Key AI Investment Opportunities to Consider

Everyone's chasing AI. The smartest founders I know are building what AI needs to exist. Last week, a founder pitched me their "AI for sales" startup. They were the 7th AI pitch I'd seen this month so far. I asked one question: "What's your monthly compute bill?" "$287,000. And growing." That's when I showed them where the real opportunity was hiding. The infrastructure paradox: Every AI startup needs: GPUs they can't get Data centers already at capacity Specialized compliance tools Cost optimization they can't build The gold rush is obvious. The shovel shortage? That's where fortunes get made. 1849: Levi Strauss didn't mine gold. He sold jeans to miners. 1990s: Cisco didn't build websites. They sold the routers. 2000s: AWS didn't create apps. They rented the servers. Today's version? Look at what's actually getting funded: GPU scheduling optimization AI model monitoring platforms Specialized cooling systems Compliance and governance tools Not getting funded: "ChatGPT for [insert industry]" What I tell every founder: You don't need to predict which AI company wins. You need to sell to all of them. One founder pivoted from "AI-powered analytics" to "analytics for AI companies." Before: Competing with thousands After: Serving thousands The difference? Every AI company needs infrastructure. Only a few need another competitor. The opportunities hiding in plain sight: Model versioning systems. Compliance frameworks. Data labeling tools. Cost optimization platforms. Inference infrastructure. Edge computing solutions. Boring? Yes. Necessary? Absolutely. Fundable? Ask any VC focused on infrastructure. The smart money isn't just chasing AI. It's building what AI needs to exist. Here's what founders miss: The best businesses in a gold rush aren't the ones finding gold. They're the ones everyone pays on the way to the mountain. Your competition as an AI startup: OpenAI, Anthropic, and thousands of others. Your competition as infrastructure: Usually just spreadsheets and duct tape. The founders getting funded in Q4 won't all be building the future of AI. Many will be building the foundation it runs on. Stop asking "How do I compete with ChatGPT?" Start asking "What does every AI company buy?" That's where the real opportunity is. #VentureCapital #Infrastructure #StartupFunding #AIInvestment #FidelmanCo

“If it’s not AI, I don’t want it” – a VC headed to Monaco for summer Q2'25 data* shows AI companies are securing significantly larger rounds across sectors, with median deal sizes hitting $4.6M – over $1M above the broader market. In Q2’25, the AI premium was strongest in Auto Tech which saw AI companies securing deals $20.6M larger than traditional peers (lead by Applied Intuition's $600M Series F at $15B valuation), followed by Robotics and Cybersecurity with median deal premiums of $10.7M and $6.4M respectively. The AI premium extends beyond funding to company performance and trajectory metrics. AI companies consistently score higher on our Mosaic Score (success probability) and Commercial Maturity (ability to compete and partner) metrics, proving their fundamentals justify investor confidence. Why are AI companies commanding these premiums? 1) Capital-intensive development cycles AI companies often require dramatically more upfront investment for compute infrastructure, data acquisition, and model training before achieving product-market fit, necessitating larger initial rounds to reach meaningful milestones. 2) Longer runway to defensibility Unlike traditional SaaS where competitive advantages emerge quickly, AI companies need 12-18 months of continuous model refinement and data collection to build meaningful moats, requiring sustained funding through extended R&D phases. 3) Premium for hybrid expertise The most successful AI companies combine rare AI/ML talent with deep domain expertise (like automotive engineers for autonomous driving), creating interdisciplinary teams that command higher compensation. 4) Infrastructure-first business models AI companies often build foundational platforms (like simulation environments or data processing pipelines) that require significant upfront investment but can later support multiple product lines and customer segments. The AI premium continues to reflect investors' "go big or go home" approach; making concentrated bets on AI teams they believe can capture outsized market share. The AI premium signals more than just funding enthusiasm – it's recognition that AI-first companies are simultaneously disrupting the last two decades of companies and building the infrastructure for tomorrow's economy. *Data from CB Insights’ State of Venture Q2’25 report. Explore the latest data on what happened last quarter across the startup ecosystem at the link in the comments.

Remember the investment atmosphere of the late ‘90s? The dotcom boom? PCs flew off the shelves, online access got cheap, browsers became mainstream. But the clearest signal? Investors doubled down. What have we seen in the last week? Similar perfect storm trends are alive in the #AI world; #infrastructure, #advertising, #voice, #security, #radiology, #schools, #dentists, #lawyers, #robots, #orchards, #sales, #seniors Here is a list of only some of the VC investments in AI in just the last week:* • DataBank, makes infrastructure for data centers, raised $250M • StackAdapt, a programmatic advertising firm, raised $235M • ElevenLabs, makes AI voice software, raised $180M • UVeye, uses AI to inspect cars, raised $150M • Semgrep, an AI-powered app security startup, raised $100M • Rad AI, develops AI software for radiology, raised $60M • Quibim, makes AI models for medical imaging, raised $50M • MagicSchool AI, generative AI software for schools, raised $45M • SafelyYou, AI software for senior living facilities, raised $43M • VideaHealth, develops AI software for dentists, raised $40M • Conifers.ai, working on AI cybersecurity, raised $25M • SuperOps, makes AI tools for IT teams, raised $25M • Paxton, develops AI software for lawyers, raised $22M • Jump, helps financial advisors utilize conversations, raised $20M • Ivo, an AI-powered contract review startup, raised $16M • Bonsai Robotics, makes robots to manage orchards, raised $15M • Unwrap.ai, AI software to help understand customers, raised $12M • qeen.ai, AI agents for e-commerce, raised $10M • Palona AI, AI agents for customer engagement, raised $10M • Little Otter, an AI-powered family mental health startup, raised $9.5M • Aligned, makes AI software for sales teams, raised $8M *This data and this photo comes from Stephanie Palazzolo's great AI Agenda newsletter at The Information #discoverthefuture

YC’s newest Request for Startups (RFS) just dropped. Here are 11 areas where they want to fund startups: 1. AI Workflows & Agents – AI that doesn’t just generate text, but automates business processes and executes real work. 2. AI Compliance & Security – AI-driven audit, risk, and security tools, especially for finance, legal, and data-sensitive industries. 3. AI-Powered Developer Tools – Building LLM-first software development workflows, debugging tools, and better infrastructure for AI engineers. 4. LLMs for Real-World Industries – AI applied to supply chain, logistics, hardware design, manufacturing, and regulated industries like finance & healthcare. 5. AI-Native Enterprise Software – Startups building AI-first SaaS tools that redefine how businesses operate (not just ChatGPT wrappers). 6. B2A: Software Built for AI Agents, Not Humans – Software that AI models use as customers, meaning entire systems built for machine-first interactions. 7. AI Cost Reduction & Inference Infrastructure – Companies solving GPU constraints, inference costs, and efficiency bottlenecks in scaling LLMs. 8. Government & Public Safety Tech – Infrastructure and AI-powered tools to modernize government software, public safety, and defense. 9. The Future of Hardware & Manufacturing – Innovations that bring chip design, advanced manufacturing, and automation back to the U.S. 10. Fintech 2.0 – YC is still betting on financial infrastructure, next-gen payments, and better banking solutions. 11. AI-Aided Engineering & Scientific Discovery – Startups using AI to accelerate breakthroughs in biotech, materials science, and complex engineering. YC’s message is clear: - AI isn’t just improving workflows. It’s becoming the backbone of business decision-making. - Companies solving AI infrastructure constraints (cost, efficiency, inference) will be huge. - Regulated industries and hard problems (finance, healthcare, manufacturing) are ripe for AI-first innovation. YC’s full RFS goes deeper, but this is the roadmap for the next generation of AI and infrastructure startups. Read the full RFS here: https://lnkd.in/gF87vht7 Thoughts?

It's interesting to follow the $ money in the AI / data / analytics space. Sharing some of the notable recent investments: Scale AI – Raised $15B from Meta for a 49% stake; builds AI training and data-labeling infrastructure Databricks – Raised $10B; offers a unified analytics and AI platform for data teams OpenAI – Raised $40B; leads in foundation models and general-purpose AI agents Anthropic – Raised $3.5B; builds aligned and safe large language models Glean – Raised $150M; enterprise AI search for workplace knowledge Cyera – Raised $540M; data security platform using AI for compliance and risk management CoreWeave – Infrastructure AI company scaling GPU cloud; IPO in motion SandboxAQ – Raised $450M; quantum and AI modeling for national security and healthcare Runway – Raised $308M; media-generation AI platform for video and creatives Together AI – Raised $305M; platform for training and deploying open-source LLMs Harvey – Raised $300M; AI legal assistant for law firms and enterprises Quantexa – Raised $180M; financial crime detection and contextual analytics Toloka – Raised $72M; crowdsourced data annotation and human-in-the-loop AI Snorkel AI – Raised $100M; programmatic data labeling for ML training ClickHouse – Raised $350M; high-performance database for analytics and ML Celestial AI – Raised $250M; optical compute infrastructure for AI workloads Skild AI – Raised significant round; building general-purpose robot intelligence Mistral AI – Raised €600M; open-weight LLMs and multilingual model development Why is all the money going into AI, data, and analytics? I think it's a mix of a few things: 1. Everyone wants in on “the next big thing” 2. Companies are trying not to fall behind 3. AI tools need a ton of expensive infrastructure 4. There’s real potential - but also a lot of hype 5. And yeah… no one wants to miss out #FOMO