Professor Daniel Martin Katz #Law + #Science + #Engineering + #Entrepreneurship PAGE: https://xitter.cf/t.co/4PK6hxI3Q3 EDU: https://xitter.cf/t.co/4iru4S5kdY CORP: https://xitter.cf/t.co/KE6PcoN5bO

@IllinoisTech @ChicagoKentLaw
Joined March 2009
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Computational Legal retweeted
I asked Claude how much it would have taken to develop Econvitals pre-coding agents. It's answer: a team of about 50 people working for three years with a budget of $20 million. Is this correct or too high? I'm not sure. econvitals.org/about/before-…
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Computational Legal retweeted
.@DKThomp: “In 2020 and 2021, investors looked at Peloton’s and Zoom’s explosive growth, projected their revenue curves into the future, and concluded that these companies would take over fitness and work. Instead, what the pandemic really did was concentrate several years of demand into a few quarters. The earnings that followed disappointed, and their stocks plummeted. I am not an AI bear. But I wonder if coding agents did something a little bit like this for AI. Software companies adopted Claude Code and Codex so quickly—and spent so lavishly in their adoption—that the most valuable AI market was saturated with great haste. The year 2026 saw some of the fastest-growing companies in the history of capitalism. But doubling revenue again would probably require a new magic trick—a new S-curve, a coding-agent revolution for finance, law, or manufacturing.” open.substack.com/pub/derekt…
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Computational Legal retweeted
Really enjoyed doing @TheTrueNorthPod where we dig into the Clio journey, our AI transformation, the challenges of scaling in Canada, and what it takes to build a generational company.
Two best friends from Edmonton swore off VCs, found their first investor in a spam folder, and built a $5 billion company from Burnaby, BC. My conversation with @jack_newton and Rian Gauvreau, the founders of @goclio is live: 0:00 Intro 3:53 Would you build it here? 16:57 Capital will find you 20:50 Two idiots from Edmonton 32:02 Why doesn't Claude eat your lunch? 43:58 Four steps to the epiphany 49:37 Sell work, not software 55:09 The shoe company 1:03:18 The spam folder 1:10:44 From "f*** VCs" to $2 billion 1:17:05 The Sagrada Família 1:23:31 Prosperity is a choice Follow @TheTrueNorthPod for more untold Canadian founder stories.
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Computational Legal retweeted
By every measure, Opus 4.8 beats human experts as peer reviewers in a study of 124 Harvard social science papers covering a range from statistical analyses to formal models to qualitative work. As academics, we need to figure out how to adapt the practice of science to AI, fast.
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Computational Legal retweeted
When my first AI book was published in 2024, the best AI could do 4 minutes of human work with a 50% chance of success When I turned in the draft of Co-Existence earlier this year, it was 17 hours When it was published two days ago, AI could do 17 weeks open.substack.com/pub/oneuse…
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Computational Legal retweeted
Big new IFP report from @GeorgiaCAdamson, @KonstantinPilz, and me on the US-China AI competition. We spent the last 6 months digging into how Chinese AI companies’ access to compute and distillation of American models translate into the current US-China capabilities gap. The result is a 23k-word research report, including an interactive analytical model that visualizes changes in the US AI lead under different policy interventions. Our findings: 1. The US AI lead over China is currently only around 6 months. 2. The US could roughly double this lead, to over a year, using authorities already available to the federal government. 3. On chips: China’s access to AI chips will roughly double over the next year, with chips housed in data centers overseas accounting for roughly half of China’s 2027 compute. 4. On distillation: the current ~16x US training compute advantage would normally yield a 13-month lead. But we estimate adversarial distillation halves this to around 6.5 months. 5. Increasing the lead would have some obvious benefits: - Give the US broadly a greater cyberdefensive advantage against Chinese threat actors. - Give US AI companies breathing room to spend more R&D resources on making their systems safer and more secure, without fear of being overtaken by less safety-conscious Chinese competitors. 6. Provide the US government more leverage in negotiations with China about the future of AI. 7. The two most important interventions are: - For BIS: Create a new end-use control to restrict China’s remote access to training clusters abroad — China’s largest and fastest-growing source of frontier-scale compute. - For DOJ/FTC: Create a narrow safe harbor to allow US companies to coordinate on countering Chinese adversarial distillation. 8. Both interventions (and all the others we discuss in the report) are things the federal government could do today. 9. To estimate the effects of each policy intervention, we model the US lead as a function of its compute advantage over China and China’s ability to adversarially distill the capabilities of the US frontier. 10. The model makes many simplifying assumptions, but provides a useful way to assess and rank the impact of different policy interventions at a high level. Read the full report here: ifp.org/how-to-increase-amer…
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Computational Legal retweeted
Repeat after me: using AI for writing is not cheating. Stop this nonsense. Is it badly written? Say so. The argument is wrong? Say so. The process that produced it is irrelevant.
Why is it always the op-eds about AI use? Hot tip: if your thesis is "here is how to make undergrads think and learn", it really undermines your case when your writing method is the primary undergraduate cheating method. Shout it from the rooftops, write your own damn op-eds!
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Computational Legal retweeted
New: Accenture has told employees it will only fund $29 per month of Claude use. Meaning that employees who often spend as much as $1,000 a month are now scrambling to find ways to keep access to the tool. A sign of the continuing crack down of AI 404media.co/accenture-tells-…
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Computational Legal retweeted
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works already-clean.goodfire.com/
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Computational Legal retweeted
It’s getting hard to build good legal evals. So many legal tasks involve judgment and discretion. I built LegalForecastBench to test frontier models on a high-level legal task with objective ground truth: predicting how judges will decide real motions.
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Computational Legal retweeted
🚨Out now in PNAS! The first ever (we believe) large scale RCT + field experiment on the performance effects of AI Personalization! 🚩 In a preregistered randomized experiment, 🚩 We paired 1,258 participants with AI agents prompted to exhibit varying levels of the Big Five personality traits. 🚩 These human–AI teams produced 7,266 display ads, evaluated by 1,168 human raters, and a field experiment on X with ~5M impressions.
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Computational Legal retweeted
I know an associate in Biglaw who is a top 5 power user of AI at his firm. He’s not just making AI slop, he checks everything that the AI produces and verifies the all citations with actual sources, even though AI gets it correct most of the time these days. Recently he’s been told by his partner that, he’s “over confident”, that he’s working too fast and that he needs to check his work more, even though the partner could not pick out substantive issues in his drafts. I told him it’s likely code for: you’re running up a bunch of tokens and costing the firm while affecting my top line since I could normally bill the client twice for this work. As such, he is now forced to take longer on his work with little substantive impact on the work product while the client gets his bills multiplied. That’s probably part of the reason why despite the billions that have been invested in legal AI for Biglaw, there is barely any productivity increase and clients don’t see their bills go down by one cent.
Investing in AI does not change what a law firm rewards. If the incentive is still to log more calls, do more busywork, and bill more hours, a new tool will struggle to change the outcome.
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Computational Legal retweeted
Alex Nunn
@AlexNunnOct 14
I made an animation showing the Top 10 most-cited Supreme Court cases every year since 1792. It's remarkable how citations (by federal and state courts) have exploded in frequency in the last half century.
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Computational Legal retweeted
This is the least surprising graph in the world if you understand how gradient descent works.
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Computational Legal retweeted
We will soon see what will be, in effect, a perfectly legal distributed denial of service attack on customer service departments authorized by millions of consumers when it becomes widely known that Muse/Dot/etc can make unlimited requests for old refunds, negotiate deals, etc.
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Computational Legal retweeted
Had early access to Gemini 4 Argon - so I asked for slides on the AI power bottleneck. It synthesized 8 years of 10-K filings, FERC queue data across all 6 US grid operators, 17 gigawatt cluster buildouts, and a decade of GPU thermal specs. And I got this interactive deck 🤯
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Computational Legal retweeted
Introducing GLM-Legal Built on GLM-5.3-Flash, GLM-Legal matches frontier models on mainstream legal benchmarks. Designed for real legal workflows: contract review, evidence analysis, compliance, and more. Tech Blog: z.ai/blog/glm-legal Apply for API Access: z.ai/consultation/legal Try AutoClaw: autoclaw.z.ai
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Computational Legal retweeted
Job Opportunity: Lawyers, come join @AnthropicAI for a 4-month fellowship!! Law is a field where it’s notoriously hard to define quality, and there’s so much interesting and important work to be done here. Link here: job-boards.greenhouse.io/ant…
Political scientists and economists: we're looking for researchers with deep expertise to come work with us for a unique 4-month fellowship at @AnthropicAI. You'll work closely with us to evaluate, improve, and apply Claude's capabilities in your field. It's going to be extremely fun and interesting work, and definitely worth taking a short leave from your university for if you need to! More info here: job-boards.greenhouse.io/ant…
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Computational Legal retweeted
The Bitter Lesson for harness design and formal math! Instead of hand-designing how an AI should prove (decompose, plan, assign agents), what if we just give a frontier model good Lean feedback and let it keep working? We built h1. TL;DR: → SOTA on formal theorem proving benchmarks → can solve open conjectures → matches/beats hand-designed workflows → one long attempt can beat many retries [1/7]
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