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We have raised a $395m Fund 3!
Even better: @davidbfischer, former Facebook CRO, is joining us as Partner!
techcrunch.com/2023/11/07/01-…
After years of competing against David, it’s amazing to be on the same side of the table now. @dickc and i feel so lucky to work with him :)
Our for-operators, by-operators fund is prob the only one where the partners have previously taken their companies from $0-$123B in yearly revenue.
(Yes, David represents $121B of that!)
And, @LiatBycel has joined us as an Operating Partner to work with our portfolio companies. Liat helped grow Airtable to $200m ARR — she’s incredible!
Our focus is still the same:
We come in right after companies have found product-market-fit and we help with scaling and GTM. We are mostly Series B but also late As. We love B2B SaaS (most of what we do) but would like to find some more consumer biz too!
We work with Founders at a very critical time: when their companies are scaling and those Founders/CEOs are looking for help from people who have been there before.
If you’re a Founder who is about to go thru a scaling moment, my DMs are open!
Even better: @davidbfischer, former Facebook CRO, is joining us as Partner!
techcrunch.com/2023/11/07/01-…
After years of competing against David, it’s amazing to be on the same side of the table now. @dickc and i feel so lucky to work with him :)
Our for-operators, by-operators fund is prob the only one where the partners have previously taken their companies from $0-$123B in yearly revenue.
(Yes, David represents $121B of that!)
And, @LiatBycel has joined us as an Operating Partner to work with our portfolio companies. Liat helped grow Airtable to $200m ARR — she’s incredible!
Our focus is still the same:
We come in right after companies have found product-market-fit and we help with scaling and GTM. We are mostly Series B but also late As. We love B2B SaaS (most of what we do) but would like to find some more consumer biz too!
We work with Founders at a very critical time: when their companies are scaling and those Founders/CEOs are looking for help from people who have been there before.
If you’re a Founder who is about to go thru a scaling moment, my DMs are open!
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After working shoulder-to-shoulder with @kevinweil in the trenches, and jamming w @dpatil for a couple of decades (!), we’d be excited about backing almost anything these guys decided to build together.
But when they told us about @warpsciences, my immediate reaction was: this isn't just a big idea, it is an idea that is desperately needed.
Here's why:
About 20 years ago, the US pharma industry made an enormous strategic mistake.
Lured by lower costs, the industry began outsourcing a critical component of drug discovery to China.
At first, the advantage was economic.
But over time, China built something far more formidable: a world-class chemistry talent base, massive research infrastructure, and the ability to execute drug discovery work at extraordinary speed and scale.
It started as a cost-saving decision and now has become a total structural dependency. Bonkers.
In the process, we allowed much of the expertise, infrastructure, and capacity underpinning American drug innovation to migrate overseas.
We effectively ceded a critical part of our drug discovery engine to China.
Think about that for a moment.
One of America's most strategically important industries now relies heavily on a geopolitical rival for a foundational part of its innovation process.
It's beyond wild that we let ourselves get here.
So that's what makes Warp so compelling.
Warp is building a next-generation, U.S.-based chemistry contract research organization (a “CRO”), combining world-class chemists, AI, and robotics to fundamentally reinvent how molecules are made and tested.
@kevinweil, @dpatil, @manohar_paluri, and Sol Reisberg's ambition goes well beyond simply bringing this capability back to American soil.
It's about building a fundamentally better model for drug discovery.
Every experiment generates data. That data trains better AI models. Better models make the labs smarter and more productive. And more productive labs enable scientists to explore more ideas, faster.
It's a compounding flywheel of scientific discovery.
If Warp succeeds, we won't just have brought a critical capability back home - we'll have rebuilt it in a way that's faster, smarter, and more scalable than what came before.
That's a future at 01A we're incredibly excited to help back.
But when they told us about @warpsciences, my immediate reaction was: this isn't just a big idea, it is an idea that is desperately needed.
Here's why:
About 20 years ago, the US pharma industry made an enormous strategic mistake.
Lured by lower costs, the industry began outsourcing a critical component of drug discovery to China.
At first, the advantage was economic.
But over time, China built something far more formidable: a world-class chemistry talent base, massive research infrastructure, and the ability to execute drug discovery work at extraordinary speed and scale.
It started as a cost-saving decision and now has become a total structural dependency. Bonkers.
In the process, we allowed much of the expertise, infrastructure, and capacity underpinning American drug innovation to migrate overseas.
We effectively ceded a critical part of our drug discovery engine to China.
Think about that for a moment.
One of America's most strategically important industries now relies heavily on a geopolitical rival for a foundational part of its innovation process.
It's beyond wild that we let ourselves get here.
So that's what makes Warp so compelling.
Warp is building a next-generation, U.S.-based chemistry contract research organization (a “CRO”), combining world-class chemists, AI, and robotics to fundamentally reinvent how molecules are made and tested.
@kevinweil, @dpatil, @manohar_paluri, and Sol Reisberg's ambition goes well beyond simply bringing this capability back to American soil.
It's about building a fundamentally better model for drug discovery.
Every experiment generates data. That data trains better AI models. Better models make the labs smarter and more productive. And more productive labs enable scientists to explore more ideas, faster.
It's a compounding flywheel of scientific discovery.
If Warp succeeds, we won't just have brought a critical capability back home - we'll have rebuilt it in a way that's faster, smarter, and more scalable than what came before.
That's a future at 01A we're incredibly excited to help back.
💥 Introducing Warp Sciences, a new company applying AI and robotics to help the world develop new medicines faster.
We’ve raised a $215M Series A led by @lightspeedvp to bring frontier AI out of the browser and into the lab, and build scientific capacity here in America.
I co-founded Warp with my long-time friend @dpatil, FAIR co-founder @manohar_paluri, and drug hunter Sol Reisberg. The early team is full of incredible people like @bkspears9, who helped achieve Q>1 fusion at LLNL.
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Great list below.
We at 01A are very happy to be investors in #1 @baseten, #13 @render and #22 @attio.
01a.com/featured
We at 01A are very happy to be investors in #1 @baseten, #13 @render and #22 @attio.
01a.com/featured
Fastest growing startups per Brex data
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Micro1 passed $1B rev run rate, up from $500M over the summer, and continues to be very profitable.
AI is driven by compute, models & data. However, the impact of data was oddly non-consensus. The industry's starting to wake up.
If you want to understand where data labs are headed, check out #8 below. Realism has never been more important.
micro1.ai/data-partnersh…
AI is driven by compute, models & data. However, the impact of data was oddly non-consensus. The industry's starting to wake up.
If you want to understand where data labs are headed, check out #8 below. Realism has never been more important.
micro1.ai/data-partnersh…
about 3 years ago, micro1 lost 60% of its revenue overnight and almost went bankrupt.
today, i'm proud to share we've surpassed $1B annual run rate while remaining historically profitable.
it’s been a special journey. we’re grateful for every struggle, moment, and person who e've taken a very untraditional path to get here. our core team is only ~180 people, spanning 14+ countries, and fully remote.
many of our decisions sacrifice hundreds of millions of revenue and slow us down short term. but we believe they compound into advantages that are impossible to replicate long term.
the long term is now.
these are the 8 principles that made micro1 a leader in the data industry and will soon make us number one:
1.) human 1st AI: treat the tens of thousands of experts helping train models with absolute best-in-class care. our goal is to make AI training one of the largest, highest paying, and most enjoyable job sectors in the world.
2.) U.S. first: we refuse to work with adversarial nations, or export data that improves models against the interests of Americans.
3.) long-term research over short-term ARR: invest in deep research, products, and infrastructure for years from now, not just the next customer pipeline.
4.) proper incentives for intensely hard work: no arbitrary requirements like 996. inspire people to work extremely hard with outlier incentive structures instead of forcing weekend work. we’ve maintained close to 100% core team retention as a result.
5.) stay lean and global: we’ve reached $1B run rate with ~180 people, and intend to slow team growth significantly from here. exceptional talent is found around the world, and pedigree is the wrong thing to over-index on.
6.) execution alignment: company to retain a streamlined board so we can optimize for the next several decades, remain light-weight and dynamic, and take bold & calculated risks.
7.) abundant intelligence improvement: invest in building data generation models to make high-quality datasets and RL environments dramatically less expensive and more abundant.
8.) scale data on realism and horizon: realism brings the complexity of the real world into training models, while horizon extends how long models can work and how much complexity they can handle. both have enormous room to scale, and we’re pushing the limits of both.
additionally: after surpassing $500M run rate this summer, we priced our Series B at a $4B valuation, alongside a ~$25M tender to give our early employees liquidity as we build for the long term. the round was led by Dream Ventures, a longtime supporter and one of our earliest investors, alongside Microsoft, Javelin, Flybridge, G Squared, Up Partners, and LG.
we’re also grateful to be backed by incredible industry leaders, including Guodong Zhang (co-founder of xAI), Andy Fang (CTO of DoorDash), Igor Babuschkin (co-founder of xAI), Behnam Neyshabur (founder of Mirendil), Jerry Tworek (ex-VP of Research at OpenAI), and many more.
data is on its way to becoming the largest industry humanity has ever seen, and we're certain this is just the beginning.
best,
Ali
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The @micro1_ai Personal Agent Benchmark just launched, scored on task completion and whether the agent’s actions could be trusted.
Super surprising results to me…
Super surprising results to me…
Preliminary benchmark results below with Gemini Spark taking the lead. View more here: micro1.ai/personal-agent…
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Can you please help me with this important list.
I know there's more. cosign.co/lists/adambain…
I know there's more. cosign.co/lists/adambain…
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Trust will be one of the most important factors across all aspects of AI.
@micro1_ai’s flow-transform model beats every frontier model on privacy. 👇🏼
@micro1_ai’s flow-transform model beats every frontier model on privacy. 👇🏼
Today we’re launching micro1’s PII transformation model, flow-transform 1.0, delivering frontier-level performance across detection, identity synthesis, and transformation of personally identifiable information.
On PrivacyBench, our model reaches 96.0% F1, outperforming every ested, including Tonic Textual, Claude Opus 4.8, Sonnet 4.6, Microsoft Presidio, Haiku 4.5 and GLiNER2.
Some of the most valuable training data for frontier AI models lives inside fully functioning companies. It captures years of real work across decisions, communications, tools, handoffs, exceptions and the relationships connecting them.
The problem is that this data is also full of PII.
Traditional redaction makes the data safe, but it also destroys the very workflows and relationships frontier models need to learn from.
flow-transform 1.0 solves this by turning enterprise operational data into high-fidelity training data for frontier models by replacing real-world identities without flattening the reality the data captures.
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Daaaaang @render.
We spent years hearing: “Will Render scale?”
OpenAI builds on Render. Base44 scales on Render.
Meanwhile, weekly paid service creation looks like this.
I think we can retire the question.
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“Furthermore, our offer aims to create net new opportunities for as many as we can from the 17,000+ former Spirit employees.”
Why we bid $12.5 million for Spirit Airlines’s data:
As you may have seen in the news, we are aiming to acquire the Spirit operational data. We'd like to transparently & directly explain why. The future of AI is a bet on two things: the messiness of the real world and the brilliance of the humans working inside it.
The combination of these two things results in a new dimension of scale called realism. Realism is the most significant thing that has ever happened to model training. It’s what lets RL environments and tasks match the conditions a model is deployed into.
The limit is the real world itself. But the real world is also dynamic. It grows with a company and shifts as operations evolve. There is not a binary scaling of entering the real world. Realism keeps scaling as long as real work keeps happening.
As a data lab, we want this data to benefit the entire AI ecosystem, while ensuring labs that we partner with for this data, agree to strict privacy obligations. This includes keeping the data away from labs outside of U.S. and U.S-allied countries. Our mission is to push the frontier in novel ways, with a human & privacy first approach.
To do that, we've bid on a portion of Spirit’s Airline’s non-sensitive operational data.
Effectively scaling realism requires de-identification as a core competency. Very few organizations can do this. We believe the few that can have an obligation to act transparently. That’s why we’ve made binding commitments. We will prohibit re-associating the data and will not profile any former employee. We’re paying for an independent data ombudsman to review the data and how we use it.
Furthermore, our offer aims to create net new opportunities for as many as we can from the 17,000+ former Spirit employees. We’re providing former Spirit employees preferential access to paid human data roles on our platform, along with free access to our AI training and reskilling curriculum.
These commitments bind us to spending at least $1M. That is just the minimum. It’s entirely possible we spend orders of magnitude more than $1M working with and training former Spirit employees to help define the frontier.
We think this transaction will set the terms for how data like this changes hands from here. Those terms should be worth copying.
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This is so great:
“We use technology to keep the mortgage process simple and our costs low. Those savings help us offer lower rates.”
opendoor.com/mortgage
“We use technology to keep the mortgage process simple and our costs low. Those savings help us offer lower rates.”
opendoor.com/mortgage
Opendoor mortgage is out of beta. The most AI pilled mortgage product designed to help average Americans become homeowners. A fair and honest rate. Built to cost less.
opendoor.com/mortgage
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Was asked by @HarryStebbings for the bull case on @linear.
Wish I were as eloquent as @karrisaarinen who has thought deeply about this. But here’s my simple take (we're investors so of course I'm biased):
We are barreling toward a world where an enormous amount of human-to-human, human-to-agent, and agent-to-agent work will all happen simultaneously.
And as the volume and the velocity of that work explode, it becomes vital to have a system that helps us humans orchestrate it all: the people, the agents, the building, the decisions, the work - and, of course, all of the issues that result from all that building!
That is Linear.
But thinking about it more, there's also something deeper.
What does it mean to be human?
To me, to be human is to connect deeply with each other + the world around us, and to find meaning in those connections.
The bull case for Linear, then, is that in an increasingly agentic world, it becomes the coordination layer that helps us work with machines (and still each other)...and in doing so, allows us to remain more human.
Linear helps us be more human as the world becomes less so.
Wish I were as eloquent as @karrisaarinen who has thought deeply about this. But here’s my simple take (we're investors so of course I'm biased):
We are barreling toward a world where an enormous amount of human-to-human, human-to-agent, and agent-to-agent work will all happen simultaneously.
And as the volume and the velocity of that work explode, it becomes vital to have a system that helps us humans orchestrate it all: the people, the agents, the building, the decisions, the work - and, of course, all of the issues that result from all that building!
That is Linear.
But thinking about it more, there's also something deeper.
What does it mean to be human?
To me, to be human is to connect deeply with each other + the world around us, and to find meaning in those connections.
The bull case for Linear, then, is that in an increasingly agentic world, it becomes the coordination layer that helps us work with machines (and still each other)...and in doing so, allows us to remain more human.
Linear helps us be more human as the world becomes less so.
The Bull Case for Linear Being a $100BN Company:
"Linear is the clear winner.
They have built an agentic product first that allows us to build 100x more software, and that means 100x more features than ever before.
Humans cannot keep up with it, and humans still have to ery human on your team's gonna build 500 features and 1,000 issues and you have 10 people on your team, you need a process and a system of record for managing all these issues with your agents.
We need a new system of record for it.
The team at Linear has figured it out. We have seen an explosion, 50x more agent usage than 90 days ago.
This will seem cheap when Replit's at $15BN, Lovable's at $100BN, because Linear will be the one powering them all". @jasonlk
Love to hear your thoughts, what is your bull case for Linear @karrisaarinen @stephzhan @adambain @dickc @soleio @zoink @Mkclements
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