Elad Gil

The indiscriminate “SaaSpocalypse” trade mistakes a real long-run shift for an immediate extinction event. Gil argues that a fleet-management product like Samsara—with in-cab hardware, distribution, enterprise sales, and support—will not simply be replaced by a weekend vibe-coded app, though support can use vibe agents. Guo says Fortune 100 change management and security make a weekend-built CRM likely implausible. Decagon and Sierra do demonstrate a genuine move from per-seat software toward usage-priced support agents, but “this isn’t gonna be every single SaaS company.”

AI-native companies show that code can become abundant without making the rest of company-building abundant. Guo cites portfolio companies with hundreds of millions of dollars of revenue and fewer than 50 engineers, yet rapidly expanding from zero to nearly 100 salespeople: “Vibe sales is not happening.” Gil expects engineering productivity to be absorbed by enormous unmet software demand rather than simply eliminating engineering teams.

The investable engineering bottleneck may shift from writing code to allocating trustworthy human attention. If agents generate enormous volumes that nobody reads, “nobody deeply understands the code base,” leaving production systems fragile and full of “vibe coding slop.” Guo calls agent-first engineering management, testing, smart review, and formal verification “open season around this really, really big problem.”

A month of AI hype blurred partnerships, demos, and planted behavior into evidence of autonomous markets. Gil disputes claims that agents already choose vendors: partnership defaults that provision particular tools resemble Airtable silently running on AWS, not independent purchasing judgment. The “Malt book” Reddit-like forum example also seemed partly human-generated or planted for marketing. Guo’s sharper equity-research critique is that “the theory of competitive advantage didn’t just like poof, disappear”; production, distribution, change management, and product completeness still matter.

The underlying economics remain extraordinary even after stripping away the hype. Using Capital IQ data and projections, Gil’s team puts the AI labs’ journey from $1 billion to $10 billion of revenue at roughly one year, versus about 20-plus years for ADP and Adobe, while public projections imply $10 billion to $100 billion could take the labs three to five years. Meanwhile, GPT-4-equivalent pricing fell from roughly $37 to $0.25 per million tokens in 21 months—150x—and o1-equivalent pricing from $26 in December 2024 to $0.30 in November 2025—88x in 11 months.

AI could pull far more GDP into technology while concentrating much of the resulting value in a power-law head. Gil says the leading tech companies’ share rose from roughly 4% of US GDP in 2005 to about 12% today, with plausible 2035 scenarios ranging from 15%-20% to 30%; the top eight tech companies already represent about $23 trillion and well over half of the S&P’s value. Guo argues that expanding technological surface area could let the tail dominate, while Gil says there may be more $100 billion businesses but “the head and torso aggregate almost all the value.”

Faster growth does not confer SaaS-era durability: AI may compress a decade-long displacement cycle into one or two years. Gil warns that many startups get only about a 12-month window at peak value, so boards should schedule unemotional exit reviews once or twice annually; only a very small group should “never, ever, ever sell.” The strongest defense is a bundle spanning five or 10 aspects of the same vertical or application, reinforced by platforms, ecosystems, networks, or hardware—because “if every two years is 10 years,” a cloneable point product is a precarious control point.

Elad Gil sees a subset of AI markets consolidating, with apparent winners for the next two years—even if today’s leaders may not be five-year winners. Foundation LLMs, medical scribing, coding, and customer success have recognizable frontrunners; sales productivity, financial-analyst tooling, and accounting remain open, and Sarah Guo adds pharma. Gil wonders whether the unresolved markets lack the right product approach or still need better models. After years when “the more I learn, the less I know,” he finally sees “a nice breather in terms of uncertainty.”

The emerging application playbook combines a valuable vertical workflow, proprietary data, distribution, and users who can create, derive, or extend knowledge from that data. Guo places Abridge and OpenEvidence in that category while remaining unsure how sales will be won in a historically fragmented market. Coding suggests the ambiguity is narrowing: plausible entry points now include Cursor, Codeium/Windsurf, Cognition, and Microsoft Copilot.

AI startup consolidation may become strategic defense against incumbents, not an admission of defeat. Gil advises the top two startups in a category to consider merging to stop the startup-to-startup war and shift the fight toward three or four incumbents. Guo says founders, boards, and investors often treat even considering a merger as capitulation, though she calls it “capitulating in service of winning.”

Gil argues that several enormous biotech markets are commercially obvious but structurally neglected. Examples include deriving sperm or eggs from reprogrammed adult cells, maturing a woman’s remaining oocytes, treating skin aging and hair loss, restoring near vision or hearing, and regrowing teeth through genes such as USAG-1. The startling implication of cell-derived reproduction is that “any adult” could potentially have children with another—and cells obtained from someone by swapping some off them in a handshake could potentially enable reproduction involving that person.

Biotech’s financing architecture selects for assets that pharma will buy, rather than enduring new companies serving unconventional demand. Gil says incubators may invest $40 million for roughly 40% ownership, then steer programs toward cancer, cardiovascular disease, or neuroscience pipelines sought by acquirers. Regulatory capture and scientists’ view that fixing wrinkles is “low status” further strand usable science outside the venture funnel.

Gil presents world models and reinforcement learning as a possible route beyond text prediction, not primarily as near-term gaming products. Agents need planning, tool use, feedback, alternative reasoning paths, and self-evaluation, while behavior cloning is brittle outside demonstrated paths. The challenge is building “a copy of the universe”—an environment rich enough to teach adaptable problem-solving, yet cheap and diverse enough to avoid overfitting. Gil is unsure about immediate commercial applications; Guo’s extrapolation from Go and molecular evolution is that unconstrained search could produce unusual but superior code, molecules, or strategies.