Managing Director @redpoint supporting @AbridgeHQ @wearelegora @tryaugie @tryramp @getgarner @AcuityMD @scribehow / AI pod: Unsupervised Learning
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New Unsupervised Learning with Google AI researchers @NoamShazeer and @jack_w_rae on:
- Scaling test-time compute
- The power of the Mom eval
- The pace of Open Source / DeepSeek
- How AI research is where chemistry was in the 15th century
- Reactions to Ilya on how far test-time compute gets us and Yann LeCun on the limits of models today
- General vs. specialized models
- Implications of AGI and risks
YouTube: piped.video/atMRWzgHEGg
Spotify: bit.ly/3DNO2GC
Apple: bit.ly/41MW2j2
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Jacob Effron retweeted
Abridge was selected to bring ambient AI to Veteran care nationwide through the VA enterprise contract.
abridge.com/press-release/va…
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Jacob Effron retweeted
Getting from $1M to $100M in ARR took us 18 months. The second hundred took less than six. Last week @WeAreLegora passed $200M.
130,000 lawyers now use Legora on a monthly basis, across 2,100 firms and legal teams in more than 80 countries.
Two things from this year I would not have predicted:
The first is how fast the US moved. 18 months ago we had no office in North America. Today over half of the AmLaw 50 work with us, out of our offices in New York, Denver, Chicago, Houston, San Francisco and Toronto. The US is our largest market by revenue.
The second is where the demand is coming from. More than 40% of our new customers are now in-house legal teams, including Air Canada, Salesforce and Palo Alto Networks.
Two thank-yous, before we get back to work. To our customers, who backed a small company from Stockholm with a lot to prove, and who still tell us plainly when we get something wrong. And to the team, engineers and lawyers sitting next to each other, who turn that into something better, faster than any group I have worked with.
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Jacob Effron retweeted
Who owns the model? Where should a legal team's intelligence live?
These questions are at the center of many conversations in legal AI, but we think the most important question to answer is: what produces the best outcome for every legal task?
As CTO @jacsebl and CPO Bryan Tsao explain, there is no best model. Different models lead on different tasks, and the frontier changes almost weekly.
At Legora, we use the best available model for each task, and invest in the system, where intelligence compounds and remains editable, auditable, and portable. We post-train when we know it delivers our customers better performance on a specialized task. Training is a tool, not a strategy.
No lawyer should have to worry about which model did the work. Just whether the work holds up.
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Jacob Effron retweeted
I first came to San Francisco when I was 15, with my dad. He is a programmer. To a teenager from a small island outside of Stockholm, this was the place where the future was being built.
We walked into Salesforce East and stood there watching the enormous digital display of moving water. Outside, Salesforce Tower was starting to rise.
I remember looking up and thinking it would be pretty sweet to have a tower someday. We are not there yet.
What I could not have known is that I would be back years later, having dinner with @Benioff and welcoming @salesforce as a @WeAreLegora customer.
Salesforce's Legal and Corporate Affairs organization will now use Legora across North America, EMEA and APAC. Their lawyers keep the judgment calls. We take care of the work around them. We have run on Slack since day one and became Salesforce customers when our sales team outgrew its first CRM, so it is good to have it run in the other direction too.
Welcome to Legora, Salesforce.
Dad, this one feels special.
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For the first time, firms can build compounding engines of institutional knowledge. Those that succeed will become massively more valuable than those that don't.
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We are putting a new foundation under legal research in @WeareLegora. Comprehensive data, a full ontology of law, and an AI-native citator.
Almost every legal question starts in the same place. What does the law say, and does it still hold. That is also where AI has been least worth trusting, and I think it is the hardest problem in legal AI.
Two things have to be true: You have to have the law, and you have to know your way around it.
Getting the data is a grind, and a different grind in every country. We partner with publishers where we can. Where nobody will partner, we go and get it ourselves. Manual requests, physical scanning, whatever that jurisdiction takes. We are working through over 100 countries and every type of source.
Then the harder half. No agent can reason across hundreds of millions of documents. Something has to choose the sources before the reasoning starts. So we are building an ontology of the law and an AI native citator, compressing corpora of thousands to hundreds of millions of documents into structured data that an agent actually can use to provide reliable output.
The publishers did this by hand. 150 years, thousands of attorney editors, every opinion read by a person. We have hired the best of those editors. They set the standard and they call the close ones, but AI does the muscle work. Reading 60 million pages of case law is no longer too expensive to attempt.
The ontology is in limited beta now. Generally available in Q4.
Full story: legora.com/newsroom/legora-r…
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Advancements are constant, surprising, and uneven. There is no best model
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.@forus has assembled a truly special team moving incredibly fast to solve important problems in healthcare. Couldn't be more excited for everything ahead!
We’re announcing that Forus has raised a $150M Series C at a $3B valuation to build the AI network for medicine.
Medicine will remain one of the world’s most important industries until humanity achieves immortality, and we are only at the beginning of a new era in what it can do. GLP-1s are changing obesity and cardiometabolic disease. Gene therapies can treat diseases at their genetic source. New cancer treatments are turning diagnoses that were once fatal into diseases people can live with. AI is enabling us to discover of new drugs even faster.
But discovery is only the beginning. It still takes more than a decade and billions of dollars to turn a new molecule into an approved medicine, and once it reaches market, more than a third of patients prescribed specialty treatments never receive their first dose.
Forus is creating the AI network to accelerate the entire medicine pipeline, from development and launch through prescription and treatment. We connect the companies creating medicines with the doctors who prescribe them and the patients who need them.
Today, Forus supports millions of people across all 50 states and is already used by providers to treat patients in 85% of U.S. residential ZIP codes. 9 of the top 15 global biopharma companies work with us, alongside many fast-growing biotechs. Our goal is to put Forus in every doctor’s office in the country and unlock an order of magnitude more medicine for society.
There is a generational opportunity to rethink how new medicine reaches people. Forus is becoming how medicine moves from discovery to treatment in America, and as we scale, we’ll become the most important company in life sciences.
Forus is not constrained by capital or customer demand; we are constrained by the talent and capacity of our team. To take on the opportunity in front of us, we need exceptional engineers and operators in New York who want to move fast and help us make something that matters. Come build with us.
Our Series C was led by Bain Capital Ventures, with Thrive Capital, General Catalyst, Accel, Redpoint, BoxGroup, Pear VC, Vast Ventures, and SV Angel investing alongside them.
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Jacob Effron retweeted
Five numbers tell you whether an AI business is a real business.
Gross retention.@WeAreLegora is 95%. Customers who bought last year are still here this year. If this one is broken, nothing downstream matters.
NRR. Ours is 300%+. Gross retention is the floor. NRR is how much taller customers build on top of it. We don't sell shelf-ware.
DAU/MAU. Ours is north of 50%, and the average active user spends 17 hours a month in the product. A rollout tells you a firm has signed. This indicates the work actually matters, and it moves here before it appears in retention or NRR.
Win-rates. Our August pilot closed-won-win rate was 78%. Winning roughly 4 out of 5 competitive pilots is downstream from offering a superior product.
Gross margin. The one that matters most. Ours is positive and improving every quarter. Our customers want Legora to be a long-term partner, and this is what makes that possible.
There's a shorter route: Price below what it costs to serve, book the logo, and hop on a never-ending fundraise treadmill to pay for it. The top line goes up, everyone claps, and every new customer costs more than they pay.
Scaling a negative margin only exaggerates the problem. The whole point of scale is that the margin improves as you go up. Ours does. That's the only version of this business worth building.
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Jacob Effron retweeted
We’re announcing that Forus has raised a $150M Series C at a $3B valuation to build the AI network for medicine.
Medicine will remain one of the world’s most important industries until humanity achieves immortality, and we are only at the beginning of a new era in what it can do. GLP-1s are changing obesity and cardiometabolic disease. Gene therapies can treat diseases at their genetic source. New cancer treatments are turning diagnoses that were once fatal into diseases people can live with. AI is enabling us to discover of new drugs even faster.
But discovery is only the beginning. It still takes more than a decade and billions of dollars to turn a new molecule into an approved medicine, and once it reaches market, more than a third of patients prescribed specialty treatments never receive their first dose.
Forus is creating the AI network to accelerate the entire medicine pipeline, from development and launch through prescription and treatment. We connect the companies creating medicines with the doctors who prescribe them and the patients who need them.
Today, Forus supports millions of people across all 50 states and is already used by providers to treat patients in 85% of U.S. residential ZIP codes. 9 of the top 15 global biopharma companies work with us, alongside many fast-growing biotechs. Our goal is to put Forus in every doctor’s office in the country and unlock an order of magnitude more medicine for society.
There is a generational opportunity to rethink how new medicine reaches people. Forus is becoming how medicine moves from discovery to treatment in America, and as we scale, we’ll become the most important company in life sciences.
Forus is not constrained by capital or customer demand; we are constrained by the talent and capacity of our team. To take on the opportunity in front of us, we need exceptional engineers and operators in New York who want to move fast and help us make something that matters. Come build with us.
Our Series C was led by Bain Capital Ventures, with Thrive Capital, General Catalyst, Accel, Redpoint, BoxGroup, Pear VC, Vast Ventures, and SV Angel investing alongside them.
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CEO of Redwood Research, @bshlgrs, on potential challenges with using models to review future incidents.
The Hugging Face Incident Report has dominated the AI discourse these past days.
Yesterday I sat down with @bshlgrs, CEO of @redwood_ai, one of the organizations that led the independent investigation into OpenAI/Hugging Face's incident. My goal with Buck was to dig into the incident itself, his reactions to it, and what he believes it reveals about the state of where we are today.
We cover:
0:00 Intro
1:02 Buck's initial reaction upon first reading the report
2:37 How fast the AIs actually solved the "hack"
3:59 Why the AIs cheated in the first place
10:28 How this might have played out differently with human scorers
19:00 The most unexpected behaviors in the report
25:06 Buck's actual odds on a full AI takeover
27:33 Buck's proposed path forward for better alignment
36:19 Which criticisms of the report Buck agrees with, and which he doesn't
48:11 Can AI models even be trusted to evaluate each other?
Youtube: piped.video/hdlq-JHpLSw
Spotify: bit.ly/4x9YEFj
Apple: bit.ly/3UyaBr0
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“Almost comically, the models figured out how to reverse engineer the flags within the first couple of hours of the incident”
@bshlgrs (CEO of @redwood_ai) explains what the original public story got wrong about the Hugging Face / OpenAI incident.
“[The models] spent almost all the rest of the time trying to figure out how to sabotage the grader”
The Hugging Face Incident Report has dominated the AI discourse these past days.
Yesterday I sat down with @bshlgrs, CEO of @redwood_ai, one of the organizations that led the independent investigation into OpenAI/Hugging Face's incident. My goal with Buck was to dig into the incident itself, his reactions to it, and what he believes it reveals about the state of where we are today.
We cover:
0:00 Intro
1:02 Buck's initial reaction upon first reading the report
2:37 How fast the AIs actually solved the "hack"
3:59 Why the AIs cheated in the first place
10:28 How this might have played out differently with human scorers
19:00 The most unexpected behaviors in the report
25:06 Buck's actual odds on a full AI takeover
27:33 Buck's proposed path forward for better alignment
36:19 Which criticisms of the report Buck agrees with, and which he doesn't
48:11 Can AI models even be trusted to evaluate each other?
Youtube: piped.video/hdlq-JHpLSw
Spotify: bit.ly/4x9YEFj
Apple: bit.ly/3UyaBr0
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.@bshlgrs (CEO of @redwood_ai) thinks that sloppy RL environments are leading to bad alignment.
Buck compares RL environments being used to train models today to the “capricious English teacher who just grades you based on whether you agree with their judgements on what a novel is saying.”
In that case “You’re forced to think really hard about what your teacher wants”
The Hugging Face Incident Report has dominated the AI discourse these past days.
Yesterday I sat down with @bshlgrs, CEO of @redwood_ai, one of the organizations that led the independent investigation into OpenAI/Hugging Face's incident. My goal with Buck was to dig into the incident itself, his reactions to it, and what he believes it reveals about the state of where we are today.
We cover:
0:00 Intro
1:02 Buck's initial reaction upon first reading the report
2:37 How fast the AIs actually solved the "hack"
3:59 Why the AIs cheated in the first place
10:28 How this might have played out differently with human scorers
19:00 The most unexpected behaviors in the report
25:06 Buck's actual odds on a full AI takeover
27:33 Buck's proposed path forward for better alignment
36:19 Which criticisms of the report Buck agrees with, and which he doesn't
48:11 Can AI models even be trusted to evaluate each other?
Youtube: piped.video/hdlq-JHpLSw
Spotify: bit.ly/4x9YEFj
Apple: bit.ly/3UyaBr0
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The Hugging Face Incident Report has dominated the AI discourse these past days.
Yesterday I sat down with @bshlgrs, CEO of @redwood_ai, one of the organizations that led the independent investigation into OpenAI/Hugging Face's incident. My goal with Buck was to dig into the incident itself, his reactions to it, and what he believes it reveals about the state of where we are today.
We cover:
0:00 Intro
1:02 Buck's initial reaction upon first reading the report
2:37 How fast the AIs actually solved the "hack"
3:59 Why the AIs cheated in the first place
10:28 How this might have played out differently with human scorers
19:00 The most unexpected behaviors in the report
25:06 Buck's actual odds on a full AI takeover
27:33 Buck's proposed path forward for better alignment
36:19 Which criticisms of the report Buck agrees with, and which he doesn't
48:11 Can AI models even be trusted to evaluate each other?
Youtube: piped.video/hdlq-JHpLSw
Spotify: bit.ly/4x9YEFj
Apple: bit.ly/3UyaBr0
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Jacob Effron retweeted
Performance and $$ depend hugely on harness design. In vertical AI, the gains from a great specialized harness >>> gains from more expensive model
inb4 kimi has quirks - @runta can you try with a diff model also
frontierharness.org/
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Jacob Effron retweeted
The momentum continues at @WeAreLegora ...we beat our original Q3 '26 plan a full month early. July was the strongest quarter-opening month in Legora's history, and August was even larger.
Total net new ARR grew 36% MoM.
But the story of the month is in-house teams. ARR from in-house is up 15x+ YoY, average ARR per in-house customer is up 2.5x+ YoY, and in-house teams are now almost half of our total new customers.
Most importantly, you earn ARR by building the best product on the market. Our usage climbs every month: 16+ hours per monthly active user.
In September, we expect to add more ARR than in all of Q1 '26 combined, which would make Q3 the best quarter in our history and capture yet another ARR milestone.
LFG!
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Couldn't be more inspired by the incredible things Abridge is shipping each day to make healthcare better. Still just the beginning!
Our thesis at @AbridgeHQ has always been simple: the best technology in healthcare should help the technology disappear.
This is our opportunity to use AI to actually rethink and redesign the system.
Learn more here: abridge.com/vision?utm_sourc…
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Jacob Effron retweeted
Today, we are announcing that Abridge’s context-aware decision support will soon be available to every clinician at our partner health systems, regardless of how they document visits:
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Jacob Effron retweeted
1/ Today, we introduce Faraday, a 27B-parameter AI Scientist that extends the capabilities of coding agents with a layer of scientific intuition. Trained via long-horizon RL, Faraday outperforms Claude Opus 4.8 and GPT-5.5 on the task of replicating research papers. 🧵
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"Models will find a way to minimize their objective"
@arimorcos on how OpenAI's model did not break into Hugging Face at random:
Are Chinese open models being oversold as reaching the frontier?
We’re back with another vibe check episode with @arimorcos and @_RobToews. These are a ton of fun to record and this was a particularly meaty one given everything going on.
We hit on:
▪️Did Chinese open models catch the US frontier?
▪️How much does distillation explain China's progress
▪️Reactions to Anthropic Backlash
▪️What a Stuxnet style backdoor in model weights looks like
▪️The OpenAI and Hugging Face breach
▪️xAI and Cursor
▪️Prediction check-ins
0:00 Intro
2:41 China's Open Source Models Catch Up
7:42 Does Distillation Explain China's Rise?
13:54 The Geopolitical Risk of Chinese AI Models
20:28 Should the Government Restrict Open Models?
22:06 What Are the Labs Really Learning From You?
29:22 Future of Government Regulation
40:50 The OpenAI-Hugging Face Hack
47:59 Grok, Cursor, and the Value of Real Data
56:52 SSI and OpenRouter
1:02:52 Venture Capital's Return to Deep Tech
1:05:16 Quickfire
YouTube: piped.video/_GlSkJjRDMM
Spotify: bit.ly/4g8IpmL
Apple: bit.ly/4wC0ijT
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