Jonathan Chen

2026 will be the year of reckoning for AI-enabled services.   This model has soaked up venture dollars on a bold promise: unlock TAMs an order of magnitude larger than SaaS while preserving SaaS-like gross margins.   But unlike SaaS, revenue growth and strong logo retention don’t prove product–market fit. You only truly have it when AI is doing a material share of the work at a high gross margin. Otherwise, you’ve built a good services firm financed with the wrong kind of capital. We call it "Mirage PMF".   Real PMF in AI-enabled services requires proving you can scale non-linearly relative to your costs. To get there, your AI must drive measurable improvements in cost, quality, or speed—or ideally, all 3.   That distinction will stop being theoretical when these companies go out to raise growth rounds this year. Investors will stop paying for the *promise* of AI-enabled delivery and start underwriting proof in the P&L that machines, not armies of humans, are carrying the load.   This will inevitably cause some of today’s fastest-growing, best-branded AI services companies to stall, take painful terms, or quietly sell when it becomes clear they’re still mostly human-powered.   The shakeout will be uncomfortable but healthy, as it will force founders to redirect energy from bespoke customer demands to internal AI tooling and to choose importance over urgency.   Ultimately, it will separate true AI-enabled services from beautifully marketed outsourcing.   And that’s a feature, not a bug.   AI-enabled services remain one of the most promising new business models in decades, and in 2026, the winners will finally earn their multiples.

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Harpoon Ventures + Black Flag is teaming up with Microsoft for Startups to provide founders with access to up to $250,000 in Azure credits, empowering builders to scale faster, train models more efficiently, and access world-class AI infrastructure. What’s included: ⚡ Up to $250K in Azure credits (usable across Azure OpenAI, Grok 3, Llama 4, and more) 💻 On-demand GPU VMs for model training and fine-tuning 🧠 Access to GitHub Enterprise + Microsoft 365 Business Premium 🤝 Dedicated Microsoft technical + GTM support This partnership is designed to give deep-tech and dual-use founders the tools and resources they need to win. Because when they win, we all win. 🏴‍☠️

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📕 YC’s Guide to Seed Fundraising — for founders raising their first round Written by Geoff Ralston (President of YC), this is one of the best startup funding playbooks ever made. It’s packed with lessons from the people who defined early-stage investing: ▫️ Paul Graham (Co-founder, Y Combinator) ▫️ Fred Wilson (Union Square Ventures) ▫️ Babak Nivi (AngelList, Venture Hacks) ▫️ Sal Khan (Khan Academy) ▫️ Carolynn Levy (inventor of SAFE notes) If you’re raising a seed round, this will help you: ✅ Know when to raise ✅ Decide how much to raise ✅ Choose the right type of investor ✅ Avoid common valuation traps ✅ Negotiate and close confidently ✅ Prepare all the right docs It’s like a mini-MBA in early-stage financing, without the fluff or the tuition :) Bookmark this and share it with someone raising soon ✌️ Join 80,000+ founders and VCs who receive these insights in my weekly newsletter: https://lnkd.in/dScY_5k3

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Founders massively misunderstand VC fundraising today. You need to sell one of two things to get VCs excited: 1. Mega AI tailwinds; AI means your product is needed way more. (Supabase, Databricks, RevenueCat) 2. Replacing jobs. (Harvey, Legora, Rogo, Solve Intelligence). If you aren’t doing one of these. It’s hard to get excited today given outcome sizes needed in VC land. #Founder #funding #business #investing #vc #venturecapital #entrepreneur #startup

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🚀 The AI-Native Revolution: Why Re-engineering SaaS Won't Cut It in the Enterprise 5.0 Era After decades of SaaS dominance, we're witnessing a fundamental shift that's reshaping how businesses operate and compete. This isn't just another tech upgrade—it's a paradigm change from traditional software to AI-native architectures. 💡 The Dawn of Enterprise 5.0 We're entering Enterprise 5.0, where human ingenuity combines with intelligent machines to create autonomous workflows. Unlike previous eras, employees become orchestrators of intelligent workflows rather than manual processors of information. 🏗️ Architecture is Destiny The difference between AI-native and traditional SaaS isn't in surface features—it's in architectural DNA: Traditional SaaS: Static workflows, bolt-on AI features, fragmented data AI-Native:Intelligence embedded from the ground up, continuous learning, unified data flows Adding AI features to legacy SaaS is like attaching a jet engine to a horse-drawn carriage. You might go faster, but you haven't built an airplane. ❌ Why SaaS Re-engineering Fails Three critical failure modes: 1. Fragmented Data Access - AI features only see siloed data, missing the full picture 2. Broken Learning Loops - Insights don't flow across systems, limiting compound intelligence 3. Task vs Process Automation - Bolt-on solutions automate tasks, not entire workflows 🎯 The Real Challenge Building AI-native solutions faces unique hurdles: • 85% of AI projects fail due to data quality issues • Only 12% of organizations have AI-ready data • 67% of employees show low-to-moderate AI acceptance • Requires entirely different tech stacks and organizational transformation 📈 The Market is Moving VCs have invested $2+ billion in agentic AI startups in the past two years. 25% of companies using generative AI will launch agentic AI pilots in 2025, growing to 50% by 2027. 🔮 The Path Forward Success requires: ✅ Full-stack AI rearchitecture ✅ Human-centric design that amplifies capability ✅ Different economic models (higher initial investment, lower marginal costs) ✅ Continuous learning architectures The Bottom Line:Just as budgets moved from TV to digital and software migrated to cloud, this transition will be swift and decisive. Companies retrofitting AI onto legacy architectures will be outpaced by AI-native competitors built for this new era. The future belongs to those who understand that we're not just adding intelligence to existing software—we're fundamentally reimagining how software and services work. At BGV, we're backing founders building these human-centric, AI-native solutions that combine the best of human ingenuity with intelligent machines. This is Enterprise 5.0, and architecture is destiny. #AI #Enterprise #Startups #VentureCapital #AINative #Enterprise50 #BGV #FutureOfWork --- What's your take?

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A lot of VCs use founder age as a proxy for how well they’ll leverage LLMs and the latest AI. In my experience, the stronger signal is how technical they are. Technical founders understand how LLMs work and can exploit them fully; non-technical founders see magic, not mechanisms.

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Adding headcount is the laziest means to achieve outcomes or unlock scale (especially in the AI era). Just look at this data from Palle Broe on revenue per employee in top AI companies: Midjourney: $4.1M per employee with just 49 people Anysphere: $3.3M per employee with 30 people Adept: $3M per employee with 22 people Meanwhile, companies at the bottom of the chart barely hit $100K per employee with teams of 300-500 people. The gap is staggering. We're talking about a 40X difference in what each person generates. I've spent years as a CRO and operating partner watching companies try to grow, and the default solution is always the same: "We need to hire more people / throw bodies at this." More SDRs to hit sales targets. More engineers to build features. More customer success to reduce churn. But every new hire creates: More communication overhead More management complexity More consensus-seeking Less accountability Less agility The founders creating outsized returns are obsessed with leverage. One of our portfolio companies generates more revenue per employee than businesses 10x our size, precisely because we can't afford to be lazy about scaling. We've built our entire workflow around the idea that every team member should deliver 5-10x the industry average output — and AI tools are making that possible in ways we couldn't imagine even two years ago. So, how are you creating leverage? // Follow more of my musings on substack >> https://t2m.io/4aDyNCpu For the love of the game 🏴‍☠️⚡️

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This is crazy: NONE of the GenAI unicorns that started post-transformer are YC companies. And 85% were founded in just the last 2 years: 55% in 2023, 30% in 2022 (we are early in the cycle) via @BennettSiegel

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