Opportunities AI Creates for Founders

A benefit of AI that startup founders aren’t thinking about enough: VCs will be forced to look for singles and doubles, not home runs. Here’s what that means for VCs and how that will have huge benefits for founders over the next 5-10 years 👇🏾 In the past, venture capitalists used their dollars to help their portfolio companies create technical moats. While some founders had success building big, VC-scale businesses, many over-funded and over-valued companies flamed out, leaving the founders and investors with nothing. But something interesting is happening in 2025. AI is making it possible to build AND scale with less capital than ever. This means founders can finally choose a different path: 1. Raise enough to build comfortably ($300-500K) - no more living on ramen 2. Keep 80%+ ownership by raising just one round 3. Focus on sustainable, profitable growth 4. Target a life-changing $10-$100M exit where everyone wins This isn't just theory. More investors (like Tony E. Kula) are actively seeking founders who want to build real, profitable businesses instead of chasing unicorn status. They're looking for: - Deep domain expertise over flashy credentials - Capital efficiency over burning cash - Profitability and sustainable growth over growth at all costs Do you know who’s been forced to develop these traits for YEARS? Underrepresented founders. AI has leveled the playing field. We can finally build companies OUR way - by securing just enough funding to pay ourselves decent salaries in the early days, maintaining majority ownership, and keeping the door open to exits that change our lives, even if they’re under the $1B mark. The question isn't whether you can build a unicorn. The question is: are you sure that you want to?

Did the entrepreneurship scene in Seattle slow down after Techstars announced its closure? Not really! Although Seattle fell 10 spots to No. 20 in Startup Genome’s annual “Global Startup Ecosystem Report,” a ranking of the leading startup regions in the world, I think it is the beginning of a new era. As one door closed, others swung open. Several founder communities, accelerators, and incubators sprung into action to support builders of AI beyond SF. ↳ Incubators and Funding: City and state leaders are working together to create a new AI-focused incubator in Seattle, with $800,000 in funding to support startups. The AI2 Incubator has already spun out over 20 AI companies, including startups acquired by Apple and Baidu, Inc. The AI2 Incubator also revealed that it will operate and help fund the new center, billed as the “AI House.” The idea is to host a physical space for AI-related events and a place for founders, investors, researchers, and nonprofits to interact. Some other incubators: → Comotion Labs - UW CoMotion → Creative Destruction Lab - Seattle → Madrona Venture Labs → Nucleate Seattle → Pioneer Square Labs → WASHINGTON INNOVATION NETWORK - Life Science Washington Institute → WASHINGTON TECHNOLOGY INDUSTRY ASSOICATION → Maritime Blue Innovation Accelerator → The AI Furnace 🧨🔥 - They've achieved amazing milestones such as 15,000 community members, 50+ founder/researcher meetups, 10,000+ connections between founders building in AI worldwide, and more! → AI Tinkerers ↳ Talent and Expertise: Seattle ranks second nationally in AI talent density, with top engineers and researchers at the University of Washington and the Allen Institute for AI. The city is home to cloud computing giants Microsoft and Amazon, which sell essential tools and services that power AI and machine learning applications. ↳ Startups and Innovation: Seattle is home to a growing number of AI startups, including Signify, which recently raised $2.1 million to build out its software platform for manufacturers. Other AI-focused startups in Seattle include Lexion, WellSaid, Xembly, and OctoML. And let's not forget the investors – angel investors, venture capitalists, and crowdfunding platforms. In my recent visit to the Seattle Tech Week 2024, I had the opportunity to interact with some amazing women in tech and investing. → Elizabeth Liu, CEO of Crowd Cow → Elisa La Cava, Principal, Trilogy Equity Partners → Yoko Okano, Founding Partner, First Row Partners → Amy Mezulis, Co-Founder & Chief Clinical Officer, Joon Care So it's clear that Techstars' departure was not a death knell for Seattle's entrepreneurship scene, but rather a chance for new players to shine and for the community to come together stronger than ever. What are your thoughts on Seattle's emerging ecosystem for AI founders and investors? #seattle #AI #artificialintelligence #technology #USA

Why Investors Should Bet on Pittsburgh’s AI Boom Before It’s Too Late 🔹 In my last post, I talked about Skild AI, a Pittsburgh-born startup that raised $300M and is now valued at $4B. It’s proof that billion-dollar AI companies can be built here. But here’s the real question: Why aren’t more investors paying attention to Pittsburgh? 📢 According to Carnegie Mellon University’s recent report on Pittsburgh’s AI Renaissance, the city is at the forefront of human-centric AI innovation. Pittsburgh’s AI & Robotics Edge: ✅ World-Class Research → CMU is ranked among the top AI & robotics research institutions in the world. ✅ Cost Efficiency → Startups can stretch capital 3X further than in Silicon Valley. ✅ AI for Real-World Impact → Unlike the hype-driven tech in SF, AI in Pittsburgh is focused on healthcare, manufacturing, energy, and defense—high-value, deeply impactful sectors. ✅ Access to Non-Dilutive Funding → AI startups here can tap into federal grants (DARPA, NSF, DoD), corporate partnerships, and local innovation funds. YCombinator’s founder Paul Graham recently said: 💬 “I would raise maybe $500K, keep the company small for the first year, work closely with users to make something amazing, and otherwise stay off SV’s radar.” Guess what? APittsburgh is the perfect place for that approach. It offers everything needed to build capital-efficient AI startups without the inflated burn rates of the Bay Area. 🔹 The Investor Blind Spot ❌ VCs continue pouring billions into overhyped AI startups in SF and NY—many of which have high burn rates, talent wars, and unsustainable models. ✅ Meanwhile, Pittsburgh’s AI ecosystem is producing deep-tech companies with real commercial applications. The Opportunity? The next wave of AI unicorns will come from cities like Pittsburgh, where world-class talent meets real-world applications. Investors who recognize this NOW will have a massive edge. If you had $10M to invest in an AI startup outside of SF/NY, would you bet on Pittsburgh? #AI #Startups #Pittsburgh #VentureCapital #DeepTech #Robotics #Entrepreneurship #PaulGraham #CMU

AI's biggest bottleneck isn't capability. It's governance. While tech headlines celebrate each marginal improvement in model performance, the real world often tells a different story. JPMorgan built their own inferior LLM suite entirely in-house, refusing to use OpenAI despite its superior performance. Why? Because when you're handling millions of financial records, a black box —no matter how sophisticated — is a huge risk. This pattern repeats across industries. The obstacle to AI adoption isn't whether the technology works but whether it can be trusted with sensitive data and critical decisions. Lorenza Binkele at SecureAIs spotted this gap early. Instead of building another AI model, she positioned her company in the space between data and AI—providing sanitization, governance, and compliance infrastructure. Her approach challenges conventional startup wisdom in three ways: 1️⃣ She rejected the all-or-nothing platform strategy. When enterprise sales cycles dragged, SecureAI broke their offering into modular components that could be adopted individually. Sales accelerated immediately. 2️⃣ She identified the wedge that opens multiple markets simultaneously. The same technology that helps banks with compliance also helps legal teams process documents and healthcare institutions share research data. 3️⃣ She recognized that the 23andMe bankruptcy wasn't an isolated incident but a preview of AI's future risk landscape. Every company using AI tools is potentially one vendor bankruptcy away from a catastrophic data breach. The real opportunity isn't building the next ChatGPT, but becoming the essential layer all AI depends on. This positions founders to: → Benefit regardless of which AI models ultimately win → Avoid direct competition with tech giants → Build defensible infrastructure that increases in value as AI advances → Target the projected $52 billion AI governance market In the AI gold rush, the biggest opportunities are in building the infrastructure that all AIs need to function safely. The conventional wisdom says focus on agents, models, and applications. The contrarian bet is on the layer that makes those things trustworthy enough for enterprise adoption. Go-to-market success in AI is fundamentally about becoming essential infrastructure rather than just another application. #startups #founders #growth #ai

With every new technology wave, investors have always been eager to back the so-called “picks and shovels'' of the moment. Today, we see this in the valuations of NVIDIA and foundational models. But, while everyone else rushes to back the enablers, someone still needs to find gold. In my new role as GP at Greylock, I want to back founders who are willing to take the risk to build enduring, AI-enabled products that will change how people work and live. I believe there’s tremendous value to be built by product builders who can successfully put the power of AI into products that people love. Of course there are plenty of detractors who believe startups don’t have a chance at this layer – incumbents own the data and distribution, and access to LLMs is both commoditized and fraught with platform risk. There will likely be many casualties of companies where an API call to OpenAI isn’t sufficient to build lasting value. I’ve put forth my thesis for the next wave of AI-first products in a new post, which you can read in its entirety at the bottom. Here are the main takeaways. As I see it, these are the three largest opportunities for founders to build AI-first companies: AI-first networks & marketplaces For all networks (including social networks and marketplaces), AI challenges many of our initial assumptions. This is creating a new arms race to build the next AI-first network. Re-defining enterprise software categories Platform shifts are often significant enough that it creates a window to re-build large categories of software.The best opportunities for start-ups attacking large software categories comes from finding angles where incumbents can’t compete. Co-Pilot for services I believe the best opportunities for AI co-pilots are “branded” sales people, like wealth managers, insurance brokers, and mortgage brokers. Their role involves a lot of text- based coordination, they work across multiple apps, and the ROI of increased efficiency is tangible. I’ve gone into much more detail in my post below. If you are a founder building in this area (or even just starting to think about it) please get in touch. Special thanks to Keith Peiris, Henri Liriani, Cristóbal Valenzuela, Sam Lessin (the resident devil's advocate), Blake Barnes, Will Ruben, Mike Duboe, christine kim, Saam Motamedi, and Jacob Andreou for their thought leadership in this space. https://bit.ly/45ImETl

Stop Building GPT Wrappers! Build AI That Acts. If you’re a tech founder, ask yourself: 1) Is your AI just answering, or is it acting? 2) Is it replacing human effort, or is it just assisting? 3) Can it operate with minimal human intervention? Big opportunities in Autonomous AI: 1) AI Agents for Financial Investing → Trading bots that don’t just analyze, but adapt in real-time. 2) AI-driven Operations Managers → Supply chains that fix themselves based on live data. 3) Fully Autonomous Marketing AI → Systems that don’t just suggest, but run and scale ad campaigns dynamically. A few months ago, I met a founder who had built a GenAI chatbot for healthcare. It could answer patient queries, summarize doctor’s notes, and even suggest treatments based on past cases. The tech was solid. The idea? Not so much. Why? Because the market was already flooded with GenAI chatbots, every new startup was just another layer on top of OpenAI or Anthropic models. The differentiation was marginal, the moat was thin, and investors had seen it all before. What’s the real opportunity? 💡 Autonomous Intelligence. AI doesn’t just generate answers but takes action, learns from outcomes, and optimizes itself—without constant human intervention. From Generative AI to Autonomous Intelligence: The Next $100B Opportunity 🔹 GenAI = Passive → It generates content, but it waits for humans to decide. 🔹 Autonomous AI = Active → It makes decisions and acts on its own. Think beyond text generation: 1) AI that doesn't just summarize legal contracts but negotiates them. 2)AI that doesn’t just suggest ads but autonomously runs and optimizes campaigns. 3)AI that doesn’t just forecast demand but actively adjusts supply chains in real-time. This isn’t a futuristic dream. It’s happening now.

Professional services—legal, consulting, accounting, marketing, HR and IT—add up to trillions of dollars in spend, yet historically they've been tough markets for founders or VCs because margins are thin and scale is hard.  With AI and agentic driven automation, professional services industries can be reimagined with software like margins, creating a trillion dollar startup opportunity, completely untapped. My Mayfield partner Navin Chaddha documents the AI-First Professional Services opportunity in his most recent Founder Insights newsletter (link below).  Here is the TLDR: The opportunity - Professional services—legal, consulting, accounting, marketing, HR, IT—exceed $5T in annual global spend; IT services alone are about $1.5T.  - Traditional professional services firms achieve 30–40% gross margins because they sell time. AI-first firms leveraging agentic AI can now achieve 70–80% gross margins. What’s changed 1. Expertise is no longer gated; generative AI puts a senior practitioner’s playbook in everyone’s hands. 2. Business models move from billing hours to delivering outcomes; AI teammates powered by agentic AI handle repetitive work, people focus on judgment and relationships. 3. Services start to look like software: recurring or usage-based pricing plus proprietary data loops that reinforce advantage. Case study in market Mayfield portfolio company Gruve delivers enterprise IT services with AI teammates. Clients pay when outcomes are reached, giving Gruve software-like economics instead of headcount-driven costs. Founder playbook  - Reimagine the service with AI at the core; don’t bolt AI onto yesterday’s process. - Price for value delivered, not effort expended. - Design human-AI collaboration so people stay in the loop where it matters most. We are looking to partner with seed-stage founders who are rebuilding professional services markets through an AI-first lens. 

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: #Cybersecurity #FinTech #HealthTech #SupplyChain #IndustrialAutomation #LegalTech #AgriTech #EdTech #RetailAndCPGTech #GovTech Here is a list of only some of the VC investments in AI in just the last week: • Meter, provides internet and AI networking infrastructure, raised $170M • Glean, an enterprise search startup, raised $150M • Laurel , an AI timekeeping startup, raised $100M • Horizon3.ai, uses AI to test companies’ cybersecurity, raised $100M • Wandercraft, develops AI-powered robotic exoskeletons, raised $75M • Aerones, uses robotics to inspect and maintain wind turbines, raised $62M • Abacum, a business financial planning startup, raised $60M • Pactum AI, which helps companies negotiate with suppliers, raised $54M • Beewise, which uses AI to monitor the health of beehives, raised $50M • Tastewise, helps food/beverage companies with marketing, raised $50M • Ellipsis Health, AI for phone calls with healthcare patients, raised $45M • Swimlane, an agentic security startup, raised $45M • Speedata.io, develops chips for analyzing large datasets, raised $44M • Voxel, which analyzes security footage to prevent accidents, raised $44M • Knowunity, which develops an AI tutor, raised $31M • Simetric, helps financial institutions reconcile transactions, raised $30M • Definely, an AI legal tech startup, raised $30M • Landbase, an AI sales startup, raised $30M • LawZero, how to develop AI systems that don’t deceive users, raised $30M • Treefera, provides companies with data about supply chains, raised $30M • Sema4.ai, software to help companies make AI agents, raised $25M • Obvio, uses AI cameras to automatically issue driving tickets, raised $22M • Ciroos, an AI-powered engineer investigate software bugs, raised $21M • Skyral, uses AI to simulate the outcomes of public policies, raised $20M • pWin.ai, supports government contractors bidding, raised $10M • Thunder Code, which uses AI to find software bugs, raised $9M • Automated Architecture (AUAR), uses robots to build houses, raised $7M • Deepdots, software for analyzing customer interactions, raised $6M • Literal Labs, builds explainable models for regulated industries, raised $6M • Lendurai, which develops AI-powered drones, raised $6M • Trustible, helps with legal compliance of adopting AI, raised $5M • Ryght AI, develops AI-powered clinical research software, raised $3M AI is on a roll indeed! #discoverthefuture

AI-native startups can scale with tiny teams. Cursor $200M ARR in 12 months with 20 people Mercor $50M ARR in 2 years with 30 people Lovable $10M in 2 months with just 15 people And the list keeps growing. Small has become a compounding advantage: Lower burn, faster iteration, less dilution, more fun. With next-gen AI tools, a few exceptional people can build software-centric businesses that scale to $100M+ in revenue, powered by automation, not headcount. Here’s what that dream team might look like: 1/ Business Visionary Knows how to build moats, master GTM, and validate ideas fast with partners and users. 2/ Tech/Product Visionary Spots tectonic tech shifts and understands where the market is going. Not just agentic, but infra-level thinking. 3/ Systems Builder Could be a CTO, a marketer, a numbers person, or someone who turns chaos into structure. In this setup: → They’ll build faster with AI dev co-pilots → Run sales, marketing, and customer success with AI → Handle legal, accounting, analytics, and compliance — faster and cheaper → Set up self-healing data pipelines and automated workflows → File taxes with AI, too The fewer the people, the simpler everything becomes. And the simpler it gets, the fewer people you need. Fewer people = more building, more selling, fewer distractions. Does that mean team doesn’t matter anymore? Quite the opposite. The value of highly organized, self-managing, daring people is only increasing. One thing AI can’t do? It can’t spot truly exceptional talent. It can’t spark interest in the right people. And it can’t convince them to join you — before the fundraising, before the hype. AI also can’t spark those flashes of insight. That’s the founder (and team) advantage. The 10% that won’t be AI. The key is to act when those moments strike. The bottom line: now is the best time ever to launch with a small team and grow fast.

The Information just featured my controversial take on why traditional Venture Capital is being disrupted in the age of Ultra-Lean AI Native companies. Welcome to the world of Seed-Strapping, the most disruptive trend Silicon Valley has seen in years. I told them about a founder who raised <$1M, built a $7M business in 12 months, and shocked his investors by telling them he will NEVER raise again. While AI unicorns like Cursor and Harvey are grabbing headlines for raising hundreds of millions, a wave of underdog startups is gaining momentum. They are raising once, becoming profitable immediately, and keeping 70%+ of their companies. Let that sink in. And if you think that's impressive, look at Cal AI, founded by Zach (who is just 17 years old). He built a nutrition tracking app that is disrupting legacy giants by leveraging AI and innovative marketing. Within 6 months of launch, he hit $12M ARR with minimal outside funding. It's mind-boggling, when you compare this to traditional VC-backed founders who raise 3-4 rounds and end up with single-digit equity stakes. The math is brutally simple: AI has driven the cost of software development and ops to nearly zero. One engineer with AI tools can now build what previously required entire teams. For investors, it's a double-edged sword. • Best case: 10X returns on minimal capital. • Worst case: Those SAFE notes never convert because companies never raise again. Some investors are now adding clauses requiring dividend payments if no further rounds occur (although I wonder if that is still enough to save the traditional VC model). But what’s fascinated me the most is the psychology of these founders. They don’t need to build the next Apple or Google. They simply want to work hard, be their own boss, build cool stuff and make $5-10M a year while maintaining control. And they are realizing this is both possible and achievable with AI leverage. The playbook is simple: 1. Raise one, well-structured seed round 2. Use AI to slash operational costs  3. Focus relentlessly on revenue and profitability, not vanity metrics 4. Negotiate SAFE terms carefully (they may never convert) The age of the lean, profitable, founder-controlled AI company is here. And this million-dollar question is keeping VCs up at night: “Why would any AI-native founder raise endless rounds of VC funding again?” —————— I am actively helping next-gen founders who are building AI Native companies. If you are one of them, let’s connect. Or if you know someone building or aspiring to build such capital-efficient AI companies, share this post with them. Lastly, if you are interested in learning more about lean AI-native companies, I created an open-source leaderboard to track such companies (link in the comments). (Shoutout to The Information and Natasha Mascarenhas for featuring my thoughts on this trend that is redefining startup funding).