VCs have invested $200 billion betting AI will disrupt everything—but are they ready to be disrupted by AI themselves?
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VCs have invested $200 billion betting AI will disrupt everything—but are they ready to be disrupted by AI themselves?
When AI causes startup costs to plummet, founders may no longer need venture capital funding at all.
Navigating Web3 tides with focused insightsAuthor: WIRED
Translation & Editing: TechFlow
TechFlow Intro: Venture capitalists (VCs) are AI’s biggest believers—last year, they collectively poured over $200 billion into AI startups. Yet an awkward question looms: Could AI disrupt VCs themselves? A platform called ADIN has already begun replacing human analysts with AI agents to conduct investment due diligence—completing in one hour what used to take days or weeks. Even more threatening is a second layer of disruption: As AI slashes startup costs, founders may no longer need VC funding at all. This article interviews several prominent VCs, revealing real divisions and anxieties within the industry.
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Last fall, as venture capitalists flooded the AI sector with record-breaking sums, a group of investors gathered to evaluate a new startup. The company, Infinity Artificial Intelligence Institute, built software that automatically fine-tunes AI models to make them faster and cheaper. Its founding team looked strong, and the market was expanding rapidly. Half the investors were cautious; the other half smelled money. One declared the deal “an absolute slam dunk.”
The company was real. The $100,000 seed round those VCs invested in was real too. But the VCs themselves were all AI agents—part of a new platform called ADIN (Autonomous Deal Investing Network).
Launched in 2025, ADIN replaces human analysts in venture capital transactions. Feed it a startup’s pitch deck, and it outputs a detailed analysis of the business model and founding team, a list of due diligence questions and compliance risks, a TAM estimate, and a recommended valuation. ADIN deploys over a dozen distinct agent-investors, each with its own persona and investment thesis: Tech Oracle focuses on foundational technology; Unit Master evaluates financial fundamentals; Monopoly Maker hunts for monopoly opportunities in the spirit of Peter Thiel. When most agents favor a project, they recommend how much capital ADIN’s fund should allocate to it. The entire process takes roughly one hour—whereas human VC analysts typically require days or even weeks.
“The venture capital game has low success rates,” says Aaron Wright, co-founder of Tribute Labs, ADIN’s parent company. Today’s approach—a gut-feel, intuition-driven method for picking tomorrow’s unicorns—yields a “home run” (a return of 10x or more on invested capital) only about 1% of the time. Three-quarters of VC deals fail to return even the original capital.
In Wright’s view, AI models can dramatically improve those odds. He believes venture capital is entering its own Moneyball era, where quantitative methods will surpass human intuition—and everyone will start hitting more home runs. “These systems will increasingly weed out weak deals, focus on higher-potential ones, and lower operating costs for these firms,” Wright says. He believes AI agents could become the world’s best venture capitalists within just a few years.
And then what? “Sand Hill Road may cease to exist.”
No group is more bullish on AI than venture capitalists. Last year, they collectively invested over $200 billion into AI. Advances in AI models have reshaped how investors assess nearly every company and industry. Vinod Khosla, founder of Khosla Ventures, recently predicted AI will displace 80% of job functions by 2030. Yet many VCs appear to have underestimated how deeply AI will disrupt their own work.
Marc Andreessen—the star VC and co-founder of Andreessen Horowitz—said on his podcast *The Ben & Marc Show* that once AI handles everything else, venture capital may be “one of the last domains humans still do.” He argues the job goes beyond writing checks: It involves selecting the right idea and the right person at the right time—and then guiding them to success.
“It’s not science—it’s art,” Andreessen added. “If it were science, someone would eventually tune it precisely enough to hit eight out of ten times. But reality doesn’t work that way. You’re in the business of randomness. There’s something ineffable about it—something involving taste.”
Many VCs I interviewed for this article echoed this sentiment. Keval Desai, managing partner at VC firm Shakti, compares early-stage investing to “picking Michael Jordan out of kindergarten.” At such an early stage, there’s no product, no revenue—only potential. “You can have all the compute and algorithms you want, but without data, there’s nothing to analyze,” Desai says. (Though he concedes he occasionally asks Gemini to “play the role of a VC analyst” when evaluating unfamiliar markets.)
Brian Nichols, co-founder of Angel Squad—an angel investment network affiliated with early-stage VC firm Hustle Fund—told me he wouldn’t trust AI to handle the “screening” part of investing. Ultimately, venture capital is a relationship business: It’s about who you know—and whom you’re willing to personally vouch for. Still, he believes AI might replace other parts of the job. During our conversation, he’d just returned from a Hustle Fund team-building event where a partner had built a tool using Claude Code to triage founders’ emails. “We spend hours every day responding to founders’ pitches,” he said. “That time could probably be better spent elsewhere.” Aydin Senkut, founder and managing partner at VC firm Felicis, told me he believes most VCs are experimenting with AI in some form to stay competitive. His firm is currently testing chatbots to draft investment memos, improve deal sourcing, and help partners “score” founders.
Projects like ADIN aim to automate more of the underlying work. Due diligence—the process by which investors investigate a startup’s viability, risks, and growth potential—is among the most time-consuming steps in venture investing, especially when evaluating companies in emerging markets. ADIN compresses this step into minutes, quickly surfacing regulatory or compliance issues that could derail a deal. When assessing a mining-tech company, for instance, ADIN flagged a series of export-control regulations and cross-border data-transfer concerns. “These aren’t questions most investors would think to ask,” says Priyanka Desai, an ADIN partner. She adds that AI “doesn’t get tired, doesn’t develop blind spots from inertia, and surfaces long-tail risks that are easily overlooked.”
Humans still handle several key tasks. First, ADIN’s deal flow originates from a network of venture capital scouts. Though ADIN raises capital from LPs like a traditional VC fund, it offers scouts an unusual economic incentive: They receive 50% of the carried interest—a share of profits traditionally reserved for GPs (general partners). “Essentially, we’re giving GP-level economics to an individual whose sole job is to submit deals and leverage their network,” Desai explains.
Humans also remain responsible for the “last mile”—meeting founders and making the final decision on whether to write a check. “We know these systems aren’t perfect, so we require human double-checking,” Wright says. AI agents sometimes recommend deals too aggressively: He showed me a case where all agents loved a particular startup—but ADIN ultimately passed after meeting the founders and uncovering problems with existing competitors.
On the flip side, Wright says he’s also used ADIN to evaluate companies that have already raised over $20 million. In some cases, ADIN’s agents unanimously disliked them. “Our challenge is figuring out whether that’s accurate—or a misjudgment,” he says. In certain instances, human investors may fall into a common trap: hyping up a startup or founder purely on instinct.
Whether AI systems can outperform human investors is one question. But another existential threat looms: The very same AI technologies that make venture investing faster and more efficient are also making it easier—and cheaper—to launch software companies. Over the past decade, much of the VC industry’s capital has flowed into SaaS. Yet a project that once required a $2 million seed round to hire a professional engineering team can now achieve comparable product velocity with just a handful of “vibe coders” and less than six-figure funding. The math behind large checks no longer adds up.
Until recently, only a tiny fraction of unicorns were self-funded. According to SaaStr, which tracks SaaS companies, the average software unicorn has raised $370 million. Now companies like Midjourney—the AI image generator—have reached unicorn status with core teams numbering only in the dozens. (According to PitchBook’s latest data, Midjourney has around 100 employees. Court documents from a copyright lawsuit indicate annual revenue exceeding $300 million. Midjourney did not respond to WIRED’s request for comment.)
This scenario—founders simply no longer needing VC funding—is likely the most frightening prospect for venture capitalists. “That’s the existential threat,” says Angel Squad’s Nichols. “The money is there—but founders don’t need it.” Perhaps AI won’t directly replace investors—but it may render their investments unnecessary.
Outside robotics, biotech, or other hardware-heavy sectors, fewer startups may soon require the large-scale financing that underpins the venture capital industry. That could push the industry back toward its origins: a small, specialized field bridging the gap between scientific breakthroughs and commercial applications. (Giant foundation-model builders remain active here—and may continue raising VC capital to cover astronomical compute, data-center, and personnel costs.)
If launching a company becomes cheap enough, we may see rapid industry contraction. That could put investors out of work—not through replacement, but through obsolescence of the business model. “If these funds sit idle, competing fiercely for the rare deals that truly need financing, that creates another problem entirely,” Nichols says. “That’s what keeps investors up at night.”
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