🌊 The rarest asset in venture
AI bubbles, defence booms, trillion-dollar charts, and courage > consensus.
I’m Ivan. This is a weekly brief on how top 1% startups use ai, raise capital, and grow. The highest signal-to-word startup newsletter you’ll read.
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Hello there - I’m back from SF and (very) jet-lagged. But it was totally worth it.
Here’s what went down in startup-land this week:
🤖 AI
The FT shared intel on OpenAI’s trillion-dollar web. There’s a big debate on bubble vs no bubble. I wrote a piece arguing that the answer is both. If you zoom in we have a bubble in the making. Financial crises are endogenous to capitalism, here’s Mr Dalio backing me up. On the other hand, if you zoom out and look at a 10 year horizon, we may be at the beginning of a tech supercycle.
New OpenAI performance numbers and their (interesting?) ownership structure popped up. My take is that a company this size with so much riding on it should just go public. Not just because this “lets stay private forever” playbook distorts early-stage markets, but also because it should be treated like any other major institution shaping society (more transparent, accountable, governance, etc.).
Found some great performance data on the most valuable private AI startups. Valuation multiples will make you dizzy. But so will their growth rates (Cognition, Cursor, Anthropic).
CB Insights published a list of the Top 20 AI agent startups that have crossed $10M in ARR. Six of the top 20 build for developers and half of them are <3 years old.
The State of AI report dropped, the tl-dr: OpenAI still leads the frontier but the gap is closing fast. 2025 was the year AI learned to think: plan, reflect, and self-correct. Adoption went mainstream (44% of U.S. companies now pay for AI), and compute is the new bottleneck (we touched on this last week).
Meta just dropped another big AI paper (Agent Learning via Early Experience) and it’s a glimpse into how agents will actually learn in the wild. Instead of relying on curated human data or slow reinforcement loops, the model learns by doing, running its own “what if I do this instead?” simulations to improve reasoning. Meanwhile, Zuck keeps vaccuuming ai talent (Andrew, cofounder of Thinking Machines Lab).
I shared a short history of LLMs, with a phenomenal dataset and visual by Harrisson Schell, 8 years of LLMs compressed into one curve of momentum.
🌊 VC
I loved this article from Nicholas Chirls arguing that courage, not intellect, is now the rarest edge in venture. Today everyone reads the same books, listens to the same podcasts, and runs the same AI tools. One of the only true differentiators left (apart from subscribing to Startup Riders Pro 😎) is having the guts to bet when it feels hard (non-consensus). Basically to risk your reputation, not just your capital.
Palle Broe shared a great ranking of the top 40 most valuable private companies in the US today. He wrote about how without immigrants more than $1T in private market cap and the following companies would not exist in the US: SpaceX, OpenAI, Stripe, Databricks, Anthropic and many more.
Howard Marks (cofounder of Oaktree, a legendary investor) just released 35 years of his best memos, investing principles + lessons from every major crash.
Jon Lai (a16z GP) shared a great reminder: when VCs pass, it’s rarely personal. It’s most likely just too many layers of risk at that moment. Peel them one by one (team, product, market, competition), come back stronger, and prove them wrong.
Really enjoyed Dan Gray’s observation on 2 types of VC’s: those complaining about “access,” and those quietly finding outliers. The first group falls into the money chasing deals trap: more capital → higher prices → lower returns → crash. Same movie every cycle: too much money flows into consensus bets, while the real value gets created where capital is scarce (a must read y
).Carta data shows 70% of all new venture dollars now go to AI startups, mostly in San Francisco. So when big-fund VCs say “it’s the frothiest market ever,” they’re not wrong, but they are also just describing their own bubble.
Dealroom shared new data showing defence is now Europe’s fastest-growing VC sector, $1.5B raised in 2025, 4× in five years. Munich leads, Lisbon and Madrid join the map, and NATO startups have already pulled in $9B this year.
ChartMogul analyzed 6,500+ SaaS startups and found only 2.3% reach $25M ARR within 10 years, a good reminder to consider survivorship bias.
🧩 Founder Frameworks
Proven → Better → New: Mark Pincus (founder of Zynga, sold for $12B) shared a framework I love for building products: start with what’s proven, make it better, then earn the right to go new.
Full hands-in, full-hands: an idea I loved from a founder who borrowed it from restaurant work, where servers always enter and leave the kitchen with full hands. In a tier-1 startup (saw this at Facebook), no one walks past a problem.
Bezos on the 3 tasks outlier leaders focus on: 1. Identify the big ideas 2. Enforce execution of those ideas 3. Grow the next generation of leaders.
🔒 Go Deeper: Startup Riders Pro
Venture Capital Resources
🔒 Fundraising toolbox — a list of key resources and benchmarks.
🔒 Founder Info-Diet — 40+ quality info resources to stay ahead.
🔒 How not to raise money — a checklist of fundraising mistakes.
🔒 Fundraising Frameworks — 3 frameworks to tighten your pitch.
Market Research Drops
🔒 AI Voice Agents — Deep dive into the future of voice AI.
🔒 Agentic Revolution — Deep dive into the next tech platform shift.
🔒 Private Communities - The rise of curated professional networks.
🔒 Vertical Social Networks - The unbundling of social media.
Founder Playbooks
🔒 Founder Compensation — salary data from top founders.
🔒 Bottleneck Thinking - a founder prioritisation framework.
🔒 AI GTM Stack — How founders are rebuilding sales in 2025.
🔒 HubSpot’s GTM Playbook — $0 to $100M sales strategy.
That’s it! I’d love to hear from you, just reply to this email (I reply to all of them) 👋.
Great work!