. There will be three levels of business opportunities in app-level AI based on the amount of work a company can take on:

1. The most modest opportunity lies in developing agents that can replace or augment existing worker profiles — software engineers, accountants, marketers, etc. However, this type of intelligence will become commoditized and diminished through competition. It will be trivial to stand up companies here in the long run. We will see excitement and investment but little long-term viability - with only a few durable businesses likely to emerge. I predict market capitalizations ranging from a few billion to tens of billions of dollars.
2. The medium opportunity lies in building comprehensive agentic workflows with domain and application-specific reasoning tailored for a fragmented and competitive market. To deeply understand the space and its opportunities and create a specialized vertical operating system. This is analogous to the existing vertical SaaS plays but with more significant growth potential. The unique intelligence and problem-solving capabilities necessary to compete, capture, and retain enterprise customers will support the development of durable businesses worth tens to hundreds of billions of dollars.
3. The most significant opportunity lies in developing a vertically integrated, full-stack solution to compete effectively and capture substantial market share. This approach can substantially increase profit margins in the right market, allowing these companies to compete on price and establish market dominance. These markets often feature only a few significant players or have deep regulatory moats. Companies with this business model have the potential to redefine the makeup of global markets.

I believe the latter two opportunities are the durable ones worth focusing on.

The recent essay "Generative AI’s Act o1” by Sonya Huang and Pat Grady had the correct arguments but the wrong conclusions. The three points that resonated with me are:

"[W]e still need application or domain-specific reasoning to deliver useful AI agents. The messy real world requires significant domain and application-specific reasoning that cannot efficiently be encoded in a general model.”

“Application layer AI companies are not just UIs on top of a foundation model. Far from it. They have sophisticated cognitive architectures that typically include multiple foundation models with some sort of routing mechanism on top, vector and/or graph databases for RAG, guardrails to ensure compliance, and application logic that mimics the way a human might think about reasoning through a workflow.”

“Cloud companies sold software ($ / seat). AI companies sell work ($ / outcome)”

However, the essay doesn’t go far enough. It makes a critical assumption: building individual AI agent types for others is the best way to capture value. It presumes the horizontal or functional SaaS business model—whether they rename it Service-as-a-Software or not.

They are wrong. To take an example, McKinsey, Accenture, the Big 4, Tata, EPAM, etc., are just derivatives of global economic activity, not the bulk of that activity itself. Those firms all do well for themselves but suffer from always being one step away from the actual work.

Horizontal (functional) agents and intelligence will be commoditized

The future of agentic applications is vertical. Verticalization has been the trend over the last few years and will only accelerate with AI.

By ‘horizontal’, I primarily mean functional. In many ways, this is the original era of SaaS — every function or department inside a company would be replaced by software. We’ll have a SaaS tool for the legal department and one (or more) for sales, marketing, finance, operations, engineering, product, etc. The idea is now to reimagine that “software” as “service,” rethinking making tools for a department as creating agents that replace workers — we have built the lawyer agent, or the SDR, marketer, accountant, analyst, or software engineer.

Applying that functional/horizontal software playbook misses the opportunity. There will be some billion-dollar companies that way, but that isn’t the big prize. If you buy individual agents like software, you are recreating the functional divisions that slow down work. You're recreating the friction that exists in organizations. We don't need an AI lawyer; we must reimagine legal work. To use that example, the fundamental problem in the real world is that legal analysis isn’t embedded in workflows and processes.

Horizontal or functional software will also be trivial to create internally or through competing startups trying to sell to enterprises. Horizontal SaaS margins will disappear. Intelligence alone will be a race to the bottom.[1] Individual agents will be trivial to spin up.

Choosing between being a vertical OS or a full-stack vertical competitor

To truly solve a problem, one has to understand it completely end-to-end. Solutions are where the ultimate margin expansion will come from. That opportunity comes from doing the much more challenging job of understanding a complete problem, integrating and mixing AI, software, automation, and workflows to do the actual work.

That means two business models will drive the next wave of innovation: being a vertically integrated “operating system” for customers (currently labeled “vertical SaaS”) or vertically integrating a solution directly.

Choosing between these two models depends on the magnitude of economic transformation, the inefficiency of selling software, the market composition (lots of players / or a few, is there power law in market share), and the equity efficiency of the build path.

In some ways, this can be summarized by the famous Alex Rampell quote — “The battle between every startup and incumbent comes down to whether the startup gets distribution before the incumbent gets innovation.” Incumbents are vertically integrated — the battle in every sector will be whether they will adopt vertical AI tooling before a startup integrates an end-to-end AI solution, reimagining the cost structures in the process.

This can be summarized in two questions:

1. How competitive and fragmented is the space? The more competitive a space is, the more likely individual companies inside it will adopt solutions to outperform one another. The more monopolistic, oligopolistic, or regulatory capture in a space, the better it will be to integrate a solution vertically.
2. How much of the work in a space is automatable? The more work there is in a space that must continue to be done by humans, the better it is to be a vertical OS vs vertically integrated directly. That way, you’re building high-margin tooling and leaving the human-intensive tasks and management to your customers.

Our bet: vertically integrated life insurance

It is informative to explain the industry we’ve taken on and why we choose to be vertically integrated.

Life insurance companies are traditionally slow to adopt new technologies and unlikely to embrace full-scale, end-to-end automation, even when they purchase horizontal agentic point solutions. The industry has a deep regulatory moat, and even though the market is fragmented (in the United States, no single company holds more than 10%), it operates much like an oligopoly.

Despite their large workforces, life insurers are essentially data and technology companies. The majority of their activities are centered on white-collar, knowledge-based work.

Recognizing this, we saw the right opportunity to vertically integrate a solution, leveraging agentic AI, to transform the industry (and also a generational opportunity for digital money to enter the market).

Our vision is to build the world's largest life insurer as measured by customer count, annual premiums sold, and total assets under management. We are a tech-first, vertically integrated insurance and reinsurance stack. Our secondary goal is to achieve the lowest combined ratio globally (a measure of cost structure) while also operating with a workforce three orders of magnitude smaller than the largest incumbents (hundreds of employees instead of hundreds of thousands).

Nevertheless, we plan to collaborate with life insurance agents and other embedded distribution partners because life insurance agencies are both highly competitive and fragmented spaces that are very human and relationship-driven. We plan to develop tools that empower these partners.

Ultimately, we have launched a fully operational life insurance company. We are regulated and licensed in Bermuda — the insurance capital of the world, a jurisdiction known for its stringent regulatory standards for life insurers. We have the exact requirements as any other life insurer, including actuarial modeling and reserving, capital calculations, underwriting, investments, customer service, claims, know-your-customer and anti-money laundering compliance, risk management, (internal and external) audit, and much more.

We manage all this with just eight people through homegrown software and automation. We have also developed customized AI agents for four key roles: (1) reserving actuary, (2) sales/customer support, (3) underwriter, and (4) risk specialist (in the future, we plan to add a claims agent too) - however, more importantly, we have integrated these agents into automated workflows to handle the full spectrum of insurance operations.

Size of the prize

There is another, not particularly intuitive, seemly small advantage that I think makes a huge difference in building a company vertically: In any of the cases where you're selling something, software, agents, or "outcomes," you still need to build the software, agents, or outcomes to sufficient completeness and robustness that you can actually sell them. You still need a sales team and sales cycle to do B2B.

But if you are just doing the work yourself, the software can be used internally, and that is SO much faster and more flexible when building with a good team.

Suppose you're founding a startup or a new business unit today. In that case, you must ask yourself: Will fragmented incumbents dominate the space I’m attacking, or is this a once-in-a-lifetime decade to redefine it?

If incumbents are likely to maintain their dominance, focus on building the operating system that powers the industry. This can be a highly profitable business with significant margins to capture.

However, if there is an opportunity to create a vertically integrated solution, seize it. The work may be more challenging — you will have to slog through rugged terrain; that’s why the space is defensible. In our case, life insurance and annuity premiums account for 3% of the world’s GDP. The market capitalizations of the largest public life insurers are over $100 billion. Our aim is significantly higher. Yours can be, too.

[1] Marc Andreessen recently said, “Are all those {AI} companies actually in a race to the bottom in which it turns out that selling intelligence is like selling rice?” If intelligence is rice, you have to make paella or risotto — somewhat challenging to cook, with a lot of other ingredients. Andressen was talking about the big model providers like OpenAI, Anthropic, and Google, but I think it applies equally well to the next level of horizontal intelligence — the accountant agent or the SDR agent.