Angela Strange

Angela Strange traces a16z’s initial international strategy to a line from Nubank’s David Vélez (relayed via her brother): “you could compete to be the fifth financial services provider for every customer in the US,” or go to markets with “5 fat, happy banks that only serve 20% of the population.” That thesis produced her first-ever a16z check—Santiago Suarez’s Addi, now serving a quarter of Colombia’s population for banking and payments—and later intersected with Gabriel Vasquez’s mapping of LatAm’s then-30 unicorns into a WhatsApp community.

Gabriel frames AI’s dichotomy: the technology “is very democratic… it distributes the ability to get it anywhere in the world,” but also concentrates the epicenter of rapid movement in the Bay Area. The flow reversed—instead of a16z flying to Brazil and Colombia, founders everywhere wanted to come to the Bay Area—and country-specific diasporas became the on-ramp. Elena Burger argues these networks may be even more powerful than elite-school alumni networks; Strange says they often lacked organization.

The discussion identifies three native advantages borderless founders can use to accelerate preferential attachment: differentiated talent pools, government-backed brand, and enterprise-customer access. Vasquez’s “AI Olympics” frame: every country wants medal-winners, so Poland’s government invested in ElevenLabs and Sweden’s supported Lovable and Legora, giving them a jump-start in validation with local and adjacent enterprises. Strange adds that “nobody wants to be the first bank or the first insurance company” in the US, but a borderless network can land that first logo abroad faster.

The bridge runs both directions: Cognition’s early go-to-market was Brazil, which represented “a really high share” of its early revenue, because Brazilian enterprises wanted to adopt AI quickly and had fewer providers. The newer, less intuitive power is intra-diaspora cross-pollination—a German company’s first design partner was the largest Spanish conglomerate—which Vasquez presents as a capability that “probably no other investor” can provide from the get-go.

Repeat founders are a deliberate sourcing wedge: a first wave of local entrepreneurs reached meaningful scale, often in the $1–5B range, without seeing their visions through, and “this $5 billion outcome wasn’t enough for me” drives round two. Frederik G. M.’s Pip.com chose a16z despite pre-existing investor relationships after the firm helped ideate with top executives from DoorDash, Lyft, and portfolio companies—differentiation on ideation, not check size.

Practical advice for international founders: visas first—a16z is invested in O-1 visa company Extraordinary—then spend at least 3–6 months in Silicon Valley, not just weeks. Strange says, “I never met somebody that came to Silicon Valley and was like, ‘This was such a waste of time.’” The real product is speed calibration: “Silicon Valley continues to be ahead of every other ecosystem” in the speed at which people operate.

The long-term claim: 40% of a16z’s investments were in international founders, split evenly between those based in the US and those based elsewhere. Vasquez wants the share of venture returns attributed to companies outside Silicon Valley to rise from 10% to 20–30%. He says the statistic does not fully tell the story: many borderless companies are based in Silicon Valley while much of their engineering team may remain in the founders’ home country, so they can be claimed by two countries.

🔗 Original source & video: The New Geography of Startups

Kavak has rebuilt itself so that between 100 and 200,000 agents are instantiated daily—one per customer, each with its own virtual machine, years of interaction memory, and a long-term goal of maximizing lifetime value. Head of AI Alejandro Maza says 96% of all interactions and 95% of transactions are now fully agent-handled, and frames the design question as “how would we build Kavak in 2035 with GPT-10-level intelligence.” The relational shift matters commercially: with 10M customers in the database, “just activating 1% of this customer base” could be worth hundreds of millions of dollars.

The agents aren’t support bots—they’re salespeople, and they now convert 2.1× better than Kavak’s human team while tripling NPS and customer satisfaction scores. Maza’s mechanism: a single “mega-expert” replaces 15 human specialists across financing, insurance, and trade-ins, is “infinitely patient,” and when one agent errs, the other agents learn from that mistake by the next day. In lending, Kavak usually approves car loans in under three minutes versus two months or more in Mexico and some emerging markets, with personalization of rate, risk, and loan amount.

Evals are the constraint on speed, not caution: Kavak spends roughly equal engineer time, tokens, and money on evals as on the agents themselves. “How fast can we go? It depends on the quality of our evals”—the car-brakes analogy—and the first checks are business outcomes: conversion, customer value and satisfaction, and willingness to re-engage.

When Opus 4.5 came out, Maza destroyed two years of working multi-agent architecture because “this isn’t the right paradigm anymore.” Thousands—possibly tens of thousands—of agents were running the business in December; he concluded that graphs and multi-agent latticework could constrain newer intelligence and rebuilt around a virtual-machine harness with memory, evals, a CLI, and long-term goals. His advice: “don’t build agentic workflows.”

An AI CEO has run the city of Cuernavaca for six weeks—it missed its goal of doubling profits but delivered 1.5×, or 50% more profits—micromanaging every number and messaging physical workers their daily plans. Maza says the remaining human-intensive work is mainly in the physical world: Kavak’s roughly 800 mechanics in Mexico get “El Mike,” a Ratatouille-style sidekick that helps with inspections, while warranties fell around 26%.

For enterprise buyers, Maza offers a token-quality framework investors can use to grade AI spend: tier 3 tokens go to agents with measurable per-token ROI, tier 2 are indirectly measurable, and tier 1 is raw Claude Code, ChatGPT, Cowork, or similar usage—“what happens with those? I have no idea.” Transformation must be top-down: “an army doesn’t really work if everyone comes up with ideas on the strategy”—hackathons and bottom-up use-case sponsorship “doesn’t work.”

The macro thesis is Schumpeterian: incumbents adopting AI superficially get 6–10% gains, while deep organizational redesign is needed to pursue 10× improvements. His electricity analogy: Ford’s production-line technologies existed by 1879 and 1881, but simply swapping a coal engine for an electric one produced about a 6% improvement; rebuilding the factory around electricity produced roughly 3× productivity. “It’s the innovator’s dilemma at an industrial scale,” and his founder advice is to “just map a trend that’s linear” in AI capability and build for that.

🔗 Original source & video: Kavak’s Playbook for Rebuilding a Company Around AI

AI turns software from a passive filing cabinet into an active labor substitute, opening a market potentially far larger than software itself. Alex Rampell traces a 65-year progression from on-premise databases to cloud systems of record and financial-services-enabled vertical SaaS; agents can now perform the work those systems merely recorded. The new formula is “Input, Coffee, Output, Code.”

The addressable budget shifts from software spend toward wages: US registered nurses alone represent more than $600 billion annually, versus under $600 billion for the entire worldwide software market. AI cannot perform CPR, but it can call patients before a colonoscopy, converse in 45 languages, and absorb work hospitals cannot staff. The operative question is how far customers let “their software budget bleed into their labor budget.”

Per-seat incumbents face a brutal cannibalization choice: lose most revenue as AI reduces seats, or reprice around outcomes and potentially grow 10×. Rampell’s Zendesk example pairs roughly $1.4 million of annual software spend with $50 million of support labor; copilots could cut 1,000 seats to 100, while autopilot could eliminate them. For Salesforce and Zendesk, AI is “both defense and offense.”

The strongest startup wedge is the “messy inbox problem”: automating judgment-intensive work between unstructured inputs and legacy systems of record. David Haber’s healthcare example, Tennr, trained against, he thinks, roughly 4 million documents and cut patient-intake administration costs about 90%, then began eating into scheduling, eligibility, and benefits. The AI capability may commoditize, so durability still comes from owning workflows, integrations, network effects, and the eventual system of record — “moats still matter.”

Previously uninvestable niches become venture-scale when software captures labor budgets or bundles labor with a 10×-better replacement system. Compliance is the model: it is reportedly America’s fourth-fastest-growing job, often runs on Excel, and remains chronically understaffed. David Haber describes AI agents clearing tens of thousands of alerts while helping introduce a better transaction-monitoring system, in the context of TD Bank’s $4 billion fine related to transaction monitoring.

AI may automate routine white-collar tasks while making scarce human interaction more valuable. The panel expects at least every white-collar job to gain a copilot, with some roles fully agentic; Rampell’s extreme formulation is that people may either “tell a computer what to do” or be “told by a computer what to do.” Yet once automated outreach becomes ubiquitous, relationships built face-to-face — even “over golf” — may command a premium.

Business fundamentals do not change, but falling costs expand both market size and competitive risk. Investors still need retention, gross profit, overhead discipline, and the “present value of future profits”; meanwhile, AI makes software easier to build and pushes prices inexorably downward. The most attractive hunting grounds are obscure industries where domain experts understand a workflow, the technology is already good enough, and 30-year-old systems can become “10× better.”

🔗 Original source & video: How AI is Reshaping Labor Markets: A $Trillion-Dollar Opportunity Explained