The timing of the filing was not coincidental: on May 18, a California federal jury dismissed all of Elon Musk’s claims against OpenAI, clearing the most critical legal obstacle in the IPO process. In September, CFO Sarah Friar favored a longer preparation timeline. The current filing reflects CEO Sam Altman’s push for a September listing, with the CEO’s position prevailing over internal disagreements on pacing.

The article argues that the real signal of this filing is not the label of “world’s largest AI IPO”, but rather that OpenAI chose to proactively lock in public market pricing power and narrative control at the moment when the three battlegrounds — ARR growth rate, operational profitability, and valuation multiples — are about to fully diverge from Anthropic. This is a defensive preemption, not a winner’s posture.

I. ARR Growth and Valuation Multiples Have Already Reversed

When the annualized revenue trajectories of both leading companies are plotted together, the reversal inflection point appears in Q1 2026:

• OpenAI rose from $20 billion ARR at end-2025 to approximately $24 billion by April — ~25% growth in 5 months;

• Anthropic surged from $9 billion to $30 billion (official disclosure) over the same period, and according to follow-up reports from The Information and SemiAnalysis, the run rate was approaching $44 billion by May.

• OpenAI Q1 actual revenue was $5.7 billion; Anthropic Q1 was $4.7 billion — the gap narrowing to a $1 billion range.

Valuation multiples calculated on current disclosed bases:

• OpenAI: $852B / $24B ARR ≈ 35x

• Anthropic: $380B / $14B ARR ≈ 27x in February funding round negotiations; $900B / $44B ARR ≈ 20.5x in this month’s new negotiations

The implied secondary market pricing for Anthropic has also touched $1 trillion. As Anthropic overtakes on ARR, valuation multiples are converging — meaning OpenAI can no longer sustain a 35–40x ARR multiple on the basis of “industry leader” scarcity premium. This is the direct trigger for the preemptive filing.

II. Is the $1 Trillion Anchor Sustainable?

Bull case for $1 trillion target valuation has three pillars:

1. Private valuation trajectory rising consistently for 12 months: ~$157B at end-2024 → ~$500B in October 2025 → $852B this round;

2. Microsoft remains both largest customer and largest compute provider — the partnership framework provides a capital markets narrative of locked-in revenue streams;

3. Dual consumer and enterprise lines — business structure is broader than API-only Anthropic.

Bear case data is equally clear:

1. Absolute growth has been overtaken: Anthropic grew ARR 4.9x in 5 months; OpenAI only 1.25x over the same period. Even at a 40x ARR target, OpenAI’s pricing is less justified than Anthropic’s current implied 20–22x multiple;

2. Operational leverage is moving in opposite directions: Anthropic may achieve its first operational profit in Q2; OpenAI’s losses are widening over the same period. CFO Friar — bound by a 5-year, $600 billion compute commitment — has publicly acknowledged that revenue growth is insufficient to support all data center construction. This is an unavoidable risk exposure in the S-1 roadshow;

3. Governance terms unresolved + banker conflicts: Revenue sharing with Microsoft, IP ownership, and board power dynamics are not fully publicly disclosed. The underwriting syndicate of Goldman Sachs and Morgan Stanley are simultaneously in talks with Anthropic about its IPO. The same roadshow team must pitch two direct competitors to the same pool of LPs — this is yet another motivation for OpenAI to file first.

III. Transmission to the financing environment of the track

OpenAI’s filing will squeeze competitors on two levels:

1. Infrastructure comparable anchor reset

GPU cloud and training platforms like CoreWeave, Lambda Labs, and Together AI — once detailed OpenAI compute expenditure data becomes available as a benchmark — will face recalibration of their current 9–15x revenue multiples in private market pricing. Some companies may be forced to prematurely disclose take-or-pay contract details.

2. Window compression effect

SpaceX publicly filed its S-1 on May 20 (target valuation $1.75 trillion, raising $75 billion, Nasdaq ticker SPCX), with a roadshow scheduled to launch June 5 and pricing around June 12. OpenAI confirmed its filing just two days after SpaceX’s S-1 went public — the time-window pressure is immediate. Q3–Q4 will see at least $150 billion combined in mega-issuances from SpaceX and OpenAI. The listing window for mid-tier AI companies is effectively pushed to Q1 2027 or later.

For Anthropic, however, OpenAI’s filing timing actually presents a tactical opportunity: its proposed $900 billion private round can be completed before OpenAI’s public S-1, positioning Anthropic as the “unlisted but ARR- and profit-stronger” comparable before its own roadshow.

IV. Three Variables to Watch Over the Next 1–2 Quarters

1. OpenAI’s public S-1 disclosure — gross margin and Microsoft revenue-sharing mechanism

The public S-1 is expected 15 days after the confidential filing, i.e., no earlier than mid-to-late August. Key metrics to watch: if gross margin is significantly below secondary market implied assumptions, or if the Microsoft revenue-sharing mechanism is above market-estimated take rates (~20% range), the $1 trillion valuation will likely fall back to the $750B–$850B range.

2. Whether Anthropic files before OpenAI’s public S-1, and its disclosed Q2 operating profit data

If Anthropic files and can demonstrate Q2 profitability, OpenAI will face the “industry leader” narrative being dismantled. OpenAI’s valuation would face substantive downside, with the lower bound potentially breaking through $700 billion.

3. SpaceX’s June offering results and final pricing range

SpaceX’s current target is $1.75 trillion at $75 billion raise. If the final issuance valuation falls below $1.5 trillion, or the price drops to the lower end of the issuance range within 30 days after pricing, it indicates that the market’s capacity to absorb trillion-dollar landmark IPOs is nearly saturated. This will directly affect the difficulty of securing anchor investors and the pricing ceiling for OpenAI’s roadshow in September.

On May 18th, SVTR partnered with Zhiwai to host a closed-door luncheon on “Chinese AI Brands Going Global” in Silicon Valley. The event gathered entrepreneurs building consumer brands, AI applications, cloud services, influencer marketing, e-commerce platforms, and deep-tech startups on US marketplaces.

Participant niches included: consumer brands for specific US demographics, AI-powered influencer marketing, UGC licensing, content platforms for micro-merchants, deep-tech ventures, and cloud infrastructure.

The core question everyone grappled with: As AI, supply chains, content channels, and global markets shift simultaneously, how should Chinese entrepreneurs rethink “brand globalization”?

Deep Needs Behind a Small Niche

After the event, Zhiwai founder Lina shared a representative case: An entrepreneur building an electric vehicle for the American hunting community.

At first glance this seems extremely niche. But digging deeper, the needs run surprisingly deep:

The US has a large population of licensed hunters who are highly willing to invest in gear. But standard e-bikes completely fail their real scenarios: climbing hills with gear, navigating muddy terrain, cold-weather starting, long-range needs, chain-break resistance, and high climbing ability.

What hunters actually need: a vehicle that handles 30-degree slopes, starts at -20°F, carries 200 lbs of gear, and lasts a full day on a single charge.

Even more noteworthy: the entrepreneur himself is the target user. The project started from “I’m fed up with this problem myself” — not “I’m going to build something for a big market.”

This case sparked the core discussion: Must great entrepreneurial opportunities start from “big markets”? The answer is no.

Often, a small circle actually has sharper pain points, more genuine demand, and stronger word-of-mouth — making it easier to carve out a true niche market. Small community, high pain point, strong reputation — this may well be the starting point for a global brand.

Especially in the US market, segmented user groups have very specific needs and clear willingness to pay: hunters, outdoor enthusiasts, micro-merchants, content creators, or professional users — they don’t need “everything to everyone.” They just need a tool that truly understands their scenario and solves their specific problems.

This is what entrepreneurs need to rethink: Going global isn’t just exporting domestic goods; it’s about using global supply chains, AI tools, and local user insights to redefine the product entirely.

AI’s Penetration into Brand Globalization

The session also covered AI’s real-world applications in traditional industries: Even in manufacturing like electric bicycles, AI has entered the product development process. The hunter e-bike startup is already using LLMs to assist with code writing, drivetrain design, and generating product mockups.

AI’s impact extends far beyond software and marketing — it has seeped into manufacturing and hardware entrepreneurship.

In the past, building a hardware brand required a long cycle: product definition, engineering design, supply chain coordination, market testing, and content distribution. Now AI helps teams move faster on solution design, prototype expression, content production, user feedback analysis, and market testing — dramatically improving efficiency.

But AI doesn’t replace an entrepreneur’s understanding of users. It amplifies the advantages of teams that truly understand users, scenarios, and supply chains.

Four Typical Paths for Chinese AI Brand Globalization

1. Leveraging China’s supply chain to serve niche US markets
Like the hunter e-bike — the essence isn’t “e-bike exports,” but redefining products around high-pain-point user groups.

2. Next-generation brand infrastructure powered by AI
Influencer marketing, creator discovery, video content analysis, and UGC licensing. Projects like Influcio, Chimeboard, and Vyrill help businesses better understand content, influencers, users, and conversion logic.

3. New store-opening platforms for micro-merchants
Aladdinmall focuses on online commerce, local discovery, pre-purchase communication, managed payments, and mobile shopping — lowering barriers for local businesses to go online.

4. Deep-tech ventures from foundational technology
Breaking through from the technology side to find industrial application scenarios for global markets.

AWS and startup solutions architects also contributed perspectives on cloud infrastructure, MVP architecture, AI agents, LLM application development, and modern data stacks — supporting startups as they scale from early product to growth.

All of this points to one trend: Brand globalization is no longer “selling Chinese goods overseas.” It has become a system-wide endeavor: product definition, supply chain organization, user insights, content marketing, channel building, AI tools, data infrastructure, and local trust networks.

For Chinese entrepreneurs, the US market is both a proving ground and an amplifier: real user needs, diverse niche markets, mature commercial infrastructure, and customers willing to pay for professional products. But it also demands that entrepreneurs truly understand local users — rather than equating “globalization” with cross-border sales.

Great brands often don’t start from a grand slogan, but from a real user’s real pain point.
A small niche may hide a deep need.
A specific user group may lead to a global brand.

This is the direction SVTR aims to connect: gathering practitioners in products, brands, technology, supply chains, traffic, and industrial services — letting real experience flow and helping more Chinese entrepreneurs discover real opportunities in global markets.

SVTR believes this is one of the most underrated secondary signals of 2026. A group of elites with the most solid information advantages are voting with their feet to redefine the real moat of the vertical Agent track. It lies neither in models nor data, but in category insights and go-to-market strategies. The homogenization at the model level has driven the marginal value of merely understanding technology down to nearly zero. Meanwhile, the genuine barriers of vertical Agents happen to fall within the core strengths of these VC partners.

It is noteworthy that such career shifts have not taken place in seemingly grander fields such as model development, foundational AI enterprises or chip sectors. Even the most high-profile tracks have failed to attract partners to step into actual business operations. As the window for information arbitrage on deal sourcing narrows, they opt to participate in the industry in person rather than making more aggressive investments.

Signal 1 | Partner migration is highly concentrated in the "enterprise back-end"

Over the past 12 months, there have been at least seven clearly documented cases where partners from mainstream US dollar funds left their positions to start their own businesses:

• a16z GP Anjney Midha → Amp: AI compute tuning, first-round raise exceeding 130M

• Kleiner Perkins GP Joublin Mirzadegan → Roadrunner: AI-native CPQ, with a total funding of 27 million US dollars raised through seed and Series A funding

• a16z Partner Jack Soslow → Ciridae: an AI operation system for mid-sized enterprises, which secured $20 million in seed funding

• Founders Fund Partner Sam Blond → Monaco: an AI sales platform, which raised $50 million in Series B funding with a valuation exceeding $500 million

• Basis Set Venture Partner Ian Crosby → Synthetic: an AI accounting firm, which obtained $10 million in seed funding

• Rough Draft VP Sahir Jaggi → Forus: an AI prescription and insurance process platform, which closed a $160 million initial round and became a unicorn enterprise

• Twitchex CSO, angel investor with investments in over 50 companies ColinCarrier → OriginLab: a provider of world model training data, which raised $8 million in seed funding.

If we broaden the scope to include senior investors and serial entrepreneurs, and further add Cowboy Space co-founded by Baiju Bhatt, co-founder of Robinhood (raised $275 million in Series B funding with a valuation of $2 billion), Exponent founded by Charlie Cheever, co-founder of Quora, and Recursive Superintelligence led by Richard Socher, former Chief Scientist of Salesforce (post-money valuation of $4.65 billion), the figures will rise even higher.

What makes this list counterintuitive is not the fact that these people have left their previous positions, but that they have collectively steered clear of the most high-profile tracks such as large models, chips and consumer-oriented AI.

Signal 2 | Not Betting on Technology, But on “GTM × Category Insight”

Analyzing what these companies are offering: CPQ solutions, accounting bookkeeping, sales compensation management, prescription pricing, franchisee financial management, insurance brokerage services, sales process management and more. Almost all of them are enterprise back-end businesses that are mundane yet deeply accumulated, entail high switching costs and require mastery of industry-specific terminology. The real barriers to entry in these sectors have never lain in model selection, but in the tripartite combination of industry expertise, client relationships and sales rhythm control.

What core strengths do VC partners possess? They have built up insights into customer profiling, sales rhythm mastery and valuation benchmarking by evaluating 2,000 companies over a decade. These are precisely the most scarce resources for vertical Agent companies in their rapid expansion phase. The biggest bottleneck for founders of vertical Agent enterprises is usually not how to train Agents, but how to get involved in procurement negotiations, set reasonable pricing benchmarks and secure the first batch of reference clients. Former general partners hold inherent structural advantages in these three aspects.

In other words, they did not enter the industry merely drawn by the AI boom; instead, they realized their inherent strengths could for the first time be monetized on the seller side. If such core strengths can be revalued, what changes will take place across the entire VC value chain?

Signal 3 | The Sourcing Era Is Ending; The Underwriting Era Is Just Beginning

This is truly a third-tier pivotal signal. If the optimal strategy of frontline GPs has shifted from “finding the next Forus” to “establishing the next Forus independently”, the value chain of the VC industry is being rewritten.

Over the past five years, excess returns from AI investments mainly stemmed from sourcing advantages — being the first to spot OpenAI, Anthropic, or issue term sheets to Cursor. However, vertical Agent projects are overly saturated and fragmented, and the technical gaps are too narrow for GPs to maintain prominent sourcing edges. The likelihood of five high-caliber AI Sales Agent startup teams emerging simultaneously is far higher than that of five equally strong foundational AI company teams.

As information arbitrage opportunities in sourcing fade away, excess returns will flow back to two types of participants: funds equipped with industrial know-how to conduct professional underwriting, and partners with GTM leverage capable of launching startups on their own. The logical closed loop behind this trend is fully formed. The only remaining question is: why did this trend break out intensively between 2025 and 2026, rather than in 2023 or 2024?

Causes | Why Now?

Three major forces are taking effect simultaneously:

1. Technological inflection point. The marginal capability gaps at the model level are narrowing to zero, forcing vertical Agents to shift their competitive moat toward go-to-market strategies and category insights. In 2024, startups could still secure Series A funding by claiming “superior Agent orchestration technology”, yet this growth narrative has lost its appeal by 2026.

2. LP inflection point. Starting from Q3 2025, dollar limited partners have grown far less patient with reinvesting in foundational AI companies, and capital is flowing toward application-layer ventures with proven revenue streams and verifiable unit economics. Leading general partners can directly sense this shift in sentiment during LPAC meetings, at least six months earlier than such trends appear in secondary research reports.

3. Incentive inflection point. The carry profit realization timeline of top-tier GPs is no longer aligned with the market opportunity windows they identify. If a GP forecasts a seed-stage project they would traditionally back could surge tenfold in value within five years, while carry profits locked in the fund take eight years to vest and get split among a 20-person core team, the expected returns from personal direct investments outpace long-term career gains within institutional funds.

This checklist comes into being precisely as these three inflection points converge.

Impact | Who Gets Hit

Founders: The profile of real competitors has shifted. Over the past 18 months, vertical Agent entrepreneurs have mainly faced competition from peer founders and incumbent software companies. Going forward, this competitive landscape will welcome a new group of “ex-GP founders” who come equipped with limited partners (LPs), go-to-market (GTM) networks, client rosters and secondary valuation benchmarks. Their edge lies not in products, but in pre-established financing rhythms and sales networks. They can mobilize over 20 million US dollars at the seed round stage, and skip traditional demos to directly connect with strategic LPs in Series A financing. Ordinary founders need to rethink their differentiated positioning: superior technology is no longer sufficient. They must prove they possess irreplicable industrial expertise in specific niche sectors that even GPs cannot match, such as exclusive client connections, unique data flywheels, or hard-to-replicate founder-market fit within the industry. A concrete strategic takeaway is that instead of pursuing general horizontal Agents, it is wiser to firmly anchor the business in vertical industries overlooked by GPs.

Investors: The era of deal-sourcing arbitrage is drawing to an end. A clear inference is that if even firm partners are launching their own startups, the scarcity of merely discovering investment deals will inevitably decline, while the scarcity of accurately evaluating projects will rise sharply. The moat of early-stage funds is shifting from gaining early access to projects to identifying industry ceiling potentials ahead of others. This means capabilities once regarded as cost centers, including investment research teams, industry analysts and exit return models, are becoming core factors in investment valuation. Meanwhile, the transferable value of secondary valuation benchmarks and LP connections is growing increasingly prominent, and early-stage personal connections alone can no longer sustain the differentiated competitiveness of new funds. Mid-sized funds need to stay highly vigilant; under this new supply-side logic, funds that are neither large enough to provide underwriting support nor close enough to participate directly in industrial layout are in the most precarious position.

Platforms and Major Enterprises: These vertical Agent enterprises pose the most direct substitution threat to SaaS giants including Salesforce, ServiceNow and Workday within the next 24 months, and merger and acquisition activities are expected to surge notably starting from the second half of 2026.

Enterprise Clients: CIOs and CFOs are no longer faced with the simple choice of whether to adopt AI tools. Instead, they have to decide between purchasing AI modules developed by established incumbents or brand-new AI-native platforms built from scratch. The latter have already accumulated client cases with annual recurring revenue (ARR) ranging from 600,000 to 1 million US dollars.

Verification: Three Key Indicators Worth Tracking

1. Within 90 days, check if more than two current partners from a16z, Founders Fund, Kleiner Perkins and Sequoia resign to start their own businesses, with their business focus still centered on enterprise backend Agents. If this happens, it indicates a structural industry shift rather than isolated individual cases.

2. Verify whether the median post-money valuation of seed-stage vertical Agent enterprises exceeds 50 million US dollars before Q3 2026. This is a core threshold to judge whether general partners are systematically lifting the bottom valuation of this track.

3. Confirm if there emerges the first public case of seed financing initiated by former GPs with participation from their former venture capital funds within 90 days. This will officially signal the recognition of this new supply-side logic by limited partner capital.

It’s a beautiful line. It casts the investor as a talent scout with preternatural instincts, places the founder at the center of the story, and wraps every check in a narrative about betting on human potential. It’s gospel in Silicon Valley. It’s even more so in the official talking points of China’s top-tier VC firms. But here’s what we believe at SVTR: in today’s AI investing landscape, this phrase has degraded from a philosophy of judgment into the single biggest fig leaf covering the industry’s most systematic failures — and the bill is coming due at an alarming pace.

More precisely, “backing people, not ideas” is no longer a form of judgment. It’s a way of avoiding judgment. When an investor says this, they almost never have to explain what they actually saw. The phrase has three convenient properties: it conforms to industry norms, it doesn’t offend founders, and it requires zero specific argumentation. Naturally, it becomes the perfect cover for every bias, every résumé worship, every shortcut.

A Philosophy Misread for Four Decades

The VC industry didn’t always have a consensus on “backing people.” Tom Perkins at Kleiner Perkins looked at technology — is it proprietary? Is it significantly better than the alternatives? Don Valentine at Sequoia looked at markets — a big enough market, he believed, could carry even a mediocre team. His mid-1980s investment in Cisco happened precisely because peers thought the team was weak, but he was convinced the networking market would lift any decent team. Arthur Rock looked at people — he believed a great management team could navigate any specific opportunity.

Rock’s version won the culture war. The reason isn’t complicated: founder-centric narratives make for better marketing. For funds that are essentially selling capital to founders, “we believe in you“ is exactly what founders want to hear.

But “backing people” in today’s practice is no longer what Rock meant. A 2016 survey by Gompers, Gornall, Kaplan, and Strebulaev — covering 681 VC firms and 885 investors — found that 96% listed the team as a key factor, 56% considered it the single most important factor, and 17% of early-stage investors admitted they don’t use any financial metrics at all. An industry overwhelmingly reliant on qualitative judgment, yet without any clear articulation of what the criteria are or how to trace them back to outcomes. Scholars Andrew Zacharakis and G. Dale Meyer put it more bluntly: investors don’t actually understand their own decision-making process.

The Historical Tab for “Backing People”

Diag Davenport, an economist at Chicago Booth, ran the numbers on this “fuzzy humanism” in a 2022 study. Using a dataset of over 16,000 startups and more than $9 billion in committed capital, he trained machine learning models using only information available to investors at the time of their decisions, and asked one question: of the deals VCs actually funded, how many could have been identified ex ante as worse than simply putting the same money into public markets?

The answer: roughly half.

If you removed that half and redirected the capital to a public market index, the overall sample returns would have improved by 7 to 41 percentage points — representing over $900 million in avoidable losses.

The more telling finding was Davenport’s parallel experiment. He trained two models side by side: one to predict the best investments, one to predict the worst. The two models relied on entirely different signals. The model predicting great investments leaned heavily on product characteristics. The model predicting terrible investments leaned heavily on founder backgrounds. In other words: when investors made good decisions, they were actually studying the product. When they made bad decisions, they were studying the team even harder.

This isn’t news to the VC industry. But few people are willing to say it out loud: the more energy VCs spend on “backing people,” the more systematically — and expensively — they get it wrong.

Pricing Foundation Models by Résumé

Map this dynamic onto the global AI investing boom since 2023, and it reads like a controlled experiment. China and the U.S. ran nearly identical playbooks, almost in sync.

On the China side, there was Lightyear (光年之外). In early 2023, Wang Huiwen — known as “Meituan’s number two” — announced he was entering the foundation model space. No product. No complete team. He didn’t write code himself. Yet within months, he raised roughly $230 million at a $1 billion valuation, backed by top-tier USD funds and leading PE firms. The money wasn’t invested in a model or any concrete product thesis. It was invested in the three characters of his name. Months later, he stepped down for health reasons, and the company was absorbed back into Meituan. End of story.

On the Silicon Valley side, there was Inflection AI. Mustafa Suleyman (DeepMind co-founder) + Karén Simonyan + Reid Hoffman — that lineup secured $1.3 billion in funding at a $4 billion valuation, the largest early-stage AI round in North America that year. A year later, Microsoft executed a “non-acquisition acquisition,” pulling out the core team almost wholesale, leaving behind a corporate shell that was repurposed into a B2B API business. Adept and Character AI followed nearly identical scripts through 2024, with Amazon and Google as the respective acquirers.

Line these names up and the conclusion is hard to avoid: among the largest, most hotly pursued early-stage AI checks of 2023, a significant portion were not investments in companies at all. They were risk exposure management on a set of résumés. Even before the outcomes materialized, these deals would have been flagged red in Davenport’s model — their entire thesis was a CV.

The more systematic version played out in each market’s “foundation model” cohort. In China, it was the “AI Six Tigers” (六小虎): Moonshot AI, Zhipu, Baichuan, MiniMax, StepFun, and 01.AI. In the West, it was the ring beyond Anthropic and xAI: Mistral, Cohere, Reka, AI21, and others. What these companies had in common wasn’t product form, technical approach, or market positioning — it was the credential layer of their founders: Google Brain, DeepMind, OpenAI, Meta AI, Tsinghua, CMU, Microsoft Research. Within roughly a year, the vast majority crossed the $1 billion valuation threshold — most while product form was far from stable and business models remained unproven. The investment logic was remarkably candid: the people who “look like they should be able to build foundation models” are these people. Résumés are a reasonable weak signal. But they were being priced as a strong one.

The Dark Horses Outside the “Expected” List

Outside this résumé-driven curve, the most globally recognizable AI companies of the past two years happen to be the ones that least fit the “résumé-pricing model.”

In China, it’s DeepSeek. Founder Liang Wenfeng came from the quantitative hedge fund High-Flyer — not a typical Big Tech AI lab pedigree, and not on most investors’ initial mental list of “foundation model founders.” Yet DeepSeek, through V2, V3, and R1, pushed open-source model capabilities to the global frontier, even forcing Silicon Valley to recalibrate its assumptions about model costs, capabilities, and valuation curves.

In Silicon Valley, it’s Midjourney. Founder David Holz came from the world of haptic interaction, not mainstream AI research. The company took no VC money, did no fundraising PR, yet built one of the most successful consumer-facing AI businesses of this entire cycle on the strength of product and distribution alone.

And then there’s Cursor — not a “model lab alumni” narrative, not a founder-résumé media story, but a team that cut into a specific developer workflow and built an AI coding tool with genuinely compelling product experience.

These companies aren’t counterexamples to “backing people.” Quite the opposite — they demonstrate that the people truly worth backing are often not the ones with the shiniest résumés, but the ones who reveal distinctive judgment within a specific problem.

If the “back people” logic holds, what investors should actually be evaluating isn’t where someone came from, but:

• Why did they choose this problem?

• How do they define this problem?

• What counterintuitive but correct trade-offs have they made in the product?

• Did they see an opportunity the market hasn’t correctly named yet — and see it earlier than everyone else?

Strip away these questions, and “this person is strong” isn’t backing people. It’s backing labels.

What “Backing People” Actually Looks Like

Peter Thiel’s investment in Mark Zuckerberg is often simplified into “early talent recognition,” but contemporaneous accounts show that what Thiel actually valued wasn’t Zuckerberg as an abstract “person” — it was how he chose to approach the problem of online identity, and the specific form Facebook had already taken. Thiel himself said it plainly at a DealBook conference: he doesn’t evaluate people, ideas, business strategy, and technology separately. It’s a complex whole.

Nabeel Hyatt at Spark Capital makes this even more operational: he never decides whether to write a check by watching a founder present. Instead, he uses the product repeatedly, studies every decision embedded in it, and reverse-engineers the judgment, priorities, and problem-solving approach of the person behind it. Sam Altman, during his YC era, formalized it further as the “non-obvious brilliance of an idea” — not whether the founder is smart, but whether the idea is smartly choosing an underestimated problem. Mattia Bianchi at the Stockholm School of Economics and Roberto Verganti at Politecnico di Milano arrived at the same conclusion from the academic side: the most important creative act in entrepreneurship is not solving problems, but defining them.

Put these voices together and the conclusion is clear: the atomic unit of an investment is neither the founder nor the idea — it’s the fusion of the two. An investor who can’t discuss the team without discussing the problem is either investing in shallow pattern-matching or personal charisma. Both will reliably produce Davenport’s bottom half of “predictably bad investments.”

What’s Actually Scarce in AI Investing

In this AI cycle, capital is the last thing the market lacks. Nor is there a shortage of star founders being chased by that capital. What’s genuinely scarce is a more fundamental capability: the judgment to evaluate founders and problems as an integrated whole.

That’s the core reason SVTR continues to build its AI venture ecosystem.

What SVTR aims to document isn’t a collection of AI founder résumés, but a series of founder × problem × product form combinations.

Our AI venture database doesn’t just track founder backgrounds — it tracks the problems companies are tackling, product evolution, funding progress, competitive positioning, and capital relationships.

Our AI venture commentary doesn’t just chase headlines. It tries to answer: Why does this problem matter right now? Why might this company become a new variable? Why is the capital market repricing this direction?

And our AI venture events and programs aren’t about putting founders and investors in the same room. They’re about making sure that people with genuine problem judgment, product judgment, and industry insight get seen earlier and connected more precisely.

Going forward, we want it to be more than a database. We want it to be a signal source for AI investing:

• Helping founders get discovered by the right capital and industry partners.

• Helping investors identify teams with real problem judgment earlier.

• Helping industry players systematically understand AI startups’ technology, product, and commercialization positions.

We believe the next truly important AI companies may not emerge from the most glittering résumé lists. They’re more likely to come from people who are already seriously defining problems, seriously building products, and seriously navigating real markets. What we want to do is make those signals visible — sooner.

Description

​Chinese AI companies are entering a new phase of global expansion.

​The challenge is no longer only about building great products. It is also about building global trust, brand positioning, distribution channels, partnerships, and long-term market presence.

​SVTR and Zhiwai are hosting an intimate private dinner in Palo Alto for a small group of AI founders, investors, GTM leaders, creator-economy builders, and industry partners.

​This closed-door conversation will bring together leaders working across AI, technology, capital, media, and global go-to-market to discuss how Chinese and Asia-born AI companies can better reach global markets.

​The dinner is designed for candid exchange, high-signal networking, and meaningful conversations around AI globalization, brand building, and cross-border growth.

​What We’ll Discuss

• ​How Chinese AI companies can build global brand trust

• ​AI go-to-market strategies in Silicon Valley and beyond

• ​Creator economy as a global distribution channel

• ​Cross-border partnerships between founders, investors, and industry partners

• ​How Asia-born AI products can better position themselves in global markets

​Event Details

​Date: Monday, May 18, 2026
Time: 12:00–3:00 PM
Location: 151 California Ave, Palo Alto
Format: Private dinner / closed-door conversation
Capacity: Limited to 10 seats
Access: Invitation only. Registration is subject to approval.

​Who Should Attend

​This dinner is designed for AI founders, GTM leaders, investors, creator-economy entrepreneurs, and strategic partners who are actively thinking about global expansion, cross-border distribution, AI adoption, or ecosystem collaboration.

​Note

​Seats are intentionally limited to keep the conversation focused and meaningful. All registrations will be reviewed before approval.

The underlying logic is this: as model capabilities commoditize rapidly, the defensibility of the AI application layer is migrating from “intelligence” to “operational depth.” A clean, well-defined, easily standardized workflow means any player with access to the same model can replicate what you do within six months. A workflow stuffed with exceptions, compliance requirements, manual approvals, and legacy system integrations demands that a company spend years rebuilding its organization, data pipelines, and processes from scratch. That rebuilding, in itself, is the new moat.

I. Clean Workflows Have No Future

Over the past two years, Vertical AI has been one of the highest-consensus themes in Silicon Valley venture circles. But companies flying the same “Vertical AI” banner are splitting into two paths with entirely different destinies.

The first type sells a vertical AI product: injecting intelligence into a specific task — faster contract review, more accurate medical summaries, smarter customer service replies. These products demo beautifully, deploy quickly, and can reach $5–10M ARR faster than any prior generation of SaaS. But their problem is structural: when a task is clean enough, the risk low enough, and the integration with existing systems loose enough, it means a model upgrade lets competitors replicate you; a slightly technical customer builds it in-house; and if a frontier lab decides to enter, they simply overwrite you. The purer the task, the faster the replacement.

The second type is doing something fundamentally different. They are not selling “task intelligence” — they are taking over the entire workflow end-to-end: from data ingestion, system integration, approval processes, exception handling, and compliance frameworks, all the way to capturing the full budget a customer used to spend on human labor. On the surface, these companies appear to be building “AI for X.” In substance, they are rebuilding the operational chassis of an entire vertical industry. The former will be compressed into a feature within two to three years. The latter will become the infrastructure of their industry within ten.

What determines the fate of a Vertical AI company is not how smart its model is, but how deep into the dirty work it is willing to reach.

II. The Dirty Work Is the Moat

Using AI to handle auto loan collections doesn’t sound like a sexy thesis. But Salient is a company worth examining closely. What it does is deploy AI voice agents to call delinquent auto loan borrowers — a scenario locked down by three overlapping regulatory frameworks: the FDCPA, TCPA, and Reg F. A single violation can trigger a regulatory investigation. The AI must process federal and state regulations that overlap in real time across jurisdictions, negotiate payment arrangements during the call, comply with contact frequency limits, and know exactly when to hand the call back to a human agent. This is not “a task.” It is a tangled mess of operational, compliance, and technical problems all fused together.

But it is precisely this mess that lets Salient run a business where a human call costs over ten dollars and an AI call costs a fraction of that — and no one can easily take it away. Frontier labs won’t crouch down to chew through compliance and exception handling. Customer companies lack the engineering capacity to build production-grade AI in-house. Competitors, even with access to the same model, cannot bypass the thousands of edge-case workflows Salient has already accumulated.

The same logic keeps surfacing in corners that traditional VCs disdain:

• Medical pre-bill auditing: Charta Health navigates the labyrinthine relationships between payer rules, CPT codes, and denial patterns.

• Freight logistics dispatching: HappyRobot, Pallet, and Augment are replacing the endless phone calls and email coordination among carriers, shippers, and warehouses.

• Tax research: Blue J has entered a $145 billion market populated by 46,000 accounting firms, the vast majority with fewer than ten employees.

These markets are overlooked for two reasons. First, the workflows are dirty. Dirty means resistant to abstraction, resistant to standardization, resistant to the top-down imposition of “general AI capabilities.” Second, the market structure is fragmented. Buyers are scattered across thousands of small-to-midsize operators who generally lack internal engineering teams, lack architectural judgment, and lack the instinct that “technology should be built, not bought.”

These two characteristics are two sides of the same coin: the very thing that makes a market overlooked is also what makes it defensible.

III. TAM Isn’t Underestimated — It’s Measured with the Wrong Ruler

The deeper issue is that these markets aren’t actually small. They’ve just been measured with the wrong yardstick.

The mainstream VC approach to TAM estimation looks at how much an industry spends on software. Apply that ruler to “AI for property leasing,” “AI for medical billing,” or “AI for trucking dispatch,” and the numbers don’t look venture-scale. But this is a fundamentally misaligned ruler — in these markets, the money isn’t in the software budget. It’s in the labor budget, the outsourced services budget, the operational costs distributed across countless small positions.

A property management company might spend $30,000 a year on leasing software, but $300,000 on leasing staff salaries. When an AI product evolves from “assisting leasing agents” to “replacing leasing agents,” its sales target shifts from that $30,000 budget line to the $300,000 one. Push further — when the product expands to maintenance coordination, rent collection, and AI-guided tours — it reaches the entire property operations budget: a number north of $1 million. Same customer, same company; merely by deepening the product, the addressable budget expands by a factor of thirty.

EliseAI is the textbook case. It started as a leasing automation product with a $50,000 ACV — looking every bit like a ceiling-locked proptech play. But as it evolved from “assisting leasing” to “replacing leasing,” then extended into maintenance, rent collection, and AI-powered tours, it now serves one-eighth of all apartment units in America, with per-customer annual spend reaching the million-dollar range. The company itself has begun replicating the same playbook into healthcare’s $600 billion administrative cost pool. The market didn’t get bigger. The product revealed how big the market always was.

This mechanism also explains a critical paradox: why these markets can accommodate $10-billion companies yet won’t be directly targeted by OpenAI or Anthropic. Viewed through the software-spend lens, the market is too small for a frontier lab to justify mobilizing massive resources. Viewed through the labor-spend lens, the market is already large enough — but by the time it surfaces in public data as an “obvious opportunity,” the vertical player who has been grinding for five years will have already stacked up the data, integrations, and distribution.

The profitability model and organizational structure of frontier labs are simply not built for this work. They must continuously invest in the model frontier, must maximize token revenue (which directly conflicts with customers’ interest in moving toward agentic workflows), and cannot possibly build custom-grade applications for dozens of verticals in parallel. In most vertical markets, the AI application layer won’t be consumed top-down by labs. It will be won by vertical players willing to dig deep and trade focus for advantage.

IV. Implications for AI Investing

This thesis carries unmistakable implications for founders and investors navigating the AI wave.

For founders, the most dangerous temptation is to build something that “looks like AI”: a polished demo, a clean SaaS interface, a tidy workflow automation. These products will reach $5M ARR the fastest and get compressed into a feature the fastest. What’s truly worth building lies in directions that seem, at first glance, absurdly labor-intensive, hopelessly fragmented, and populated by buyers so unsophisticated you wonder if they’ll even sign a contract. It demands a temperament entirely different from the past decade of SaaS entrepreneurship: a willingness to make part of the company an “operations company” rather than purely a “software company,” a willingness to shoulder the dirty work of compliance, exceptions, and service delivery, and a willingness to accept lower margins than pure software in the early days in exchange for deeper customer embeddedness.

For investors, the screening mechanism needs an upgrade. The center of gravity in AI investing is shifting from “can the model do this?” to “is the workflow ugly enough?” A Vertical AI company worth backing should not have its TAM estimated by “software spending in the market” but by “the total labor and service costs the industry spends on this function.” A founder worth backing should not merely demonstrate the boundaries of model capability but the depth of their understanding of the industry’s dirty work: which exceptions account for 80% of the workload, which compliance requirements have been the Waterloo of every software company for the past decade, and which integrations truly prevent competitors from replicating.

It’s worth noting that this playbook carries an underappreciated divergence between the U.S. and China. The reason “replacing labor” drives such dramatic TAM expansion in the American market is that the multiplier between software budgets and labor budgets is enormous — per-customer labor spend is often ten times or more the software spend. China’s domestic labor cost structure compresses that multiplier, making per-customer expansion potential naturally narrower. But in reverse, Chinese teams have a natural lack of resistance to “doing operations, doing delivery, doing service” — precisely the part Silicon Valley founders are least willing to touch. We’re already seeing, within SVTR’s network, a cohort of Chinese-background teams bringing this operational resilience into America’s fragmented vertical markets. They may not have the strongest models, but they’re willing to go two steps further on the dirty work than their Silicon Valley competitors. In the Vertical AI generation, that willingness itself is the scarcest asset.

This is also the direction we continue to track: which industries have the most complete triangle of “heavy labor + high fragmentation + low tech DNA,” which teams truly understand the difference between “selling software” and “selling a system,” and which opportunities offer structural arbitrage between the U.S. and Chinese markets. The next batch of breakout companies will look, on the surface, less “AI” than many Silicon Valley darlings — but they will quietly take over the operational chassis of an entire industry.

Models win the demo. Wedges win the pilot. Systems win the market. The real bet in Vertical AI was never on the smartest end — it’s always been on the end most willing to do the dirty work.

This is not a sampling error. SVTR’s AI Deal Database, tracking the past eight weeks of data, shows that among rounds of $100 million or more, the share held by general-purpose AI application companies has fallen from 38% in Q3 2025 to 0% last week. Over the same period, the median valuation for research-stage seed rounds rose 2.4×, and for late-stage vertical Agent rounds, 1.7×.

The collapse of the middle layer deserves more attention than the boom at either end. (See chart at the end: Last Week’s Top 10 Funding Rounds.)

Signal 1 | Seed Rounds Are Being “Unicornified” by Paradigm Research

David Silver’s Ineffable Intelligence redefined what “early-stage betting” means with a single $1.1 billion seed round at a $5.1 billion valuation. Sequoia, Lightspeed, Index, Google, and Nvidia all came in simultaneously — a cap table that looks less like a typical seed syndicate and more like a Series C investor lineup transplanted to day one.

This wasn’t an isolated case. The same week, Standard Intelligence closed a $75 million seed at a $500 million post-money valuation, betting on computer-use models. On the China side, Wujie Power accumulated over $200 million in angel funding on a “native world model + reinforcement learning” thesis. What these three companies share isn’t a sector — it’s a narrative: they’re all betting on “post-LLM paradigms.”

When capital is willing to assign a $5 billion valuation on day one, it means the LP base has already accepted a new premise: the capability curve of general-purpose large language models is converging. The next leap won’t emerge from fine-tuning or RAG — it will require entirely new training paths (autonomous RL, world models, computer use). Once this premise takes hold, the scarcity of “founder credentials” gets rapidly financialized, while the moat narrative for downstream general-purpose application layers gets diluted in reverse.

Signal 2 | Vertical Agents Have Stopped Telling Growth Stories — They’re Telling Category Lock-In Stories

The back half of last week’s Top 10 features a dense cluster of large late-stage rounds forming the other pole: Rogo (investment banking AI / $160M Series D / $2B valuation), Aidoc (medical imaging / $150M Series E / 31 FDA clearances / 1,600+ hospitals), Hightouch (marketing data / $150M / $100M+ ARR / $2.75B valuation), Avoca (home services AI / $125M+ / 800+ enterprise clients), and Netomi (customer service / $110M / clients include Delta and the NBA). Add Legora (legal AI / $50M / $100M+ ARR / $5.6B valuation) from the same week, and that’s at least six companies exhibiting “category leader” characteristics within their respective vertical workflows.

These companies differ from “vertical AI startups” of twelve months ago in two critical dimensions. First, ARR is no longer a $10 million-level story — it’s a $100 million-level reality. Second, the valuation anchor no longer derives from “market size × penetration rate” but from “workflow embedding depth × customer switching cost.” Hightouch’s $2.75 billion valuation against $100M+ ARR implies a 27× multiple — a figure that, in SaaS history, corresponds to companies with established category leadership at Series E/F, not growth-stage companies that just crossed the $100M ARR line.

When capital is willing to pay that multiple, it means these companies are already being priced as the next generation of ServiceNow or Salesforce within “vertical SaaS + AI” — not as one more variant among the sea of general-purpose Copilots.

Signal 3 | The Middle Collapses: General-Purpose AI Products Are Collectively Absent from the Top 10

Zoom out, and not a single company in last week’s Top 10 is a horizontal Copilot, general-purpose Agent platform, or general-purpose LLM application-layer play. The closest candidate to a “middle layer” company might appear to be Parallel Web Systems ($100M Series B / $2B valuation), founded by former Twitter CEO Parag Agrawal — but the product is explicitly positioned as “web search infrastructure for AI agents,” serving downstream Agents as infrastructure rather than being an Agent product itself.

This is a quiet turning point. From 2024 through the first half of 2025, general-purpose Copilots, general-purpose Agent orchestration platforms, and horizontal LLM applications were the most crowded lanes in the AI primary market. Multiple companies secured $1 billion+ valuations at sub-$10M ARR. Last week, the absence of such companies from the Top 10 isn’t a valuation markdown — it’s a complete exit from the frame.

Why is the middle collapsing? Because it’s being squeezed from both directions simultaneously. From above, OpenAI, Anthropic, and Google are embedding general-purpose Agent capabilities directly into their flagship products (ChatGPT, Claude, Gemini), making it nearly impossible for Copilot startups to build moats beyond the model itself. From below, vertical Agent companies have locked in paying enterprise customers through workflow depth, keeping the switching cost of replacing a “general-purpose product” on the procurement list extremely low. When both directions are squeezing, capital simply stops betting on the middle.

Root Cause | Why Now

This repricing is, at its core, a second-order reaction to capability convergence at the LLM model layer.

In 2024, “model capability” itself was still a differentiating variable. General-purpose products could establish temporary leads through prompt engineering, RAG, or fine-tuning. By the second half of 2025, the gap between flagship models on general tasks was narrowing rapidly (GPT-5, Claude Opus 4.7, and Gemini 2.5 shuffle their rankings on standard benchmarks week to week), meaning “model capability delta” can no longer sustain a $1 billion valuation as a moat.

Under this premise, capital can only find genuine differentiation at the two extremes: either bet on the next paradigm (one that doesn’t sit on the LLM convergence curve), or bet on vertical workflows (which don’t compete directly on model capability). The middle layer is on neither the new paradigm nor the workflow-depth axis — so its pricing loses its anchor.

This also explains the tight temporal clustering of all three signals last week: they aren’t independent events, but the same underlying logic surfacing simultaneously across different cross-sections.

Implications | Who Gets Caught in This Repricing

For founders: The fundraising window for general-purpose AI application startups is closing fast. If you’re building a horizontal Copilot, a general-purpose Agent platform, or a prompt-engineering-based LLM application-layer product, you’ll need to make a choice within the next 12 months. Either converge your product depth toward a specific vertical workflow (healthcare, legal, finance, customer service, marketing — pick one), and let ARR, not user counts, do the talking. Or pivot toward a position closer to the model/data/training layer. Products stuck in the middle will struggle to raise their next round on the back of a “fast growth” narrative alone. Over the past 60 days, SVTR’s AI Deal Database shows that 100% of application-layer companies raising rounds of $50 million or more were vertically positioned.

For investors: Valuation frameworks need a rewrite. The old logic of valuing general-purpose AI applications through “DAU × ARPU” or “user growth rates” is breaking down, because these metrics no longer correspond to sustainable moats at the general-purpose layer. The new anchors need to return to two variables: on the vertical axis, “workflow embedding depth” (switching cost, regulatory barriers, data flywheels); on the model axis, “training-paradigm scarcity” (research credentials in RL, world models, computer use, and other non-LLM mainline directions). If a portfolio company has neither accumulated workflow depth nor placed a bet on a new paradigm, its valuation anchor needs to be proactively adjusted downward.

For Big Tech: OpenAI, Anthropic, and Google’s “product-layer squeeze” strategy was indirectly validated by last week’s capital flows. Their incentive to keep squeezing upward at the general-purpose layer will only strengthen.

For enterprise buyers: On procurement lists, the budget split between “general-purpose Copilots” and “vertical workflow Agents” will continue to diverge. The former increasingly folds into Big Tech subscriptions; the latter becomes a standalone line item.

Validation | Three Key Metrics Worth Tracking

Over the next 90 days, these three metrics can validate whether this signal holds:

1. Median seed valuation for general-purpose AI application-layer companies. Watch whether the median seed valuation for horizontal Copilots and general-purpose Agent platforms in Q1 2026 has declined 30%+ from Q3 2025. If so, the repricing thesis is confirmed.

2. Number of vertical AI companies entering the $2B+ valuation tier. Watch whether, within 90 days, three or more vertical AI companies with ARR above $50M enter the $2B+ valuation zone. If so, the category lock-in logic is being consistently priced in.

3. Whether a second “seed-round unicorn” appears. Watch whether another company with RL, world model, or computer-use research credentials emerges at a $500M+ seed valuation. If two or more appear, “academic-founder unicornification” has become a structural phenomenon rather than an outlier.

If all three metrics are confirmed within 90 days, last week’s Top 10 reflects not a weekly fluctuation, but the starting point of the 2026 AI primary market pricing framework.

SVTR’s AI Deal Database has completed retroactive tagging across three categories — “model research layer / general-purpose product layer / vertical workflow layer.” It can now filter comparable historical rounds and valuation multiples across the six verticals featured in last week’s Top 10 (investment banking, medical imaging, marketing, home services, customer service, legal) over the past 72 months, providing a reference map for founders and investors recalibrating their pricing frameworks this quarter.

That’s how many of last week’s largest AI funding rounds went to companies building things you can touch — robots, chips, reactors, trucks, energy systems. The top 10 companies tracked by the SVTR AI Venture Database raised nearly $10 billion in combined fresh funding (excluding Project Prometheus’s $10 billion round still in negotiation). The lone exception, VAST Data, counts labs training large physical models among its key clients.

Meanwhile, the hottest pure software Agent startups? They raised between $5 million and $20 million per round. Two orders of magnitude smaller.

This isn’t an AI downturn. It’s a reallocation of capital weight. And if you’re a founder, investor, or enterprise leader in the AI space, the implications are enormous.

What the Money Is Actually Saying

Let’s start with a simple exercise. Add up all the representative software Agent rounds from last week: Orkes ($60M), NeoCognition ($40M), Petual ($20M), Band ($17M), Coral ($12.5M), Brev ($3.3M), Zig.ai ($3M). The total comes to less than $150 million.

That’s roughly equal to the single round raised by Reliable Robotics — a company that flies cargo planes.

Let that sink in.

The capital isn’t just flowing toward physical AI. It’s flooding in across every layer of the stack, forming what looks like a complete industrial architecture for the first time.

The Three-Layer Stack of Physical AI

Think of physical AI as a three-layer system, and last week’s deals lit up all three independently:

The Simulation & Design Layer turns physical processes into computable, optimizable objects. Project Prometheus (in talks for $10B), CuspAI ($200M), and others are building the digital twins and design engines that make physical AI possible. These companies are often misclassified as “software” — but their customers pay based on manufacturing line and lab outcomes, not SaaS subscriptions.

The Compute & Infrastructure Layer provides the chips, data pipelines, and networks optimized specifically for physical AI workloads — not general-purpose GPU training. VAST Data ($1B Series F), EVAS Intelligence (CNY 1.5B), Sunrise (AI inference GPUs), NineTree (neuromorphic chips), and others are building this middle layer.

The Body & Application Layer is the most visible destination for capital. Last week alone, at least five humanoid and industrial robotics companies secured funding: Blue Energy ($380M), DeepWay ($310M Pre-IPO), Reliable Robotics ($160M), Pudu Robotics ($150M at ~$1B valuation), and Booster Robotics (nearly $100M).

Here’s what matters: these three layers now form a closed industrial chain. The simulation layer tells the compute layer “how to build this reactor.” The compute layer tells the body layer “how to drive this truck.” The body layer feeds real-world data back to the simulation layer.

A year ago, this chain was broken. Last week, it connected for the first time in terms of funding rhythm.

Signal 1: Physical AI Deals Are 10x Larger Than Software Agent Rounds

When you split last week’s funding by whether a company delivers physical actions or products, the gap is impossible to ignore:

• Physical AI: Median deal size ~$160M per round. Strip out mega-rounds (Prometheus, VAST), and the median still holds at $150M.

• Software Agents: Median deal size ~$17M per round. Even the largest round (Orkes, $60M) is smaller than the smallest physical AI round in the top 10.

The gap isn’t 2x or 3x. It’s more than an order of magnitude.

Capital no longer believes the “small check, massive upside” logic holds broadly for Agent startups. The market is repricing in real time.

Signal 2: A Complete Stack, With Independent Funding Across All Layers

If concentrated funding were limited to isolated outliers, it wouldn’t constitute a trend. But last week’s data shows full industrial depth. Capital is flowing independently into simulation, compute, and body layers — each attracting its own dedicated investors and its own deal logic.

That’s what separates a trend from an anomaly. When multiple layers of an industrial chain attract capital simultaneously and independently, you’re looking at a structural shift, not a hot sector.

Signal 3: The “Zero Marginal Cost” Narrative for Software AI Is Hitting a Wall

The core investment thesis behind software AI was elegant in its simplicity: model capabilities would keep breaking through, marginal costs would approach zero, and winners would take all. This narrative supported the sky-high valuations of OpenAI, Anthropic, and Mistral, as well as the “grab the entry point first, build the business later” logic of Agent startups.

That narrative began hitting three boundaries simultaneously in the second half of 2025:

First, the pace of generational leaps in model capability has slowed. Each new model delivers noticeably smaller increments over its predecessor.

Second, Agent startups have become overly homogenized. “GPT-wrapper plus workflow” products went from scarce to template within six months, blurring valuation anchors.

Third, inference costs are not approaching zero. GPU compute has become a hard constraint, making the marginal cost of one API call increasingly close to the marginal cost of delivering one physical service.

Capital won’t wait for the narrative to collapse naturally — it migrates first. And migration destinations must meet two conditions: unit economics must be measurable, and external barriers must not be controlled by large model companies.

The physical world meets both criteria perfectly:

• The unit gross margin of a robot, a truck, or a reactor is crystal clear.

• The data generated from motion, force feedback, and environmental closed loops is currently inaccessible to OpenAI and Anthropic.

• Chinese manufacturing capacity spillover and U.S. reshoring policy tailwinds create a rare window where both cost curves and policy incentives align.

This is the real pricing logic behind Project Prometheus’s $38B post-money valuation. Bezos isn’t investing in the next OpenAI. He’s investing in the AWS of the physical world — a base layer providing simulation and design infrastructure for all downstream vendors building physical AI.

Why Now?

The timing is no accident. Three forces are converging in the same window:

1. Scaling diminishing returns — Large models have hit marginal diminishing returns simultaneously across algorithms, compute, and data for the first time.

2. Hardware cost decline — U.S. manufacturing reshoring policies are overlapping with Chinese robotics production capacity spillover, pushing hardware BOM costs into a rapid decline channel.

3. GPU capex repricing — The market’s patience for “compute-consumption” business models has shortened dramatically.

Who Should Care

If You’re a Founder

Here’s the one question that matters: Does your product have a closed loop from physical action to physical result?

If not — if what you’re building can be summarized as “adding an Agent workflow on top of a large model” — last week’s data suggests your valuation ceiling is likely to keep falling in 2026, not rising.

Conversely, if you can embed AI into a specific physical scenario (a robot joint, a factory production line, a design process step), even a small team is more likely to secure patient capital.

One telling data point: companies that raised more than $100M last week have an average founding year of 2020. The market is willing to pay a premium for teams that have put in five years of hard work.

If You’re an Investor

Time to shift your investment paradigm from SaaS templates back to semiconductor and new energy templates. Physical AI rounds are characterized by large check sizes, long cycles, strong industrial chain ties, and less predictable exit timelines.

Funds that previously specialized in ARR models need to build three new due diligence capabilities:

• Hardware supply chains and manufacturing processes

• Energy and material structural bottlenecks

• Cross-border capacity scheduling

Watch out for a valuation trap: early-stage physical AI valuations are already rising on “strong storytelling,” but it will take 18-24 months to see if unit economics actually work. Don’t apply software AI pacing to these deals.

If You’re Big Tech

• Bezos is building a new flagship outside the OpenAI ecosystem via Prometheus.

• NVIDIA is locking in the compute consumption side of physical AI through investments in VAST and others.

• Microsoft and Google’s relative positions are becoming more nuanced — and potentially more vulnerable.

If You’re an Enterprise Customer

Manufacturing, logistics, energy, and medical devices are the four industries most likely to capture the first wave of “physical AI application dividends” in 2026. If you’re in one of these sectors, the time to start evaluating physical AI vendors is now.

Three Numbers to Watch Over the Next 90 Days

To confirm whether last week’s trend is structural or noise, track these three indicators:

1. The physical-to-software ratio at the $100M+ level. Count how many physical AI companies raise >$100M rounds vs. pure software Agent companies crossing the same threshold. If the gap widens to 5:1 or higher, this is not a one-off anomaly.

2. NVIDIA and TSMC partnership announcements. If 3+ partnerships explicitly target “physical simulation, robotics, industrial AI,” the upstream of the industrial chain has formally tilted.

3. VC thesis language. Track the frequency of “Physical AI,” “Embodied AI,” and “Physical Simulation” in theses or annual letters from top U.S. and Chinese VCs. If it doubles compared to the first half of 2025, LP consensus has formed and capital will concentrate further.

Last Week’s AI Funding Leaderboard

The SVTR AI Venture Database has updated complete data cards for all 6,000+ tracked companies, adding a new “Physical AI” tag. Users can filter more than 30 active targets by the three-layer structure for cross-sector comparison.

Let’s get one thing out of the way: the hardest unsolved problem in the agent economy is not making agents smarter. Models will keep improving — that’s the easy bet. The hard problem is everything around the model: the payment rails, the security layers, the trust protocols, the interoperability standards. Intelligence without infrastructure is a demo. Infrastructure is what turns it into a business.

Everyone’s talking about how smart AI agents are getting. But the real story right now isn’t about what agents can do — it’s about what they can’t do yet because the infrastructure doesn’t exist.

Payments. Security. Identity. Interoperability. These aren’t glamorous problems. They’re plumbing. And plumbing is exactly what separates a demo from a production deployment.

Over the past 12 months, we’ve tracked a new category of companies emerging in our AI venture database: agent middleware. A year ago, this category barely existed. Today, there are over a dozen funded startups building the connective tissue that agents need to operate in the real world — and the investor rosters now include Kleiner Perkins, a16z Scouts, Polychain.

This isn’t a coincidence. This is a sector being systematically priced in.

While embodied AI and coding agents continue absorbing large rounds, the consensus there is already high. The real “low consensus, high signal” space? Agent middleware.

Signal 1: Payment Rails — Agents Need Their Own Financial Infrastructure

On the surface, you might think existing payment APIs — Stripe, bank integrations — should work fine for agents. They don’t. Here’s why:

When the entity initiating a transaction shifts from a human to a machine, the entire trust model breaks. Traditional fraud detection is built around stolen credentials and unauthorized card swipes. But an agent operates under delegated authority — it can execute multiple transactions across environments without any human interruption. The attack surface moves from the checkout page to the system architecture itself.

The market has noticed. In the past 90 days alone:

• Ralio (London, $2.5M seed) — payment infrastructure for agents with built-in risk controls and audit trails

• Nava (New York, $8.3M seed) — verification, guardrails, and custodial execution for agent-managed capital

• Natural ($9.8M seed, late 2025) — explicitly positioned around “agentic payments”

As FinTech Weekly noted in April 2026: the market is already putting a price tag on the question of “can an agent legally spend money?” That’s the first hard prerequisite for the agent economy to move from experiment to production.

And it naturally leads to the next question: once an agent spends money, how do you know it hasn’t been hijacked?

Signal 2: Security & Auditability — The “Insider Threat” Problem, But for Agents

In December 2025, OWASP published its Agentic Applications Top 10 — the first-ever security risk framework specifically for autonomous AI agents, developed by over 100 researchers. The most striking concept: the “rogue agent” — a system that has proper authorization, appears to be functioning normally, but has deviated from its design intent.

This isn’t an external hack. It’s an internal mutation. The agent equivalent of an insider threat.

The industry has moved from “what can agents do?” to “what happens when agents go off the rails?” Capital is responding fast:

• Capsule Security (Tel Aviv, $7M seed) — runtime agent security and trust layers

• ActionAI (New York, $10M seed) — explainable, auditable, rollback-capable execution infrastructure

• Microsoft open-sourced its Agent Governance Toolkit in April 2026

• Ledger announced it’s extending its hardware security architecture from crypto to agent identity verification

And the attack side is already racking up real costs: security researchers disclosed in April 2026 that 26 LLM routers were secretly injecting malicious tool calls and stealing credentials. One case resulted in $500,000 stolen from a client wallet.

If payment rails solve “how does an agent spend money,” the security layer solves “can we trust how the agent spent it.” But even together, they’re not enough — when multiple agents need to collaborate on a task, you need something deeper.

Signal 3: Agent OS & Transaction Protocols — Who Defines the “Handshake” Between Agents?

When multiple agents need to work together, a fundamental question emerges: how do they discover each other, exchange data, and establish trust? No single company’s SDK solves this. What’s needed is a protocol layer — something analogous to HTTP or OAuth, but for agent-to-agent interactions.

Active companies in this space include:

• Keman Intelligence (Hangzhou, ~$10M, building an A2H marketplace; founders from Didi)

• Panfeng Intelligence (Hangzhou, tens of millions RMB, A2A-native e-commerce Agent OS; founders from DingTalk’s open platform)

• Creao AI (Hong Kong, $10M Pre-A, Agent OS platform; backed by Sequoia China and Hillhouse)

• Modern Relay (Barcelona/San Francisco, $3M, shared data layer connecting people, software, and agents)

A pattern worth noting: nearly every founding team comes from major platform companies — Didi, DingTalk, Meta, Amazon. They lived through the last generation of “platform ↔ developer” ecosystem construction, and they’re now trying to replicate that paradigm in an “agent ↔ agent” world.

One signal that’s easy to miss if you only read English-language tech media: Chinese teams are disproportionately active in A2A and A2H transaction protocol layers. This is a structural signal, not a trend piece.

Why Now?

Three timing factors converged in Q1–Q2 2026 to trigger this middleware funding wave:

1. Regulation got real. The EU AI Act’s high-risk obligations take effect in August 2026. Colorado’s AI Act kicks in June 2026. Enterprises deploying agents now must have auditable, traceable execution records — which is exactly what companies like Capsule Security and ActionAI provide.

2. Agent transaction volume became economically meaningful. The World Economic Forum, citing Adobe data, reported that AI-driven retail site traffic surged 805% year-over-year during Black Friday 2025, with agents helping drive over $22 billion in global online sales. When agents start materially participating in money flows, infrastructure needs stop being theoretical.

3. Security incidents went from hypothetical to actual. Poisoned LLM routers, stolen agent wallets — these are no longer red-team exercises. They’re incidents that happened. They’ve forced enterprise buyers and investors to reassess the prerequisites for agent deployment.

Who Should Care

If you’re building agent applications: Ask yourself — what infrastructure does your agent rely on for payments, security, and cross-agent collaboration? Until these pipes mature, your deployment ceiling with enterprise customers will be painfully obvious. Conversely, if you have platform-level experience (especially in payments, identity, or developer ecosystems), the entry window is short.

If you’re investing: Agent middleware is in a “seed-to-Series A” density phase. Valuations haven’t been inflated by application-layer narratives yet. But the window is closing fast — once one or two companies establish network effects in payments or security, late movers will face a much narrower path.

If you’re at a platform company: Microsoft and Ledger are already making moves, but history shows that platforms tend to ship “80% good enough” general solutions. Depth in vertical scenarios will still belong to startups.

If you’re an enterprise buyer: The most important question to ask your agent vendor isn’t “how smart is your agent?” It’s “what does the audit chain look like when your agent initiates a transaction?” and “what’s your rollback mechanism when agent behavior deviates?”

Three Metrics to Watch Over the Next 90 Days

1. Enterprise customer sign-ups for agent payment companies. Watch whether Ralio, Nava, and Natural can each sign 3+ paying enterprise customers within 90 days. This validates whether “agents need independent payment rails” is a real thesis — or whether existing payment infrastructure APIs are good enough.

2. EU AI Act compliance-driven security procurement. Before the August 2026 high-risk deadline, watch for European enterprises bulk-purchasing agent security and audit tools. If Capsule Security or peers show an ARR jump in Q2–Q3, it confirms regulation as the core catalyst for this sector.

3. Series A density in China’s A2A / Agent OS space. Keman Intelligence and Panfeng Intelligence are still at angel stage. If two or more Series A rounds close in this space within 90 days, it signals that China is independently forming its own agent transaction protocol ecosystem — not simply following Silicon Valley’s narrative.

These locations are not simply different urban districts or industrial zones. Each represents a distinct path of AI development within China’s broader innovation landscape. Taken together, they offer a more grounded way to understand Chinese AI—not only through the lens of individual companies or technical breakthroughs, but through the structure of the ecosystem itself.

After one week on the ground, our conclusion was clear: China’s AI competitiveness is increasingly not defined by a single company, a single model, or a single technological leap. It is being shaped by a more layered and coordinated innovation system.

Based on this field visit, SVTR summarizes six emerging models of Chinese AI innovation.

1.

Productization Innovation: AI Is Entering Real Products Faster

At AWE Shanghai, one trend was immediately visible: AI is moving rapidly into consumer products and intelligent devices.

From AI-powered robots and home appliances to vision-enabled hardware and smart terminals, the deployment of AI in China is no longer confined to conceptual demos or software interfaces. It is increasingly embodied in tangible products entering the market at speed.

What this reflects is not just technical progress, but a structural advantage. China’s manufacturing depth, hardware ecosystem, supply-chain coordination, and large domestic market together create a shorter path from prototype to commercialization. In the AI era, that matters enormously.

This means China should not be understood only as an application market for AI. It is also becoming one of the most important environments for AI productization and large-scale market validation.

2.

Park-Driven Innovation: China’s Industrial Parks Function as Systems

Suzhou Industrial Park highlighted a model with distinct Chinese characteristics: park-driven innovation.

In many markets, innovation clusters are formed primarily through spontaneous concentration of talent and capital. In China, industrial parks often play a broader and more active role. They do not simply offer office space. They combine industrial planning, policy support, capital guidance, incubation, and supply-chain coordination into an integrated platform.

For AI startups, the value of this model is practical and immediate. It lowers the threshold between technical capability and commercial deployment. It reduces friction in areas such as customer access, pilot opportunities, regulatory navigation, and ecosystem connection.

In this sense, Chinese industrial parks are better understood not as real estate carriers, but as infrastructure for organizing innovation resources.

One of the strongest impressions shared by overseas founders on this trip was that Chinese AI development is often not just company-led innovation, but regionally coordinated innovation.

3.

Startup Cluster Innovation: Ecosystem Density Still Matters

At Shanghai’s Xuhui ModelSpace, another important dynamic became clear: the efficiency created by high-density startup ecosystems.

In one concentrated environment, we saw AI startups, large-model teams, investors, researchers, and enterprise users interacting at high frequency. This kind of proximity produces real advantages: faster technical exchange, more efficient fundraising conversations, quicker product feedback loops, and stronger alignment between founders, capital, and market demand.

For AI, many of the most important opportunities still emerge through repeated offline interaction rather than purely digital connectivity. Teams that can more quickly access technical talent, customer signals, investor perspectives, and industrial resources tend to move faster in the early stages.

Several overseas entrepreneurs noted that this environment, in some respects, reminded them of Silicon Valley—not because the two ecosystems are identical, but because density itself creates momentum.

AI entrepreneurship is not only a competition of models and products. It is also a competition of ecosystem density.

4.

AI in the Physical World: Robotics and Intelligent Manufacturing Are Becoming Core Directions

At the Shanghai Maqiao Artificial Intelligence Innovation Experimental Zone, another shift came into focus: AI is moving beyond the digital layer and into the physical world.

What stood out there was not only software or model capability, but a broader industrial ecosystem tied to robotics, intelligent manufacturing, logistics automation, and core components. This points to an important next phase of AI development.

If the previous wave of AI was centered largely on information processing and software workflows, the next phase is likely to depend much more on how AI integrates with machines, factories, infrastructure, and real-world operations.

China has clear advantages in this domain. Its manufacturing base, engineering capabilities, supply-chain depth, and large industrial deployment environments create conditions that are difficult to replicate elsewhere.

For that reason, robotics and intelligent manufacturing should not be seen as peripheral application areas. They may become one of the strongest long-term growth directions for Chinese AI in the global market.

5.

Industrial Chain Cluster Innovation: Coordination Across the Value Chain Is a Strategic Strength

Zhangjiang AI Town illustrated another key feature of China’s AI ecosystem: industrial chain clustering.

What makes this model powerful is not dependence on a single standout company, but the co-location of multiple interdependent layers of capability. In one relatively concentrated geography, we observed AI chips, model platforms, robotics companies, smart hardware players, application teams, and supporting services beginning to form a more complete collaborative structure.

The significance of this kind of ecosystem is that it improves both efficiency and resilience. Innovation is no longer driven only by isolated excellence. It is accelerated by upstream and downstream coordination across R&D, testing, manufacturing, supply, channels, and customer integration.

For many international founders, this is one of China’s most compelling advantages. The attraction is not only market size, but the possibility of finding a workable cooperation chain more quickly and within a shorter operating radius.

As AI competition deepens, the real contest may increasingly be not between individual companies, but between ecosystems with different levels of coordination. From that perspective, industrial chain clusters represent one of the most important structural strengths of Chinese AI.

6.

International Innovation Platforms: Lin-gang Is Exploring a Global Interface for Chinese AI

The Lin-gang Special Area represented a more future-oriented model: the international innovation platform.

Compared with the other regions we visited, Lin-gang stood out for its emphasis on openness. Its development logic is closely tied to cross-border capital flows, global talent attraction, international business environments, and policy experimentation around global-facing innovation.

This matters because for overseas founders, entering China has never been a simple market-entry decision. It involves local networks, policy understanding, industrial partnerships, operational support, and long-term trust building. Regions that can absorb and connect international innovators more effectively will play a different role in the next stage of AI development.

In that sense, Lin-gang is not just serving local industrial growth. It is exploring how Chinese AI ecosystems can build more effective interfaces with the rest of the world.

That makes it not only a regional case study, but also a signal of where future institutional innovation may emerge.

7.

A Larger Judgment: China’s AI Advantage Is Becoming Systemic

Viewed together, these six models suggest a broader conclusion.

China’s AI development path is increasingly not about a single “point breakthrough.” It is about systemic innovation capacity.

That system includes the speed of productization, the organizing role of industrial parks, the efficiency created by high-density startup clusters, the integration of AI into physical industries, the coordination enabled by industrial-chain ecosystems, and the global-facing potential of more open innovation platforms.

This has broader implications for the global AI landscape.

A new division of labor may be taking shape. Frontier breakthroughs may continue to emerge from leading research centers around the world. But large-scale productization, industrial deployment, and real-world validation are increasingly tied to ecosystems with manufacturing depth, operational coordination, and policy support.

In that context, China’s role in AI is becoming clearer. Its importance lies not only in market size, and not only in application adoption. It lies in its ability to connect technology, industry, capital, infrastructure, and commercialization into a more integrated system.

The future of AI competition may therefore depend less on who has the strongest standalone model, and more on who can connect the strongest innovation systems.

One of the most important combinations to watch may be this: frontier technical capability from global research centers, combined with China’s industrialization and deployment capacity.

8.

Final Thought

This trip reinforced a view that is becoming harder to ignore: China’s AI competitiveness is increasingly a function of system capability.

Technology, manufacturing, capital, policy, industrial infrastructure, and international connectivity are no longer operating as separate layers. They are increasingly interacting as part of one larger engine.

For global founders, investors, and ecosystem builders, the opportunity ahead may not simply be to identify the next strong company. It may be to better understand how different AI systems work, where their strengths are complementary, and how new forms of cross-border cooperation can be built on top of that understanding.

That is one of the questions SVTR will continue to study through research, field observation, and long-term engagement across global AI ecosystems.

If you are a founder, investor, or ecosystem builder interested in joining SVTR’s global AI venture platform, you can apply here:

Living in Palo Alto, we have all witnessed the explosion of GenAI. We’ve seen the models get smarter, faster, and more efficient. But there is a shift happening that is impossible to ignore: AI is trying to leave the screen.

Whether it’s embodied intelligence, humanoid robotics, or next-gen smart terminals, the next wave of AI isn’t just about code—it’s about the physical world. And the moment your software needs a body, every software company eventually becomes a “supply chain company.”

This transition brings three massive walls that every founder hits:

1. Compute Costs: The burn rate to train and run models is crushing.

2. Hardware Realities: Prototyping is easy; mass production at unit economics is hell.

3. Capital Matching: Finding investors who understand both deep tech and hardware cycles.

The Solution: Bridging the Gap

I founded Silicon Valley Technology Review (SVTR) to solve exactly this. We believe the future belongs to those who can connect the “Brain” (Silicon Valley innovation) with the “Body” (Global Supply Chain).

That is why I am proud to announce the official launch of the 2026 SVTR AI Venture Bootcamp (Spring Cohort: AWE Edition).

In partnership with AWS and AWE (one of the world’s top 3 consumer electronics expos), we are looking for 15 high-potential Global AI Startups to join us on a 5-day deep-loop business journey in Shanghai this March.

This isn’t just a trade show. It’s a “fast-track” to land your technology in the world’s most robust manufacturing ecosystem.

Why You Should Join Us

We have secured resources that are typically out of reach for early-stage teams:

☁️ Premier Compute Support (Powered by AWS): We are offering a Grand Prize of $100,000 in AWS Credits, with guaranteed tiered support ($10k-$25k) for all selected participants.

🛂 Visa & Entry “Green Channel”: China travel can be complex. We provide government-level invitation letters and assist with Visa Fast-Track or Port Visas to ensure you and your team land smoothly.

🏭 The Supply Chain “Deep Dive”: We aren’t just sitting in a conference hall. We will take you deep into the Yangtze River Delta to visit “Lights-out Factories” and top ODM manufacturers, letting you evaluate mass-production costs on-site.

💰 “Silicon Valley Night” Capital Match: Private 1-on-1 roadshows with top global VC firms and CVCs who are actively deploying capital in this space.

Choose Your Track

We know founders are at different stages, so we have designed three distinct paths:

🔴 Track A (Exhibitor): For teams with physical demos. You get a free core booth at AWE (reaching 200k+ attendees) to find global orders.

🔵 Track B (Delegation): For executives who need to inspect the supply chain and network, but don’t need to exhibit.

⚪ Track C (Virtual): For pure Cloud/SaaS teams. You stay in the loop for the $100k AWS Credits and fundraising matching without leaving your desk.

The Clock is Ticking

We are stationed in the brand-new “Eastern Hub · Advanced Tech Zone” at AWE, competing alongside global leaders in Embodied AI and chips. But logistics for an international cohort take time.

Important Dates (Beijing Time):

• Feb 5: Priority Deadline for China Trip (Tracks A/B).

• Feb 15: Final Selection & Invitation Letters Issued.

• Mar 12-15: AWE Exhibition & Deep Networking in Shanghai.

We are limiting this cohort to 15 spots to ensure every founder gets personalized attention from our partners and investors.

If you are ready to give your AI a body, or if you simply need the compute power to build the next big thing, we want to see what you’re building.

Apply below before the Feb 5th priority deadline.

👉 Apply via Official Website

See you in Shanghai,

Min Liu

Founder, SVTR

Partner, PK Capital

With Stanford to our left and downtown Palo Alto to our right, SVTR (Silicon Valley Technology Review) and ZHIWAI GLOBAL brought 10 founders and investors from vastly different backgrounds into a single “folded” space.

There were no PPTs. We didn’t discuss Large Language Model architectures. For three hours, the conversation focused on one core question:

When the Google Search box becomes a ChatGPT dialogue box, who actually holds the power of traffic distribution?

From a GEO (Generative Engine Optimization) veteran who just flew back from Shenzhen, to Bay Area Vertical AI founders, to the mastermind behind millions in influencer revenue—together, they pieced together a combat map for AI traffic in 2026.

Who Controls the AI’s “Answer”?

While most people are still debating the theoretical definition of GEO, two players at the table shared their actual report cards. They represent the “Shovel Sellers” of the GEO gold rush.

“Only High-Authority Knowledge Bases”

Will Fu (Founder of LEAP UNION), one of SVTR’s earliest members, represents the hardcore operational power of the cross-border tech scene. As the exclusive GEO partner for top brands like Anker and Levoit, he cut straight to the core of GEO: It is no longer about keyword stuffing. It is about building “Citable Knowledge Bases.”

Will revealed that they utilize a proprietary knowledge base driver system, combined with a matrix of high-weight YouTube and Reddit accounts, to set specific KPIs that counter AI hallucinations. This strategy of “Proprietary Product + Top-Tier Brand Endorsement” proves that GEO has evolved from “black magic” into a standard specification for major tech giants.

“Governing Hallucination is a Business in Itself”

Jiaqi Chen (Founder of Topify AI), based in San Francisco, shared a figure that lit up the eyes of the investors in the room: an ARR (Annual Recurring Revenue) approaching $1 million.

Topify AI attacks a very specific angle—Negative Information & Hallucination Governance. When brands discover AI is “spouting nonsense” about them, Topify provides the tools and services to correct these “answers.” This isn’t just optimization; it is a battle for brand asset defense.

Content & Community: The “Fuel” for GEO

Beyond technical hacks, the group reached a second consensus: The smarter AI gets, the more it relies on “Real Human Evaluation.”

The “Hidden Champion” Niche

Lina (Founder of ZHIWAI GLOBAL), the event’s co-host, brought a shocking perspective. Running a “Hidden Champion” agency with hundreds of millions in revenue, she services top global hardware and biotech brands.

She shared: “AI also needs to ‘read’ things. Real discussions on Reddit and Zhihu, or unboxing videos from KOLs—these are the trustworthy ‘Sources’ that AI scrapes and cites.”

The Double-Engine of Reddit & IP

Bei Li (specializing in International AI Search Optimization) added practical strategies for Reddit: how to nurture accounts and how to navigate the edge of community rules. Meanwhile, Elaine Liu (Founder of Prosgrow.ai) used her own success as a “Silicon Valley Lifestyle Influencer” to demonstrate how Personal IP becomes a high-weight trusted source in the GEO era.

Without a high-quality content ecosystem as fuel, even the best GEO technology cannot run.

High-Ticket Tracks: The “Payers”

Why were architects and hardware founders at this table? Because they represent the “High-Value Scenarios” that GEO technology craves most.

The Customer Acquisition Anxiety of Verticals

Joshua (Co-founder of ADU Pilot), known as “Georgia Joshua,” is using Agents to save architects 90% of their workflow time. His primary concern was direct:

“When a homeowner asks ChatGPT, ‘Who is the best ADU designer in the Bay Area?’, can my name appear at the top?”

For high-ticket services like architecture, every precise exposure via GEO is valued in the thousands of dollars.

Hardware & The Storytelling Breakout

CY brought Traini (Silicon Valley’s first dog translation collar), representing the new demand in consumer electronics—how to make AI accurately understand and explain innovative products.

Meanwhile, Stella (AI+CRM Founder)—whose legendary background includes Oxford, investment banking, and doing food delivery in the Bay Area—and Lin Zhiyan (a hardware-focused investor) verified the urgent need for this traffic revolution from the perspectives of commercial relationships and capital.

Conclusion: A Microcosm of Silicon Valley Efficiency

Allen (Founder of SVTR) offered a retrospective at the end of the night:

“Tonight, this table represents a complete GEO industry closed loop: We have the Tool Builders (Will, Jiaqi), the Content Sources (Lina, Bei Li, Elaine), and the Vertical Scenario Owners who desperately need traffic monetization (Joshua, CY, Stella).”

This is exactly what Silicon Valley Technology Review (SVTR) aims to do. We are not just a media outlet; we are an ecosystem node combining “Community x Incubation x Information Source.”

In this era of uncertainty, global founders cannot rely on fighting alone. You need to find the node that connects “Technology, Content, and Capital.”

This dinner in Palo Alto was just the beginning.

Upcoming:

We will continue to organize “Small Scale, High Density” closed-door matchmaking sessions in the Bay Area. Themes in planning: AI Agent Commercialization, Consumer Electronics GEO Specials, and more.

If you are:

• A GEO/Traffic Operator with real battle-tested data

• A Vertical Track Builder looking for high-value growth

• An Investor focused on AI Infra and Applications

Don’t miss the next invite.

Subscribe to SVTR now to join our Global Innovation Network. For the next dinner, we’ll save a seat for you.

Six months ago, the caffeine-fueled debates were all about latency—who could autocomplete a function faster. Today, the smartest Founders, Infra Leads, and VCs have stopped looking at speed and started staring at a much deeper, structural problem:

Now that models are actually good, how do we rebuild the engineering system to handle them?

After spending the last two months talking to dozens of builders across the Bay Area, our conclusion is clear: 2025 is the year AI Coding graduates from a “Copilot” (an assistant) to an “Agentic System” (the primary engineer).

The logic behind this transition is far more brutal—and far more lucrative—than simply “writing code faster.”

I. The Threshold: Crossing the Line of Usability

Why is this happening now?

Bluntly put: The models stopped hallucinating quite so much. With the arrival of the latest reasoning models (like GPT-o, Claude 3.7, and DeepSeek-R1), we’ve seen a steep inflection point in three specific capabilities:

1. Instruction Following: LLMs can finally respect complex engineering constraints, not just generate a Python script that “looks” correct.

2. True Long Context: We are no longer treating the context window like a scarce resource. The “Lost in the Middle” phenomenon has been significantly mitigated.

3. Cross-File Reasoning: This was the biggest hurdle. Models have graduated from understanding a snippet to understanding a repository.

For top-tier founders, the excitement isn’t about code aesthetics. It’s about error rates dropping to a level where autonomy becomes viable. When reliability goes up, end-to-end tasks—automating bug fixes, running unit tests, and passing CI checks—move from science fiction to “table stakes.”

II. The Deep End: The “Dirty Work” is the New Moat

There is a massive misconception in the current market: Many people think AI Coding = An IDE + An LLM API.

They are wrong.

If the model is the brain, the engineering system is the nervous system. The real moat isn’t in the model itself (which is becoming a commodity); it lies in the unsexy, messy “Engineering Dirty Work”:

• Context Orchestration: You can’t just dump a repo into a prompt. The magic lies in how you use high-quality embeddings to surgically inject the exact 1% of relevant code into the window.

• AST (Abstract Syntax Tree) Mastery: Text generation is dangerous. Unless your agent understands the AST, it cannot guarantee that a refactor is logically safe.

• Deep Tooling Integration: An agent that can’t run a linter, parse a CI/CD error log, or trace a compilation failure is just a toy.

The verdict is in: The war won’t be won at the Model layer, but at the Infra layer. Whoever figures out how to elegantly handle this “dirty work” owns the platform.

III. Why Kuaishou? Why KAT-Coder?

This is why SVTR, in collaboration with Kuaishou (快手), is hosting a closed-door roundtable.

Silicon Valley has no shortage of great models or slick demos. But in the realm of AI Coding, industrial-scale reality is a scarce resource.

While Kuaishou is known globally for high-concurrency video algorithms, their Code LLM team (KAT-Coder) brings a perspective that is rare even in the Valley: They have tested this technology in “Hell Mode.”

We are talking about hundreds of thousands of private repositories, a chaotic mix of legacy languages, strict enterprise CI/CD flows, and a production environment where “hallucination” means “outage.”

On Dec 5, the core team is flying in to share the battle scars:

• Haotian Zhang, Head of Intelligent R&D Center

• Xuetie Zhang, Tech Strategy & Development

• Haixuan Gao, Head of Tech Operations Center

They will be joined by George, Founder of Artificial Analysis, to discuss the discrepancy between public benchmarks and real-world performance.

IV. The Invitation: Palo Alto, Dec 5

On the eve of this system rewrite, we don’t need more broad narratives about “The Future.” We need granular, high-bandwidth engineering discussions.

We are hosting a private lunch at Imperial Treasure (宝藏·烤鸭) in Palo Alto. No conferences, no badges. Just 15 people around a table, good food, and hard truths.

The Agenda:

• Model Breakthroughs: Capabilities vs. Benchmarks vs. Reality (feat. Artificial Analysis).

• KAT-Coder Roadmap: Performance tuning and inference optimization in massive repos.

• Agentic Workflow: How to build tooling that Agents can actually use.

• Investment Outlook: The next 12-24 months for AI DevTools.

The decade-long race to rewrite software engineering has just begun.

Join the Discussion

We have very limited seating (15 spots max) to ensure high-quality conversation.

Note: The event is strictly vetted. We prioritize Founders, CTOs, and active practitioners in the AI Coding/Infra space. Confirmation will be sent within 24 hours.

Global AI venture activity went full throttle this week. Across check sizes, category breadth, and founder backgrounds, the market showed extreme bifurcation: top-tier valuations ripping higher, unprecedented capital density in infra, model-layer gravity tilting toward giants, and application-layer companies pushing harder on commercialization.

At the same time, the capital map is crystallizing into a U.S.–Middle East–China tripod. The new AI founder archetype blends top research talent, big-tech operators, the defense ecosystem, and repeat entrepreneurs.

On the ground, community conversations focused on pragmatic themes—turning from pure “go-global” to a dual-track playbook and planning for “re-growth.” Stanford’s session on “How AI is reshaping VC,” plus next week’s talk on Asian LLMs, add fresh angles to this global AI sprint.

1) Notable Deals

Thinking Machines Lab — founded by former OpenAI CTO Mira Murati — is in early talks with multiple global blue-chip investors for a new equity round at a $50–60B target valuation. That’s more than 4× the $12B seed valuation the company reportedly closed in July 2025, which would place it firmly in the top tier of private AI companies by value.

Lovable (Stockholm, 2024) offers an AI dev platform that builds apps and websites through “vibe coding” in natural language. The company is negotiating a new round at a $6B target valuation; $215M raised to date.

Neura Robotics (Metzingen, 2019) develops cognitive and humanoid robots for manufacturing, logistics, and consumer use. Reports indicate a new raise of about $1.2B (≈ €1B) led by Tether under discussion, implying an €8–10B valuation (≈ $8.8–11B). Cumulative funding exceeds $200M.

Sources: SVTR.AI

2) VC Focus

Data from the AI venture tracker points to an unusually “barbell” week:

• Mega-rounds everywhere; capital density hit a quarterly high.
  – Anysphere closed a $2.3B Series C, resetting the ceiling for software startup valuations.
  – CHAOS ($510M Series D), Firmus ($327M), and others continued to lift the tide for models and infra.

• Breadth across the full stack: infra → models → apps.
  Energy, compute, chips, AI agents, legaltech, biotech, robotics, defense/security, ad systems, industrial automation—nearly every high-moat niche saw representative financings.

• Geo spread is clear: U.S. leads; Israel, China, Europe each break out.
  – U.S. accounted for >72% of deals; full-stack AI remains concentrated stateside.
  – Israel surged in security and systems-infra.
  – China broke out in embodied intelligence, DPU chips, and AR hardware.
  – Europe shined in AI storage and industrial automation (FMC, Euler, Forgis).

2.1 Infra Layer: Biggest rounds, hardest-core founders

Infra—especially chips, data centers, and power—was the most active band this week.

• High concentration of nine-figure rounds.

• Founder pedigrees skew to Stanford/MIT and Google/Meta/Broadcom alumni.

• Multiple valuations now pressing up against $3B.

2.2 Model Layer: Highest valuations; winner-takes-most

• Anysphere (Series C $2.3B, valuation approaching $30B) pulled up model-layer medians.

• Platform plays (e.g., Cursor, Parallel) drew heavy interest.

• Clear “top-heavy breakout, mid-tier stall” dynamic.

Early-stage model rounds are evaporating as capital concentrates in a small set of scale-capable platforms.

2.3 Application Layer: Complex structure; vertical and platform plays in parallel

The app layer saw the most companies and the widest spread of directions, with a shift from tool-like MVPs to commercially closed-loop platforms.

• Legal AI led app-layer check sizes: GC AI, Theo, Greenshoe each raised ~$50M-level rounds.

• Enterprise: automation, SaaS, marketing, and collaboration lit up in pockets.

• Healthcare/biotech AI: multiple $100M+ raises.

• Embodied intelligence/industrial robotics: notable momentum out of China.

• Defense/security: Israel-based startups continue to define the frontier.

3) Investor Landscape: U.S., China, and Sovereigns form a tripod

• U.S. top-tier firms still dominate global AI deployment of capital. a16z, Accel, Sequoia, Thrive, Kleiner Perkins show up frequently across infra, model platforms, and enterprise AI tools.

• Middle East sovereigns are now structural players. QIA and Mubadala exhibit strong appetite, with a tilt toward biotech, model platforms, and deep-tech.

• China capital concentrates on the compute + embodied-intelligence chain. Firms like Qiming Venture Partners and Zhangjiang Haoheng focus on chips, autonomous robotics, and industrial intelligence—while largely sitting out overseas model bets.

4) Founder Archetypes: Five clear paths

• Research-driven builders: academic + deep-tech backgrounds, biased to hard-tech (chips, biotech, medical, security infra). Core alma maters: MIT, Harvard, Oxford, Cambridge.

• Ex-big-tech operators: product-led with fast GTM; backgrounds from Google, Meta, Replit, McKinsey; focus on enterprise, SaaS, and legaltech—areas with short paths to revenue.

• Security/defense alumni: densest in Israel (e.g., Unit 8200, Akamai lineage); domains include cyber defense, data security, and mil-grade detection.

• Repeat founders: investors favor high-retention playbooks and proven exits. Exowatt, Red Queen, Bindwell are examples led by second-time CEOs.

• China-born founders: strong correlation with chips + robotics; roots in Huawei, Tsinghua, SenseTime; focus on embodied intelligence, vision operators, and industrial AI.

5) Community

On Nov 16, SVTR｜Silicon Valley Tech Review hosted a closed-door meetup in Shanghai (Mosu Space) on “Anti-Outbound & Re-Growth under the Silicon Valley AI wave.” Dozens of frontline AI founders, operators, and investors from the U.S. and China joined.

Panels dug into low-altitude economy, spatial computing, AGI content infra, service robots, AI digital employees, and autonomous mining—debating how to balance global vision with China execution: when to shift from single-market “outbound” to a dual-engine strategy; how to split “build abroad / amplify at home”; and how to design KPI systems and leverage capital + industry partners for the re-growth stage.

In open networking, teams and funds matched on financing cadence, industrial resources, and cross-border collaboration—several agreed on concrete follow-ups. SVTR will extend this thread via its AI Venture Database, AI Venture Forum, and AI Venture Camp to connect U.S.–China and global AI founder networks.

SVTR Incubator has officially secured AWS Startup Credits support. Projects backed by SVTR may receive at least $10,000 in credits, stackable with AWS Activate.

6) Essential Read

How AI is Reshaping VC — from “what to invest, how to invest, who invests” to “how to use AI to invest.”

At last night’s Stanford AI and Investment Series, Mohammad Rasouli (building an end-to-end AI platform for funds) hosted a high-density discussion with Som (Greylock), John Whaley (Inception Studio), Will (Prime Unicorn Index), and Salil (independent investor).

From model-vs-app dynamics to indexation of private markets to AI-native fund workflows (sourcing, DD, portfolio ops, and fundraising), the session offered a crisp snapshot of how AI is hitting VC at the core.

7) Next Week

Date & Time
Tue, Nov 18, 2025 — 4:30–5:50 PM (PST)

Venue
Bishop Auditorium, Stanford University
Address: 518 Memorial Way, Stanford, CA 94305

Topic
“The Latest in AI and Large Language Models in Asia.”
A talk by Dr. Tatsunori Hashimoto (Assistant Professor, Stanford CS) on the latest innovations across Asia in AI and LLMs—and how technology development and geopolitics together shape the next phase of model evolution. Dr. Hashimoto’s research focuses on building robust, trustworthy ML systems, especially long-tail behavior, out-of-distribution reasoning, and bias mitigation in complex models.

Host
Dr. Richard Dasher, Director, U.S.–Asia Technology Management Center (Stanford).

Editor’s note: For more details, visit Silicon Valley Tech Review at svtr.ai.

Grounded in multidimensional data and trend tracking from Silicon Valley Technology Review (SVTR)’s AI Venture Database, the AI Venture Outlook series systematically maps the latest financing structure of the global AI market—from capital allocation across the three-layer tech stack, to the generational and geographic profiles of startups, to shifts in VC strategies and the evolution of founder lineages. For founders and investors focused on medium-term opportunities in AI, this is not only a dynamic market guide but also a roadmap to understanding how future technologies will monetize.

I. Global AI Funding Overview

Heat persists while the structure diverges—incremental capital is returning to sectors and companies that both run viable businesses and build defensible moats.

• Q1 2025 peaked at $107.8B, driven primarily by mega-rounds in the model layer; Q2 normalized and pulled back; Q3 saw volume up and pricing/valuations up, highlighting strong market resilience.

• A sustained high deal count alongside a renewed rise in total dollars shows VCs haven’t left; capital is being allocated more rationally in the pursuit of “certainty.”

Volume in applications, dollars in models—the division of labor across the stack is increasingly clear.

• The application layer consistently accounts for ~75%–80% of deal count, directly reflecting market heat and spillover demand.

• Model and infrastructure layers show relatively steady deal counts, skewing toward “fewer but larger” rounds with a technology/capacity-build profile.

Peaks come from a few mega-rounds in the model layer, while steady, broad mid-sized rounds in applications drive the incremental growth. Structurally: “Dollars in models, increments in applications.” Capital keeps betting on foundational/model mega-projects, while go-to-market and commercialization sustain fundraising on the application side.

• Q1 2025: the model layer hit a single-quarter high of $81.9B, then eased over the next two quarters, yet remained well above the infrastructure and application layers.

• Applications: funding has trended up steadily since Q4 2024 and stayed elevated in Q3;

• Infrastructure: continued a modest upward trajectory.

II. Funding Profile of AI Startups

• Application layer: 501 deals — the broadest ecosystem, spanning the most sub-sectors.

• Model layer: 74 deals / highest capital — a handful of mega-rounds drive total raise elasticity.

• Infrastructure layer: 64 deals — funding fluctuates with compute/data cycles.

2.1 Infrastructure Layer: Capital still targets the supply-side bottlenecks—compute first, then data, lastly frontier algorithms

• Compute is the absolute core (~43 deals; highest share of dollars), accounting for the bulk of infrastructure-layer funding.

• Data comes next (~23 deals), focused on data generation, governance, and quality improvement.

• Algorithms take a very small share, mostly frontier research and exploratory bets.

2.2 Model Layer: the base (foundation models) captures capital; the ecosystem (agents/verticals) drives deal count

• Foundation models: few deals (~10) but dominant in total dollars, continuing to absorb the mega-rounds.

• Agents (~28) and vertical models (~21): more active in deal count, with mid-sized tickets.

• Optimization/Evaluation (~11): smaller in number but emerging as must-have infrastructure.

2.3 Application Layer: B2B strong scenarios (cost-out & productivity, risk/compliance, high ARPU) keep attracting capital; industrial and decarbonization enter a near-term window

• Life sciences, enterprise services, robotics, financial services, and security/compliance sit in the upper-right quadrant (high deal volume and large round sizes).

• “Dual-carbon”/decarbonization has fewer deals but larger tickets—high capital density.

• EdTech, legal tech, and defense/public safety remain in an early scale-up phase.

2.4 Founding Year: Capital tilts toward more mature, revenue-driving companies; newer cohorts are building momentum

• ≤ 2021 cohort: 240 deals, the largest contributor to total capital.

• 2023 cohort: 130 deals, notably active.

• 2024 cohort: 136 deals, high in count but with smaller ticket sizes.

• 2025 cohort: 55 deals, still in early validation.

2.5 Geographic Distribution: a stable U.S. unipolar landscape, with others forming a long-tail

• United States: 388 deals (~61%), commanding the clear high ground in both deal count and capital.

• China: 64; United Kingdom: 47; Germany: 26; France: 10.

• All others combined: 80 — a dispersed, long-tail pattern.

2.6 Round Size: A few mega-rounds + mid-sized core + small long tail

• $10–$100M mid-sized rounds: 326 deals — the mainstream by count.

• >$100M mega-rounds: 59 deals — contribute the vast majority of capital.

• ≤$10M early rounds: 220 deals — a stable long-tail supply.

III. AI Investors

AI investing has entered a new normal where broad early-stage coverage and concentrated bets on leaders coexist. YC supplies massive early-stage deal flow, while top USD VCs and strategic corporates pick up momentum in mid-to-late stages. The top ten are mostly North American; corporate capital (e.g., NVIDIA) is active alongside accelerators/VCs.

• Y Combinator (~40 deals) — far ahead, leading early-stage flow with many small checks.

• Tier 2: General Catalyst (~25), Andreessen Horowitz (~22), Insight Partners (~20).

• Tier 3: Lightspeed (~16), Sequoia (~15), Khosla Ventures (~15), NVIDIA (~13), Accel (~13), Bessemer Venture Partners (~14).

IV. AI Founders

The AI founder talent pipeline shows a Big Tech backbone with academia in parallel—a dual engine of engineering execution and frontier research.

• Google leads by a wide margin (~38–40), followed by Meta (~25–30) and Microsoft (~24–26). Amazon and Intel (~16–20) trail closely, underscoring strong supply of platform-scale engineering experience.

• MIT, Stanford, Cambridge, UC Berkeley, and other academic institutions contribute a meaningful share, signaling an active lab-to-startup path.

• Consulting backgrounds (e.g., McKinsey) are a smaller slice, but add commercialization and go-to-market strength as a complementary capability.

The Bay Area and the Boston corridor remain the primary “talent pools” for AI entrepreneurship, with elite alumni networks creating strong positive externalities for early team formation and fundraising. Overall, the talent mix is “U.S.-school led, complemented by top UK/China universities.”

• Stanford ranks first (~55).

• Harvard is second (40+).

• MIT is third (~30).

• Columbia, UC Berkeley, and UPenn/Wharton form the second tier (~14–22).

• Oxford, Cambridge, IIT, and Tsinghua have a steady presence, though materially smaller than U.S. schools.

V. AI Quarterly Funding Top 10

These are not conceptual showcases, but “in-motion products” that have already undergone validation, pilot deployment, or early commercialization—driven by simultaneous advances in both technology and business execution.

The event revealed not just the breadth of AI applications, but also three foundational trends shaping the next phase of AI adoption:

• Multi-agent collaboration

• Human-AI shared control systems

• AI-native interaction paradigm shifts

Our Thesis:

What emerged from this showcase is not incremental tech or model improvements—but a structural rethinking of the application layer. Multi-agent systems, human-like collaboration (such as remote operation and robotic task execution), and AI-native interaction logic are becoming defining certainties.

Three notable paths to commercialization:

• Agent coordination + autonomous operations: “Human-Machine-Task” triadic architectures are now deployable across security, robotics, and auditing scenarios.

• Rewriting high-frequency entry points: AI-native products are re-architecting foundational functions in messaging and compliance, breaking away from traditional plugin models.

• High reusability + value density scenarios prioritized: Hospital logistics and smart prosthetics offer clear economic upside and repeatable models, making them ideal for early AI scale-up.

Showcase Highlights

01｜Netra Security – From Tooling to Operating System: Redefining Data Security Automation

Netra tackles the inefficiencies of traditional DLP (Data Loss Prevention) solutions—often complex, ineffective, and underused—by building a multi-agent, end-to-end security operations platform. Supervised agents handle modular tasks, using a Zero Trust framework to autonomously acquire context and execute full-cycle automation—from detection to response and remediation.

• Ultra-low deployment barrier: 5–10 min agent install; go-live within 4 hours for small teams; full enterprise rollout in 2–3 days.

• Initial customers: Data-heavy, high-velocity industries like tech and finance. With data breach costs averaging $60K/event, ROI arrives in 6–12 months.

• Key differentiator: Less about “stronger tools” and more about “lighter services”—built for even junior IT staff.

• Team: Founded by the former head of security at Alibaba Cloud, with core team members from FireEye and AWS. Team of 12, product live for 9 months.

02｜AIConform – Turning CPA Audits from Art into System

With AI regulations tightening (e.g., EU AI Act), ~20,000 of the 60,000 CPA firms in the U.S. face mounting pressure in assurance services. AIConform offers an AI-powered, workflow-configurable compliance platform that automates evidence collection, document generation, and provides style-learning-assisted review—supporting audit sampling, traceability, and anomaly detection.

• Not just a toolbox, but a scalable compliance productivity platform.

• Exploring M&A of small CPA firms to create tool + service integration and consolidate a highly fragmented market.

• Team: Founders with academic + consulting backgrounds in AI privacy, bias, and compliance; advisors include experienced CPAs across SOC2, ISO27001, and ISO42001.

03｜Chat10000 – Building an AI-Native Messaging Protocol from Scratch

Unlike AI-enhanced IM tools, Chat10000 reimagines messaging from the ground up with AI-first design. Interaction between users and AI is seamless: auto-summarizing conversations, generating to-dos, predictive replies, and memory updates—all embedded within the message stream, not tacked on as plugins.

• Multi-tiered memory system (private/public), integrating emails, X (Twitter), WeChat, etc.

• Goal: 10,000 paying users within 12 months.

• Positioning: Not “passive transmission” like legacy IMs, but “conversation = structure; communication = collaboration.”

• Team: Compact team of 4–5; serial founder with >10 projects; tech lead from IBM/Amazon with AI system deployment experience.

04｜OpenAgents – Making AI Agents Collaborate Like Gamers on Discord

Built by Acenta, OpenAgents turns agents from isolated units into community-based collaborators. Developers can spin up Agent Communities with one click. Agents can discover, join, and communicate freely, powered by a real-time collaboration engine that keeps latency in the millisecond range.

• Example scenarios: HR interview agent alliance (multi-round screening from a single submission), live market intelligence aggregation, and team project workflows.

• Partnering with Columbia, University of Tokyo, and others to co-develop benchmarks and plugin ecosystems for multi-agent coordination.

• Founder: Raphael, former Amazon AI scientist, led the first Dialog2API Admin Agent and its integration into Titan/Nova models. Published in Networks; helped deploy multi-agent systems at large tech firms.

05｜Emancro – Hospital Logistics Robot Deployed at U.S. Medical Groups

Emancro’s dual-arm hospital logistics robot is designed for high-ROI scenarios like delivering medications and lab samples. With a suction + gripper system and a VLA (Vision-Language-Action) framework, it achieves 97% task success and 150 packages/hour efficiency—even in complex environments with minimal recalibration.

• Completed a 3-week pilot at Mayo Clinic; aiming for full deployment in 2026.

• Priced at $80K–$120K per year per unit; ROI via reduced waste, risk mitigation, and compliance automation.

• Team: Berkeley-trained founders with deep robotics/VLA expertise; strong execution in dual-arm planning and regulatory integration; exploring Chinese industrial partnerships.

06｜Anthropilot – Remote Takeover Platform for Robotic Edge Cases

To address robots’ frequent failures in real-world long-tail tasks, Anthropilot offers a three-tier remote takeover framework:

• T1: Template-driven micro-control for mainstream users

• T2: Moderate control for task-specific hardware

• T3: Expert-level remote ops (e.g., healthcare, hazardous zones)

The platform abstracts hardware differences across robot vendors, allowing global “gig workers” to control robots without deep technical familiarity. It’s also exploring insurance partnerships for human-AI shared safety assurance.

• Team: Deep roots in middleware, real-time distributed systems, and AI decision-making; alumni networks from Cornell, CMU; advisors from top robotics firms.

• Operates a dual-node (US + China) structure for data localization and ecosystem alignment.

07｜Smart Prosthetic Knee – AI-Driven Intent Recognition at Breakthrough Cost

Traditional powered prosthetics often lag in response or fail to adapt to sudden movement, making hydraulic models more popular. This smart knee uses IMU-based motion prediction to adapt in milliseconds—handling variable treadmills, emergency stops, and slope walking with ease.

• Fully self-developed hardware; cost kept under ¥50,000 (~$6,800), vs. $20K–$30K for top models.

• Go-to-market via China first, with “SaaS + hardware” feedback loop and global compliance in parallel.

• Team: Founder is a PhD from a Dutch university and Stanford visiting scholar, with long-standing work in biomechanics/exoskeletons. Backed by two senior professors. Chinese partners in rehab and fitting networks secured. Prototype completed; 4–5 person team expansion in progress.

Closing Thoughts

1. A Structural Rewrite Is Underway
   These teams aren’t just refining tech—they’re restructuring the application layer around coordination, reusability, and composability.

2. Three Foundational Pillars Are Emerging

   • Multi-agent systems: A new default for tackling complex tasks

   • Human-in-the-loop control: Especially in weakly supervised or high-risk environments

   • AI-native interfaces: Moving beyond plugins to protocol-level redesign

3. Smart Market Entry Strategies
   Many teams are entering sectors with high reuse potential and clear value—hospital logistics, compliance, prosthetics—ensuring scalable growth paths.

4. Faster Lab-to-Market Conversions
   Multiple teams are already in pilots, revenue stages, or pricing iterations. The productization cycle is clearly accelerating.

Contact & Access

To request pitch decks, pilot data, user interviews, or live demos, contact Kerry from SVTR (kerry@svtrai.com) to apply for Dataroom access and schedule follow-up.

By focusing on a “large model + infrastructure + application platform” core, the AI ecosystem is rapidly maturing. Infrastructure financing jumped 87% quarter-over-quarter, making compute and data foundations the hottest new frontier. Meanwhile, more than half of Q2’s funded startups were founded after ChatGPT’s debut—founders now include FAANG engineers, interdisciplinary scientists, and vertical-market experts. Top global funds like Sequoia, a16z, and Y Combinator doubled down, ushering AI investing into a global, diversified new phase.

1. Global AI Funding Overview

In H1 2025, global AI startup funding heated up further: 1,151 deals (+41% YoY) raising $148.1 billion (+247% YoY). While Q2’s total dipped from Q1 (which was buoyed by OpenAI’s $40 billion raise), deal count hit a new record, underscoring investors’ enduring confidence in AI.

• Record Deal Volume. Since Q4 2024, quarterly deal counts have stayed above 500; in Q2 2025 they topped 630.

• Funding Concentration Fluctuates. Q1 saw nearly $99.8 billion—its highest since early 2023—driven by mega-rounds in the model layer.

• Diffusion Effect. In Q2, average deal size scaled back 52% QoQ as funding spread to more early- and mid-stage startups.

• Daily Averages Soar. H1 2025 averaged $820 million funded per day—over three times H1 2024’s $230 million—and saw 6.4 startups secure financing daily.

2. Portrait of AI Startup Funding

2.1 By Technology Layer

In Q2 2025, 633 AI funding events raised $48.3 billion. Generative applications still dominated deal count (~80%), but the tide is turning: infrastructure funding surged, nearly matching applications in total dollars.

• Applications (≈80% of deals). Tools, platforms, and vertical AI services continue to attract the most rounds.

• Infrastructure. Compute and data foundations saw an 87% jump QoQ.

• Model Layer. Funding fell 76% QoQ, though average round sizes remain 5–8× larger than other segments.

2.2 Infrastructure Layer

• Data Services (32 deals, ≈ $15 billion). Demand exploded for training datasets, privacy-preserving compute, and compliance tooling.

• Compute (31 deals, ≈ $2 billion). Cloud GPU clusters, liquid-cooling servers, and chiplet interconnect accelerators led the way.

• Algorithm Frameworks (1 deal, <$100 million). Next-gen distributed schedulers and compiler optimizers began attracting early capital.

2.3 Model Layer

Large model funding cooled: average deal size fell 40% QoQ, yet remains the highest among all segments. Meanwhile, verticalized models and intelligent agents racked up smaller, more frequent raises, driving deal count.

2.4 Application Layer

• Life Sciences & Enterprise Services. Life sciences startups led average deal size 3× above the mean.

• Defense & Security. Policy tailwinds pushed per-round investments above $500 million, placing defense companies in the top three by dollars raised.

2.5 Founding Cohort

A whopping 107 “post-ChatGPT” ventures (founded within the last year) secured funding—17% of total deals. While headline-grabbing unicorn raises grab headlines, most of these newcomers are scaling carefully from seed- and Series A rounds under $100 million.

2.6 Geographic Distribution

• United States. Still dominant, capturing 87% of capital and over 400 deals—1.3× last year’s Q2 volume.

• China & UK. Tied for second in deal count. Notably, over 100 founders of Q2 rounds have Chinese heritage overseas, reflecting active global diasporas.

2.7 Deal Sizes

• $10 M–$100 M. Accounted for 60% of all rounds.

• Mega-Rounds. 67 deals exceeded $100 million; 7 ever-larger raises topped $1 billion.

  

3. Top AI Investors

In Q2 2025, the ten most active AI investors collectively led 252 deals, spanning seed to Series D, with a strong tilt toward infrastructure and enterprise services.

• Frequency Up. YC, a16z, and Sequoia increased quarterly deal flow by 59% on average.

• New Entrants. Insight Partners, Accel, and Benchmark cracked the top ten, signaling more large-scale funds entering AI.

• Shift in Focus. Compared to Q1, more capital trended toward security/compliance and enterprise Copilot offerings, while mega-round infrastructure deals were confined to a handful of mega-funds.

• Bigger Cheques. Top 10 average check size jumped 29% QoQ to $22 million; 14 rounds saw syndicates write checks above $50 million.

• Regional Diversification. U.S. share of deals dipped from 72% to 68%, with Europe (France, UK, Germany) plus Israel rising to 22%. Early-stage projects also emerged in India and Latin America.

  

4. AI Founders

Q2 funded founders encompassed over 1,200 individuals—up markedly from Q1. Two key trends emerged:

1. Big Tech Alumni. Google, Microsoft, and MIT labs remained the top talent feeders (33% combined); Meta slipped to fourth, hinting at a slowdown in social-platform talent exodus.

2. Elite Academia. MIT grads now trail only Stanford, followed by Harvard and UC Berkeley; IITs and Ivy League schools (Cornell, Yale) continue to contribute steadily.

   

5. Top 10 Q2 Financing Highlights

1. Scale AI (2016, San Francisco) raised $14.3 billion from Meta at a $30 billion valuation—Meta’s largest external investment ever.

2. Anduril (2017, Costa Mesa) closed a $2.5 billion D-round co-led by Founders Fund, valuing the defense AI specialist at $30.5 billion.

3. Thinking Machines Lab (2024, San Francisco) raised $2 billion seed from a16z, Conviction Partners at a $10 billion valuation.

4. Safe Superintelligence (SSI) (2024, Palo Alto & Tel Aviv) secured $2 billion Series A from Greenoaks at a $32 billion valuation.

5. TAE Technologies (1998, Foothill Ranch) added $150 million growth funding from Google, Chevron, NEA.

6. Grammarly (2009, San Francisco) completed a $1 billion non-dilutive round with General Catalyst.

7. Commonwealth Fusion Systems (2018, Devens) closed $1 billion from a major cloud provider.

8. Anysphere (2022, San Francisco) raised $900 million Series B led by Thrive, a16z, Accel, DST.

9. Helsing (2021, Munich) secured $693 million Series D from Prima Materia, General Catalyst, Saab, Accel.

10. TerraPower (2006, Bellevue) raised $650 million from NVentures, Bill Gates, HD Hyundai.

About SVTR
Silicon Valley Technology Review leverages our AI Venture Database, weekly AI investor roundtables, and intensive “AI Venture Camp” to forge a community-driven incubator. Visit us on SVTR.AI for deeper analysis, or add us on WeChat: pkcapital2023.

Over the past 30 days Stanford’s campus once again morphed into a micro-cosm of the global hard-tech AI boom. More than 100 venture projects were pitched across internal demo days and local accelerators, spanning ten high-momentum tracks from healthcare and robotics to climate tech and consumer AI. Fund-raising data confirm that capital is rotating back to “AI + deep-tech” themes: global digital-health investment hit $6.3 billion in Q1 2025, 60 % of which targeted AI solutions , while Robotics-as-a-Service (RaaS) is forecast to expand 25 % CAGR to $125 billion by 2034 (marketresearchfuture.com).

SVTR analysts mapped each Stanford pitch to one of ten sectors, counted the companies, and visualised the result. “Healthcare Services”, “Developer Tools”, “GTM/Customer Ops” and “EdTech & XR” jointly account for one-third of the cohort, signalling that applied AI still captures the imagination of technical founders even as infrastructure and hardware regain investor appetite.

Key take-aways:

2、Methodology

• Primary source: the master list of 100 + Stanford pitch companies shared during May–June demo events.

• Secondary data: SVTR.AI venture database, Galen Growth digital-health tracker, Market Research Future (RaaS), Preqin 2025 VC outlook and public policy documents (IRA, CBAM) (galengrowth.com, marketresearchfuture.com, preqin.com, taxation-customs.ec.europa.eu).

• Classification: ten tracks were defined to align with SVTR’s broader AI taxonomy. Counts were verified manually (see bar chart).

3、Sector Deep-Dives

Each subsection summarises market context, notable Stanford projects, illustrative deal highlights and SVTR commentary.

3.1 Healthcare Services (10 projects)

Market pulse

Global digital-health funding rebounded to $6.3 bn in Q1 2025; AI-enabled care delivery and clinical documentation captured 60 % of dollars, up 19 ppts YoY. Epic-integrated ambient scribe platforms dominate mega-deals (galengrowth.com). CMS has extended RPM reimbursement codes into 2025, lowering pay-lag risk.

Representative Stanford teams

• Walnot – wireless fNIRS headband for in-home neuro monitoring.

• VitifyHealth – NLP-driven patient-trial matcher; 40 % reduction in recruitment cycle.

• Vine Medical – robotic intubation device with potential FDA “breakthrough” pathway.

SVTR outlook

Early-stage winners will (i) secure payer pilots or large IDN sandboxes, and (ii) offer fully managed FHIR/Epic adapters. EU MDR alignment is needed for trans-Atlantic scale.

3.2 Drug Discovery (6 projects)

Market pulse

AI-native small-molecule pipelines rank top-three in digital-health subsectors by capital raised. R&D cycles—historically 10 years / $2 bn—are shrinking through target-to-IND automation.

Representative Stanford teams

• SpectralQC – combines quantum computing with generative chemistry to unlock “undruggable” pockets.

• reThink64 – thermostable mRNA delivery platform mitigating cold-chain cost.

SVTR outlook

Regulatory risk is paramount; favour ventures partnered with CROs or pharma co-dev deals providing wet-lab validation and data exclusivity. Hybrid compute + lab automation skill sets will outperform pure-play model shops.

3.3 Robotics (8 projects)

Market pulse

Global RaaS is projected to reach $125 bn by 2034, 25 % CAGR (marketresearchfuture.com). Hardware margins must outrun depreciation. Subscription models (opex vs capex) ease buyer adoption.

Representative Stanford teams

• Zurons Robotics – autonomously patrols apartment complexes; pilot in Oakland.

• Libra Robotics – PV-panel installation bot vs >15 % EPC labour share.

• Hardhat Robotics – “robotic apprentice” trains via imitation + reinforcement; rental + SaaS maintenance revenue.

SVTR outlook

Series A investors look for >35 % unit gross margin and evidence that post-sale service can be delivered remotely. Integration with OSHA / CE certifications accelerates enterprise uptake.

3.4 Developer Tools (8 projects)

Market pulse

GPU spot-rates for Nvidia H100 dropped below $2/hr; platforms such as TensorPool advertise 50 % lower opex than Hyperscale IaaS. AI assistants are shifting from code-completion to auto-commit agents.

Representative Stanford teams

• TensorStax – deterministic agent that heals failed Airflow/DBT DAGs.

• Potpie & ComputerX – desktop workflow agents for semi-technical founders.

SVTR outlook

Multi-cloud GPU schedulers plus open-weight model hubs will stratify winners. EU AI Act transparency rules heighten the value of provenance tooling and dataset audit features.

3.5 GTM / Customer-Ops (8 projects)

Market pulse

Sales / support remains the fastest path to positive ROI for GenAI: cost-to-serve drops 60 – 80 %, CSAT rises double-digits in early deployments. Voice + multimodal agents unlock SMS, telephony and email in one stack.

Representative Stanford teams

• Palona AI – $10 m seed; high-EQ multimodal agent across voice/SMS/email.

• Walnut AI – grafts AI co-pilot onto professional network graph.

• Vectorial AI, Otom AI – embed LLM insights into Jira & CRM.

SVTR outlook

Procurement hinges on privacy posture (Bring-Your-Own-CRM) and explainability. Vendors that tie agent output directly to revenue or ticket-deflection metrics can command usage-plus-outcome pricing.

3.6 Fintech & Investment (6 projects)

Market pulse

Preqin expects a deal rebound in 2025 as rate cuts unlock liquidity, and LPs increasingly back “efficiency tools” in investment workflows (preqin.com). AI augments, not replaces, human analysts—time-to-model compresses from hours to minutes.

Representative Stanford teams

• Synexis – real-time AI analyst scrapes private-company data, matches term-sheets.

• Exo AI – autonomous agent for granular risk pricing.

SVTR outlook

Competitive moat = proprietary financial/talent graph + interpretability dashboards for compliance. Sandboxed deployments (on-prem VPC) are table stakes for regulated asset managers.

3.7 Climate Tech (7 projects)

Market pulse

IRA tax credits and EU CBAM have created explicit revenue accelerants for decarbonisation projects (pv-magazine-usa.com, taxation-customs.ec.europa.eu). Over 62 % of disclosed climate funding YTD 2025 targeted mitigation & efficiency.

Representative Stanford teams

• Sunscreen – micro-fog cooling to cut crop water use 25 %.

• Helix Carbon – in-situ CCU module for steel mills; converts CO₂ to syngas.

• TransformerAM – additive-manufactured transformer cores boosting grid efficiency.

SVTR outlook

Hardware CAPEX is heavy—government grants, strategic CVC or project-finance vehicles often complement VC. Teams that pair policy fluency with tech depth out-execute.

3.8 Public Safety (5 projects)

Market pulse

National security start-ups shorten deployment cycles from decades to months; FedRAMP and StateRAMP define software compliance gates. Procurement still runs 9-18 months.

Representative Stanford teams

• Vimes – cross-agency crisis-intervention platform; cuts response delay from 40 days to 12 h.

• KARDA – auto-generates body-cam incident reports.

SVTR outlook

SaaS + integration fees is winning model. Early alignment with CJIS, ITAR and ethical AI audit frameworks de-risks later stage bids.

3.9 EdTech & XR (8 projects)

Market pulse

With GPT-4o and Apple Vision Pro releases, “immersive + personalised” learning overtakes MOOC 1.0 content. Investors value DAU retention over total enrolled headcount.

Representative Stanford teams

• Weightless Realm – glasses-free 3-D hardware for surgical simulation.

• Remix – 2-D to 3-D live stream engine.

• Videotudor.io, Modo – AI tutor chatbots reducing non-tech founder learning curve.

SVTR outlook

Hardware plays must validate latency and comfort metrics; software platforms need path to district-level budgets (K-12) or employer-sponsored upskilling spend.

3.10 Consumer & Social (7 projects)

Market pulse

GenAI is reshaping user-generated content, NPCs and social graphs. Monetisation leans on in-app purchases or revenue share with creators.

Representative Stanford teams

• Glamir AI – real-time AR make-up recommender.

• Cove, HotSpot Live – AI-curated local events and hobby tribes.

SVTR outlook

Unit economics hinge on CAC vs repeat spend; international DTC expansion (Asia-Pac, LatAm) tests retention of culture-specific AI assistants.

4、Cross-Sector Patterns

5、Outlook & Recommendations

1. For Founders

   1. Prioritise regulatory-ready architecture—FHIR, FedRAMP, CBAM or EU AI Act alignment shortens enterprise sales by quarters.

   2. Embed closed-loop data capture; models without proprietary feedback risk commoditisation.

   3. Design capex-lite pilots—RaaS or usage-based GPU credits lower customer friction.

2. For Investors

   1. Hard-tech valuations now account for IRA/CBAM subsidies—model blended IRRs including tax credits.

   2. Underwrite GPU cost sensitivity and multi-cloud arbitrage when diligencing dev-tool plays.

   3. Assess explainability layer in financial & safety-critical AI—expected to become mandatory.

3. For Corporate Partners

   1. Leverage Stanford pipeline for innovation sprints; many teams seek design-partners rather than pure capital.

   2. Offer data partnerships—early access to domain-rich datasets is decisive in model fine-tuning.

6、Appendix

Acknowledgements

SVTR.AI thanks Stanford Founders, Breakthrough and Llama Lounge for access to on-campus pitch sessions, and the 100 + founders who shared data and demos.

Prepared by the SVTR.AI Research Team | Published 4 June 2025

At SVTR.AI, we analyzed the backgrounds of hundreds of founders behind today’s most heavily funded AI companies. The results reveal powerful patterns across both education and professional experience.

Part I: The Education Factor

Stanford leads—but Wharton surprises

In terms of founder education, elite U.S. institutions dominate. Stanford stands out as the clear #1, with 271 AI founders who collectively raised over $90 billion USD. MIT, Harvard, and UC Berkeley round out the top tier.

Interestingly, Wharton/UPenn, despite a smaller number of founders (72), is associated with more than $40 billion USD in startup funding—a strong indicator that business school alumni are excelling at capital-intensive growth.

Meanwhile, Tsinghua University represents the top non-U.S. institution on the list, contributing 85 founders and over $15 billion in total funding.

UniversityFoundersFunding Raised (USD)Stanford271$90B+Harvard197$40B+MIT156$30B+UC Berkeley128$20B+UPenn / Wharton72$40B+Tsinghua University85$15B+

🧠 Insight: Educational pedigree isn't just academic—it's a trust signal to investors, especially in high-risk, high-reward AI ventures.

Part II: The Corporate Training Grounds

Google trains, OpenAI launches

On the career experience side, Google is by far the most common background among AI founders, with 253 alumni responsible for $40B+ in funding. These are engineers, researchers, and product leaders who have absorbed years of institutional know-how.

But the most shocking datapoint belongs to OpenAI: only 23 founders previously worked at OpenAI, yet their startups have raised over $100B USD combined. In other words, OpenAI has become a signal amplifier—its alumni have an outsize fundraising advantage.

CompanyFoundersFunding Raised (USD)Google253$40B+Microsoft129$20B+Meta106$18B+Amazon75$12B+OpenAI23$100B+Tesla19$40B+McKinsey34$10B+Baidu28$10B+

🧠 Insight: Working at OpenAI is not just a job—it’s a founder credential. Capital follows credibility, and the OpenAI brand delivers both.

What This Means for Founders and Investors

If you're a founder:

• Your alma mater and employer do shape perception—especially among top-tier VCs who are optimizing for pattern recognition.

• But outliers (e.g., Tsinghua, McKinsey, Tesla) show there are multiple viable paths into AI entrepreneurship, especially when paired with differentiated insight or networks.

If you're an investor:

• Pedigree still matters, not as a deterministic factor, but as a proxy for founder readiness.

• Look closely at the clusters—Stanford x Google, MIT x Meta, or OpenAI x Wharton—and ask: what new clusters are emerging?

Final Thought

In AI, the race isn’t always won by the best idea—but often by the most credible founder. Education and career backgrounds are fast becoming the new early-stage diligence layer.

Follow SVTR.AI for more founder data, AI startup intelligence, and venture trends.

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📊 From founder trends to startup financing maps, we decode what’s behind the rise of generative AI.

According to research from Silicon Valley Technology Review (SVTR) AI Database, the competitive landscape for AI agents is becoming increasingly clear. With continuous advancements in large models, AI agents are no longer just simple conversational tools; they are now deeply integrated into critical areas such as development, operations, and business decision-making, serving as key engines for efficiency enhancement and new business model creation.

In this emerging era of AI agents, which companies will emerge as the true winners? What technologies and business models should investors focus on?

Based on in-depth research on over 100 AI agent startups worldwide, Silicon Valley Technology Review has curated a list of the Top 10 Most Promising Companies, positioning them as strong contenders for the SVTR AI 100 list. If you know of high-potential companies—or if you’re leading one yourself—we invite you to share and discuss the future landscape of AI agents with us.

Top 1: Cognition

Founded in 2024 in San Francisco, Cognition develops AI chatbots capable of writing and debugging code. The company has raised $175 million in funding, reaching a valuation of $2 billion. It is backed by Founders Fund, which also led its Series A round at a previous valuation of $350 million.

Scott Wu, Co-Founder & CEO. Previously, he was the Co-Founder & CTO of Lunchclub and a software engineer at Addepar. He studied economics at Harvard University. The company’s team of 10 members has collectively won 10 IOI gold medals.

Top 2: Sana

Founded in 2016 in Stockholm, Sweden, Sana provides a platform that indexes organizational knowledge, enabling employees to access internal information efficiently. The company has raised $55 million in funding from NEA and Menlo Ventures, reaching a valuation of $500 million, with total funding of $130 million.

Sana’s clients include Merck, Novartis, Hinge Health, and Volvo.

Joel Hellermark, Founder & CEO of Sana, taught himself programming and started coding in C at 13. At 16, he founded his first company, a video recommendation engine. He was recognized by The Guardian as one of the "Top 10 People Under 35 Changing the World" during the COVID-19 pandemic. Forbes also listed Sana as one of the Top 50 AI Companies Worldwide.

Top 3: The Browser Company

Founded in 2019 in San Francisco, The Browser Company develops Arc, an AI-powered browser designed to navigate the web intelligently. The company raised $50 million in funding from Pace Capital, reaching a valuation of $550 million. Previous investors include notable figures such as Jeff Weiner (LinkedIn), Ev Williams (Medium), Dylan Field (Figma), Akshay Kothari (Notion), and Jason Warner (GitHub). The company’s total funding amounts to $128 million.

Josh Miller, Co-Founder & CEO, previously served as a board member at Patreon, an investor at Thrive Capital, and a product director in the Obama White House. He was also a product manager at Facebook and studied at Princeton University.

Top 4: Decagon

Founded in 2023 in San Francisco, Decagon leverages generative AI to create customer support agents that handle complex customer interactions. The company raised $65 million in a Series B round, with investors including Bain Capital Ventures, Elad Gil, A, Accel, BOND Capital, and ACME Capital*. The Series A round was backed by *Accel, A, Elad Gil, and angel investors such as Aaron Levie (Box CEO), Howie Liu (Airtable CEO), and Jack Altman (Lattice CEO)**, among others. The company has raised a total of $100 million.

Decagon’s AI agents are built on large language models from OpenAI, Anthropic, and Cohere, trained using enterprise data (such as operational blogs, manuals, and past customer service interactions). Companies like Bilt, Duolingo, Eventbrite, Notion, and Rippling use Decagon for customer interactions.

Jesse Zhang, Co-Founder & CEO, was previously the CEO of Lowkey (acquired by Niantic), a trader at Citadel, a software engineer at Google, and an algorithm developer at Hudson River Trading. He graduated from Harvard University with a degree in Computer Science in just three years.

Top 5: Emergence

Founded in 2024 in New York, Emergence is building an agent-based system capable of executing various tasks traditionally handled by knowledge workers. It leverages both first- and third-party generative AI models, including OpenAI’s GPT-4o. The company raised $97.2 million in funding from Learn Capital and secured an additional $100 million in credit lines.

Satya Nitta, Co-Founder & CEO of Emergence.ai, was previously the Co-Founder & CEO of Merlyn Mind and a researcher at IBM, where he led global AI learning solutions. He earned a Ph.D. in Chemical Engineering from Rensselaer Polytechnic Institute.

Top 6: /dev/agents

Founded in 2018 in San Francisco, /dev/agents develops a universal technology framework that enables AI services and agents to connect and communicate with one another. The company raised $56 million from Index Ventures, CapitalG, Scale AI CEO Alexandr Wang, Palo Alto Networks CEO Nikesh Arora, and OpenAI co-founder Andrej Karpathy. The current valuation is $500 million, with a total funding of $56 million.

The company aims to build a cloud-based operating system that runs on smartphones, laptops, and even cars, alongside a new UI for natural AI interaction across devices.

David Singleton, Co-Founder & CEO, was previously the CTO of Stripe for nearly 7 years and spent 11+ years at Google in leadership roles. He holds a Computer Science degree from the University of Cambridge.

The three other co-founders also have extensive OS development backgrounds:

• Hugo Barra, Chief Product Officer, was VP of Android Product Management at Google and VP of Oculus at Meta.

• Ficus Kirkpatrick, CTO, was an early engineer at Android and VP of AR/VR at Meta.

• Nicholas Jitkoff, Chief Design Officer, was Lead Designer for Chrome OS at Google and an executive at Dropbox.

Top 7: Norm AI

Founded in 2023 in New York, Norm AI develops an AI-powered regulatory compliance platform. The company raised $27 million in a Series A round from Coatue.

Norm AI's platform translates complex regulations into machine-readable code, creating "Regulatory AI Agents" that automate compliance analysis with greater efficiency and accuracy.

John Nay, Founder & CEO, has worked on AI research for the U.S. National Science Foundation and the Office of Naval Research. He founded Brooklyn Investment Group, an AI-driven investment software firm, raising $20+ million from Morgan Stanley and TIAA Ventures. He earned a Ph.D. from Vanderbilt University and has conducted research at Harvard and NYU.

Top 8: Ema

Founded in 2023 in Mountain View, California, Ema is developing an AI-driven platform to automate complex enterprise workflows, allowing tasks to be completed through simple conversations. The company raised $36 million from Accel, Section 32, Prosus Ventures, Sozo Ventures, Hitachi Ventures, and Wipro Ventures.

Surojit Chatterjee, Founder & CEO, was previously the Chief Product Officer at Coinbase and a VP at Google. He has also invested in Udemy, Palantir Technologies, and TrueLayer. He holds an MBA from MIT Sloan with a focus on entrepreneurship, marketing, and finance.

Top 9: UnifyApps

Founded in 2023 in San Francisco, UnifyApps provides tools for rapid application development, business process automation, and seamless data transfer across systems. The company raised $20 million from Iconiq Growth, with total funding reaching $31 million.

Pavitar Singh, Co-Founder & CEO, was previously CTO of Sprinklr and a product leader at multiple companies. He holds an MBA in Finance, Operations, and Strategy from Management Development Institute, Gurgaon and a Computer Science degree from Dhirubhai Ambani Institute.

Top 10: Sema4.ai

Founded in 2023 in San Francisco, Sema4.ai is developing intelligent AI agents to revolutionize knowledge work. The company raised $30.05 million from Mayfield and Benchmark.

Rob Bearden, Co-Founder & CEO, previously co-founded Hortonworks, which merged with Cloudera in 2019. He has also led Docker and Cloudera, overseeing its $5.3 billion acquisition by private equity firms KKR and CDR.

This list represents the Top 10 AI Agent Startups shaping the future of AI-driven automation and intelligence.