Data Operations

AI startups dominate venture capital flow: 300+ deals in July as investors back artificial intelligence. Nearly 28% of all VC deals in July involved AI, as mega-funds close and early-stage investors double down Three hundred artificial intelligence startups closed funding rounds over the past month. That's more than one deal per hour, across every major sector from healthcare to defense tech to robotics. The capital flowing into these companies represents a fundamental reshaping of where venture capital is going - and how fast it wants to get there. The data tells a striking story: in a market where venture capital is measured in billions, AI is now measured in a scale of its own. The AI funding tsunami. July saw 1,082 venture capital deals close across all sectors. Of those, 300 were AI-focused startups. That means nearly 28 percent of all VC activity this month involved companies building artificial intelligence products, platforms, or infrastructure. The absolute scale of capital involved is staggering. AI startups alone attracted $2.3 trillion in committed capital during the period - a number that reflects both mega-fund closures and standard venture rounds. The average AI deal came in at $13 billion, which tells you something important about the composition of these signals: the largest fund closures (Dimension Capital's $800 million fund, for example) are getting bundled into the same data stream as seed-stage AI companies raising $5 million. But even when you account for that mix, the trend is unmistakable. Traditional venture capital firms are pouring resources into AI at a pace that would have seemed impossible two years ago. This isn't just Khosla Ventures or Andreessen Horowitz doubling down on AI - it's every major venture firm on the planet reallocating capital in the same direction simultaneously. The investor stampede. Look at which investors are writing the most checks. Khosla Ventures and General Catalyst led the pack this month, each closing 28 deals. Index Ventures, Y Combinator, and Accel followed close behind with 26, 26, and 19 deals respectively. What's remarkable here isn't just the volume - it's the consistency. These firms aren't taking bets on one AI narrative. They're investing across the entire spectrum: AI model developers, infrastructure plays (compute, memory, chip design), AI applications in enterprise software, AI in defense tech, AI in robotics, AI in biotech. It's a strategy that says "we don't know which AI bets will win, so we're making as many as possible." That approach wasn't possible five years ago when venture capital was more selective. Now, with AI fundamentals moving as fast as they are, the safe strategy for VCs is paradoxically to diversify broadly within AI rather than make concentrated bets on "the" AI winners. Series A rounds are no longer where the money stops. Seed and Series A rounds used to be the bread and butter of venture capital. July's data shows a pronounced shift: Series A rounds remain the most common (76 mentions), but they're now followed closely by Seed rounds (75 mentions). More significant, Series B and beyond deals have declined relative to historical norms. This pattern makes sense when you consider the venture landscape AI has created. A startup building a generative AI application can now reach meaningful revenue and user scale in 12-18 months instead of the traditional 3-4 years. That acceleration compresses the typical venture timeline. Companies are either raising Seed or Series A - or they're raising mega-rounds at the growth stage (Series C and beyond) because they've proven out a real business model. The traditional middle stage - where Series B used to matter most - is compressed. Investors see this as a feature, not a bug. It means AI startups with solid founders and product-market fit can reach profitability or significant scale faster than their predecessors. Defense, healthcare, and physics are the new frontier. When you look at individual deal announcements, a few themes emerge consistently. Defense tech startups are raising at unprecedented scale. Resist.UA closed a €50 million European defense tech fund. BRINC raised $125 million from Motorola Solutions for autonomous systems. Humanoid raised $152 million for industrial robotics powered by AI. Healthcare applications are booming. TerraFirma (SpaceX's climate-focused spinoff) raised $115 million. Chai Discovery, an AI drug discovery company, closed $400 million from a blue-chip investor syndicate including Kleiner Perkins, Sequoia, and OpenAI. And across these categories, a single word keeps appearing in deal announcements: "physics." Dimension Capital's $800 million fund explicitly targets "the intersection of science and compute." That's shorthand for AI companies using machine learning to solve real-world physics problems - molecular dynamics, protein folding, materials science, energy systems. This is venture capital's way of saying: AI isn't just software anymore. It's infrastructure. It's industrial equipment. It's national security. The implication for founders and competitors. For AI startups, this environment is historically generous. Capital is abundant. Competition for the best deals is intense, which means terms are favorable for founders. Round sizes are larger than they used to be, which means more runway before the next fundraise. For founders building non-AI products, the message is less comfortable. Venture capital's total dollars are finite. The disproportionate allocation toward AI means less capital is available for other sectors - infrastructure, consumer software, enterprise tools that don't involve machine learning. Some of this is rational; some of it is herding. The broader implication: if your startup's competitive advantage doesn't involve AI in some material way, you're competing for a shrinking pool of VC capital. That's not necessarily fatal, but it means lower valuations, smaller round sizes, or a need to prove profitability faster than AI-first startups. What's next. The venture capital industry is not known for subtlety. When a trend emerges, investors tend to overshoot - more capital flows in than the market can productively deploy, which eventually leads to corrections. InforCapital, partnership is almost certainly in an overshoot phase with AI right now. But overshoot is not the same as misjudgment. AI is genuinely reshaping every major software category, and now hardware categories too. Some percentage of these 300 AI startups will become category leaders. Many won't. That's venture capital's expectation, not a surprise. What should be watched: whether AI deals maintain this pace in August and beyond, or whether summer was a peak. Whether the mega-funds that closed this month actually deploy capital, or whether InforCapital, partnership see slower follow-on investing. And whether the companies raising at these historically large checks can actually justify the valuations that capital implies. For now, the data is clear. AI is the only narrative in venture capital that matters. Every other story is secondary.

Chai Discovery nabs $400M Series C as ai-designed antibodies reach big pharma. Chai Discovery Inc., a company that develops artificial intelligence models to predict interactions between biochemical molecules, today announced it has raised $400 million in Series C funding, nearly tripling its valuation to $3.8 billion. Index Ventures led the round alongside Kleiner Perkins, Sequoia Capital and Dimension. New investors joining the investment included Bain Capital Ventures, Battery Ventures, Baillie Gifford, BDT & MSD, Sapphire Ventures, Avra Capital and other notable investors. Existing investors included Thrive Capital, OpenAI, Oak HC/FT, Menlo Ventures and General Catalyst, among others. The round raises the company's total funding to around $630 million to date, including $130 million at a $1.3 billion valuation in December. Chai Discovery builds frontier AI models designed to accelerate drug discovery. They act by predicting and reprogramming molecular interactions, a necessary first step toward fully understanding how chemistry works for pharmaceutical and life sciences companies. "Tomorrow's medicines should be designed with the precision, speed and scale of modern engineering, and this support helps us move faster towards that future," said co-founder and Chief Executive Joshua Meier. "AI drug discovery has moved from promise to deployment." The company says that its latest model Chai-3 is a "step-change" over the previous generation Chai-2. It reportedly doubles success rates for molecular interaction targets, implying around 35% to 40% hit rates. The company targets enhanced bonding affinity and broader antibody design foundations. Chai Discovery targets antibodies because they are part of the body's primary defense against foreign invaders. Antibodies act as custom locks designed to identify and lock down attackers. The problem: there are roughly a quintillion (1 with 18 zeroes) customizations that can uniquely target foreign bodies. The old way of discovering which ones worked involved screening millions of potential molecules and testing them one by one. To tackle this problem, Chai created an AI system that can perform rapid simulations based on disease targets to design a molecule that fits correctly. It doesn't need to guess, no pile of random molecules, instead it curates the vast set of possibilities to generate a likely high-quality candidate. Recent commercial traction for the company includes a landmark licensing agreement with Pfizer Inc., granting access to Chai-3, as well as an AI model trained on the pharmaceutical company's proprietary data. It has also signed a customer agreement with the American multinational pharmaceutical company Eli Lily and Co. and formed a formal collaboration with the Swiss multinational drug company Novartis AG. Chai faces a very real climb within the drug discovery industry. According to an executive report from Excelra Knowledge Solutions Pvt. Ltd., an AI life sciences company, despite around $20 billion being poured into generative AI drug discovery, no AI-discovered drug has been approved yet. According to OncoDaily, a news site about cancer research and treatments, there are more than 173 AI-originated drug programs now in clinical development, up from almost two dozen in 2023. Around 15 to 20 are expected to reach working trials in 2026. The reason is that although AI-driven discovery achieves fairly high accuracy, with Phase I pass rates at 80% to 90%, it drops to around 40% in Phase II. This matches up with the rates of traditional methods. Ideally, drug discovery and life sciences will need to overcome this hurdle by gaining a better understanding of candidates and their effects as they advance into clinical trials. 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Chai Discovery, an AI-driven drug startup, has secured $400 million in funding, valuing the company at $3.8 billion. The round was announced on 14 July 2026 and includes venture capital firms and strategic investors. The startup uses machine learning and data analytics to streamline drug discovery. Its platform analyses vast datasets to identify potential drug candidates more efficiently than traditional methods, potentially reducing development time and costs. The investment reflects growing venture capital interest in AI technologies within healthcare. The funding will enable Chai Discovery to expand its research team and operational capabilities. As AI becomes more integrated into pharmaceuticals, collaboration between AI startups and traditional pharmaceutical companies is expected to increase. The success may encourage similar startups to pursue innovative solutions in drug development.