David Haber
Frontier Insights
Core Frontier Thesis: Software is shifting from passive record-keeping to labor execution. By capturing payroll rather than software budgets, AI expands addressable markets into trillions of dollars.
Strategic Imperatives:
- Sell finished outcomes, not software seats; legacy per-seat SaaS faces inevitable self-cannibalization.
- Prioritize labor automation, greenfield systems, and embedded fintech over raw models.
- Moats require owning vertical workflows, proprietary outcome data, deep system integrations, and network-scale trust.
Critical Risks:
- Commoditized foundation models erode thin workflow layers lacking proprietary data.
- High-stakes consumer agents face severe trust constraints.
- AI-accelerated fraud demands immediate enterprise-grade defenses.
Key Views & Dialogues
Goldman Sachs Chairman on Why Finance Adopts AI Differently | a16z
Finance is adopting AI aggressively, but near-zero error tolerance requires proven and experimental systems to run in parallel—sometimes 50 runs with perfection on the last 49—raising costs before efficiency gains. Opaque software executing 70,000 transactions could scale hidden errors and leverage, making testable reliability, regulation, and public legitimacy key adoption constraints for OpenAI, Anthropic, and other systemically important AI companies.
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- Blankfein’s core risk-management call is that investors must take risk while planning and buying cheap mitigants in advance. Forecasting matters less than asking what could happen, what the portfolio would do, and which protections are available before “the hurricane is coming.” Thorough contingency planning lets a firm move so quickly that it appears prescient: “I want everybody to be called for a false start.”
- Finance will adopt AI aggressively, but its near-zero error budget produces a different adoption curve from Silicon Valley’s. Goldman kept the trusted system running while testing its replacement, sometimes needing “50 times” and perfection on “the last 49” before switching. Technology therefore increased costs initially; regulated institutions could not rely on apologies after a rollout failed.
- The underappreciated AI danger is opaque, massively leveraged execution rather than machine supremacy. Software could execute 70,000 transactions without the human intuition or visible reasoning trail that once stopped a trading room cold. Regulation might need to slow deployment “not because it’s smarter than us and it’s going to turn us into pets,” but because institutions cannot yet test whether outputs are right.
- AI is “going to be very, very important,” yet that does not make every model or company a winner. Blankfein sees genuine conviction in founder-dominated hyperscalers risking their own wealth and egos, but conviction is not correctness. He suggests the world might need four large language models rather than 10, with two becoming very large winners and the field possibly reducing to two.
- Goldman preserved partnership behavior after its IPO by aligning people to the whole firm, then reshaped its earnings for public-market math. Glass-Steagall’s repeal made a larger balance sheet necessary, but shareholders valued consistency: “In a private company, you care about the E; in a public company, you care about P/E.” Moving principal risk into funds converted “100-cent dollars” into lower-risk “20-cent dollars,” requiring more volume but supporting higher P/E and return on equity.
- Goldman’s financial-crisis edge came from forcing marks into reality before reality forced the firm’s hand. A separate valuation group told traders to “go out and sell something—sell a fraction,” exposing vanished bids in purportedly AAA assets and embedding losses before positions had to be sold. Goldman also fully hedged AIG exposure, demanded collateral, and honored commitments such as Chrysler’s—though “not for more” and “not sooner” than agreed.
- Systemically important AI companies should establish public legitimacy before backlash arrives. Goldman learned that it had become “too important, too influential, too big to be anonymous,” yet had no consumer relationship anchoring its reputation when the crisis hit. Blankfein’s advice to leaders at OpenAI, Anthropic and similar institutions is to explain their economic function early, because being modest and invisible becomes a liability when the public decides something went wrong.
How to Reorg After AI Changes Everything | Block’s Owen Jennings on the a16z Show
Block says Opus 4.6 and Codex 5.3 broke the link between headcount and output, prompting a workforce reduction of slightly more than 40% concentrated in development rather than compliance or sales. Small squads now supervise abundant machine labor, with meetings down 70-80% and Money Bot reduced from roughly 15 people to four plus $2,000 of tokens, while distribution, regulation, network effects, hardware, and proprietary economic data define the emerging moat.
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- Jennings says the first week of December broke the decades-old link between headcount and company output. Opus 4.6 and Codex 5.3 suddenly became capable inside complex existing codebases, letting one or two engineers become “10, 20, 100 x more productive.” After a Q1 review, Block cut slightly more than 40% of its workforce.
- The cut’s composition is Block’s rebuttal to the claim that this was merely a 2021 overhiring cleanup. Reductions were far larger in development, while outbound sales and account management were barely touched; Block also protected compliance and compliance technology. Jennings’s categorical line: “We’re not writing code by hand anymore. That’s over. That’s done.”
- Block replaced feature-team economics with small teams supervising abundant machine labor. Money Bot moved from roughly 15 people to four plus $2,000 of tokens, while Jennings says he now context-switches among as many as 14 agents producing parallel PRs. Meetings fell 70-80%, development layers fell roughly 50-60%, and squads now contain one to six people.
- The operating leverage extends beyond software development into deterministic business workflows. Block’s chatbots and AI phone support automate a majority of inquiries. Block keeps humans in the loop for risk and compliance today, but Jennings expects systems eventually to outperform “a thousand humans” processing those queues.
- Block’s product bet is that static financial-app interfaces will start disappearing within six months. Goose, its model-agnostic harness with access to probably 120 models, underpins Cash App’s Money Bot and Square’s Manager Bot. Generative UI can build customer-specific charts or even a restaurant scheduling app on demand, creating engagement upside alongside a “potentially a nightmare” QA problem across tens of millions of users.
- Near-term defensibility remains distribution, regulation, network effects, and hardware; long-term defensibility becomes proprietary understanding. Anyone might build peer-to-peer software in a week, but not “vibe code” 50-60 million monthly active users. Block’s intended moat is a rapid feedback loop around its distinctive signal—how buyers and sellers participate in the economy—because companies unable to name what they uniquely understand “maybe could get vibe coded away.”
- The AI transformation has not yet resolved Block’s public-market disconnect. Haber notes that the business and gross profit per employee grew while the stock stayed roughly flat for six or seven years; Jennings concedes that the roughly $260 price in 2021 was “a little bit irrational.” His answer is deliberately long-term: markets are voting machines now, weighing machines later, so “just focus on building.”
How AI Will Transform Fintech In 2026
Fintech is entering an early-to-mid-spring cycle where survivors are expanding from digital access into cash-flow underwriting, embedded finance, fraud controls, and full-stack products. AI’s clearest institutional opportunity is software that performs compliance, servicing, treasury, and trading work, while consumer agents still face a trust barrier around unexplained money movement. Fraud is growing 18% to 20% annually as AI accelerates adversaries, making Plaid’s cross-network Protect and Lens Score important 2026 catalysts amid unresolved defensive challenges.
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Fintech has moved from a startup category into the operating fabric of financial services, but its capital cycle remains brutally seasonal. About 25% of venture dollars flowed into fintech during the mid-2020-to-early-2022 “big EDM pumping summer,” followed by “basically 0%” from the second half of 2022. David Haber places the current cycle in “early to mid-spring”: stronger survivors, new startups, and lingering snow in lending and the broader economy.
The first fintech wave solved access; the next must improve the underlying financial product. Digitization can now produce 30 mortgage offers in an hour or a Rocket application in five minutes, but, as Perret put it, “We’ve made it digital. We haven’t necessarily made it excellent.” Cash-flow-informed underwriting, better fraud controls, embedded finance, and full-stack product bundles are the next value pools.
AI’s clearest near-term institutional role may be automating labor rather than launching autonomous consumer finance. Haber sees financial institutions moving from defensive, in-house development toward outside software that “can actually do the work” across compliance, risk, servicing, treasury, and trading. Voice agents operating in 50 languages illustrate the new economics: “The TAM is largely labor.”
Consumer financial agents remain a trust problem disguised as a technology opportunity. Perret wants an app that automatically routes his paycheck among expenses, high-yield savings, and investments, yet doubts mainstream users will accept unexplained money movement: “Where’s my money? What’s going on?” Plaid’s strategy is infrastructural—let users safely connect data and authorize actions, then observe emerging behavior while watching for new risks.
AI is already scaling the adversary faster than the defense. Financial fraud is growing 18% to 20% annually, and Perret’s bleak 2026 call is that “the mouse is winning right now,” even if the cat eventually prevails. AI has replaced the human factories described in some pig-butchering operations, while Plaid’s Protect combines bank, device, and cross-network behavior to score users, accounts, and actions.
Crypto’s mainstream path runs through familiar financial behavior, not necessarily a wholly separate system. Perret framed speculation, prediction markets, saving, investing, and spending as durable consumer desires whose form factors change. Haber expects some convergence between core financial services and stablecoins such as USDC, while Perret was not sure crypto would merge with banks; Haber left room for its more decentralized frontier.
Plaid’s next leg rests on products that became possible only after its network reached sufficient scale. Protect and the income-and-expense-based Lens Score are positioned as major 2026 drivers, following an 11-to-13-year journey that included paperwork to sell to Visa, the deal’s reversal, fintech winter, and several “refounding” moments. Perret argues the downturn—not the boom—created the discipline and data foundation for faster product development.