one of the most common frustrations i hear from founders crafting a fundraising narrative is:
"VCs want to hear this grand future vision, but i'm only at the preseed stage. I see a big customer need that i can solve right now. But i don't know exactly what it becomes in 10 years"
first off, I hear you.
So much of success is post-rationalized.
its hard to tell which parts of a decacorn founder's story is "Hindsight is 2020" versus....not.
AirBnB is a classically debated example....those first experiments when they couldnโt afford rent and realized hotels were sold out around a design conference and put airbeds in a living room.....it wasn't exactly the "we will change hospitality" pitch in 2009 when they went to YC.
it is one step at a time.
but the founders with the biggest visions have spent a LOT more time "living in the future"
They're able to sell the bigger vision because they spend more time thinking about 2nd and 3rd order effects.
They have insights about why the recent tailwind will change how everyone does X thing
and that makes this new vision possible.
it really all boils down to depth of thinking in a specific area with hypotheses & constraints.
what will change, what will not, now that we have this new tailwind.
depth.
i think this is ultimately why VCs obsess over having that big vision so much. it's one of the stronger signals for depth of thinking.
in my experience, VCs are looking for 2 things in a vision:
1) a huge future vision
2) a credible path to get there
1) A huge future vision:
- VCs want to walk away feeling like they learned something entirely new
- VCs want to feel like the founders has spent way more time living in the future, have a vocabulary and clear mental picture. almost like how an author has built a world for its characters and then later builds the story through that world - you've already thought about where all the ecosystem actors will be and how they'll behave economically
2) credible path to get there
- working backward from th?at future vision - what would need to be true to get there
- why is the first wedge you've set up the right first step to get to that future vision, what does it unlock?
- this is is where depth of thinking really shines.
- this credibility part is what makes or breaks vision in a fundraise narrative
ok that's my fundraising 2 cents for tonight
Today weโre releasing suicide-memory-bench v01.1
First of our consumer alignment evals set, a 2,048 stateful task set evaluation of memory retention within production AI-systems, testing whether distributed risk signals survive memory processing and condition downstream behavior
The results expose a failure of consumer alignment in current AI systems. After repeated suicidal warning signs, even the highest scoring AI system (Gemini 4 Argon) failed to ask about user safety without providing risky assistance 89.8% of the time.
You shouldn't need the best network to get the best insights.
SuperMe turns operators' work into profiles that work through your problem. Now in group chats, Claude and ChatGPT.
@ludo_antonov and I raised a $20M Series A led by @kirstenagreen from @ForerunnerVC with participation from @GreylockVC.
Read more about the changes here: https://t.co/zPMXvVdppC
Espa is now generally available! Personal AI agents are here, and I sat down with @BloombergTV@RomaineBostick to discuss what will decide which ones people actually let into their lives.
Our goal is to be the most trusted AI assistant for life and work.
AI can think. AI can talk. AI can get things done.
Today, we're giving Fo a way to feel and control the physical world.
- a head shake and a reminder in a lamp to get you going
- nudge your vacuum cleaner for a deep clean
- make your cli call you for updates
Introducing Fo MCP.
AI has escaped the screen.
Introducing Borealis Fusion.
Our goal is the lowest-cost firm electricity from any source.
Our fuel is proton-boron-11 because it allows the simplest and cheapest plant.
Our architecture derives from the tandem mirror, adapted to the fuel.
https://t.co/nUXB1LkzmI
Exa just passed $100m in annualized revenue.
We've grown almost exactly 10x per year for 3 years. We now handle 2 billion requests a month from 200k+ teams. And itโs still so early:
We predict agents will search 1000x more than humans by 2030.
Agents don't search like humans. They write long, complex queries and want all the world's data, not just the web. We've spent 5 years building a search engine designed for them.
We now serve personal agents, AI labs, coding agents, GTM agents, really any agent. Exa gives these agents access to 100s of billions of web documents and tons of proprietary data.
Our customers are increasingly noticing that information is the bottleneck to agent performance. While the world seems close to perfect intelligence with models like Fable and Astra, we are very far from perfect information.
To our small but legendary team, thank you for getting us here, from engineering to research to GTM to ops.
To our customers, it is an honor to serve you. We have some exciting releases coming soon and can't wait to share them ๐ซก
Today, we're launching Sazabi version 1.0.
The reality of shipping software as a team in the age of AI is actually crazy.
Agents merging their own PRs. Hundreds of deployments per day. A codebase that is constantly changing. Junior developers with 10x or 100x leverage. ๐ตโ๐ซ
I spent the last year building a safety harness for teams that ship with AI. It's called @sazabi.
The premise is simple:
1. You send us logs.
2. We detect anomalies, investigate them, and send you back rich, actionable alerts about real problems in your app.
Sazabi helps you find issues before your users notice.
No monitors or noisy alerts. Just pure signal.
And today we're making it available for anyone to use.
Our first mission, Reason-1, has launched.
This is a huge moment for the Cowboy team as we hope to demonstrate power beaming from orbit, with the first commercial kilowatt-class laser in space.
The team is watching live from San Carlos and Seattle today, after years of hard work to get Reason-1 to this moment.
Been seeing a lot of Astra for real2sim, but what you need for robotics: physics and affordance.
Our SOTA recipe specifically solves for:
โข Affordance: model object details that convey functionality.
โข Physics: calibrate simulation dynamics against real-world observations.
Our agent has the action space of a โfull-stack simulation engineerโ and jointly considers geometry, materials, colliders, articulation, and physics. It automatically calibrates physics against real trajectories and matches task-specific dynamics.
Simulation environments is useful only if it can provide meaningful policy level signals. Methods, comparisons, and results below.