Konrad kording, @Penn Prof, deep learning, brains, #causality, rigor, neuromatch.io, c4r.io, Transdisciplinary optimist, Dad, Loves outdoors, 🦖

Philadelphia, PA
What is interesting here is that AI does not produce value for *anyone* but AI companies. It just exploits aspects of the system. How much AI use is like this and how much produces genuine value? I would love to see an accounting.
🦔The Blue Cross Blue Shield Association says insurers paid out nearly $1 billion more than usual from AI-assisted hospital documentation in 2024-2025. Hospitals use AI to scan records and find secondary diagnoses that human reviewers missed. More diagnoses means higher reimbursements. But treatment levels haven't gone up. BCBSA's SVP of data science said AI is "identifying more billable conditions, not sicker patients." Insurers now deploy their own AI to deny those same claims. Patients pay for both through higher premiums. My Take This might be the most American healthcare story I've ever read. Hospitals bought AI to find more things to bill for. Insurers bought AI to deny those bills. The technology cost money on each end and the tab went straight to patients through higher premiums. A billion dollars moved and no one got any healthier. The only thing that changed was the size of the invoices. Two sides of a trillion-dollar industry found a new tool to fight over the same pool of money and the person in the hospital bed pays for all of it. Premiums go up, denials go up, and the AI vendors collect from everyone. If you want to know who AI works for, stop reading the press releases and look at where the money goes. In healthcare it went straight into the billing department. Hedgie🤗
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I am writing a super accessible explainer of AI (for non-CS folks). Anyone wanna test drive?
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Kording Lab 🦖 retweeted
“Don't think for a second that because you are an alarmist that you are doing a social good.” This sentence is just devastating for so many intellectuals who need everybody to presume that they’re “the good people” Thank goodness we have Jensen!
Innovation Council
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Kording Lab 🦖 retweeted
See you next week in Philadelphia! 🎉 In just a few days, researchers and educators from around the world will come together for three days focused entirely on scientific rigor. We're excited for the talks, workshops, conversations, and games that happen when we bring our community together in the same room. Which part of C4R26 are you most excited about? See you at C4R26! 📅 September 28 📍 Philadelphia #C4R26
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Super excited about this. Hardware is where the future is at!
The reactor team at @terraformindies has demonstrated successful production of high purity methanol at scale at our Muroc desert test site. Let's review our progress in 2026. In January, we closed on our Muroc test site. By March, construction was underway. By May, we had 1.8 MW of solar installed. In July, we produced CO2 on site (nitter.net/CJHandmer/status/20801…), and in August, hydrogen (nitter.net/CJHandmer/status/20847…). Terraform Industries has built an incredible team to revolutionize not just one or two key technical pieces, but the entire stack required to convert sunlight and air into fuel. Like plants, only 100 times higher productivity per acre and requiring no irrigation, fertilizer, or pesticide. Two years ago, Terraform produced pipeline-grade natural gas for the first time. Since then, we've scaled up our methane reactor, increasing production by 250x and reducing contaminants by 10x, which allows us to increasing revenue per unit of product by 100x because it's now at chemically pure grade. But it was always clear to me that natural gas was only one piece of the puzzle. It is possible to convert natural gas into other hydrocarbon products like gasoline or jet fuel, but given our precursors are CO2 and hydrogen, we can do one better by directly synthesizing methanol as well. Methanol is far easier to convert into longer chain hydrocarbon products, either via the MeOH->DME->ethylene pathway, MTG, or via variations on Fischer-Tropsch. Methanol also has significantly higher revenue per molecule than pipeline natural gas, which forms an essential part of our cost-reduction-through-profitable-scale-up strategy, explained in more detail in our post: terraformindustries.wordpres… TL;DR: If our learning rate is above 11%, our margins will strictly increase as we scale up into a market worth about $10t/year. Not including induced demand. Amir joined the team earlier this year and speed ran the methanol reactor development program, successfully demonstrating recirculation and heat recycling before integrating these new technologies in a production scale reactor, built from the ground up at Terraform's facility in Burbank. As soon as the hydrogen team was done, we shipped our latest reactor up to the desert, plugged it in, and began activating the catalyst. An extended test campaign through the heat of summer followed, punctuated by bunnies nibbling on hoses! The hard work paid off, with Terraform's production of significant batches of high purity methanol, now packaged and ready to ship. A huge congratulation to the entire team, including Amir, Enric, Dominic, Hugo, and @lucie_nurdin. With the completion of this test, Terraform has now demonstrated TRL 9 operation of all of its subcomponents, each developed from scratch, in house, to deliver the highest possible value to the end customer. Terraform will never rest until we have broken the geological and geographical monopoly on oil, unlocking unconditional energy abundance for all of humanity. Join us!
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Kording Lab 🦖 retweeted
Biology does not need models that just describe the world. It needs models that predict what happens when we intervene. In @CellCellPress Focus on AI in Biology, we lay out how to build biomedical world models, from the data they need to how they should be trained and evaluated.
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Kording Lab 🦖 retweeted
We’re hiring. The Department of Bioengineering is seeking faculty at the Assistant, Associate and Full Professor levels across bioengineering as part of a multi-year hiring initiative. Applications due Nov. 23, 2026. Learn more + apply: be.engineering.upenn.edu/abo… @PennEngineers
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Kording Lab 🦖 retweeted
NSF announces 3 additional topics as part of the NSF X-Labs initiative to pursue generational breakthrough science and technology efforts ift.tt/hKl1YtB
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Kording Lab 🦖 retweeted
When should you use #AI vs. not?  1You shouldn’t use AI instead of efforting when efforting is the very goal of the activity (intrinsic value): like don’t watch a video of weight lifting instead of doing it yourself 2Don’t use AI instead of developing your general skills, or specific skills useful in your job/degree 3Do use AI when you could do it without AI but AI is faster 4Do use AI when the skill it replaces is not important to you, which can be hard to assess without developing your own skill (see point 2)
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Kording Lab 🦖 retweeted
Are virtual flies playing Beat Saber? SM64? Doom? No. The new fly connectome is an incredible achievement! Less is going on in the sim videos than it seems. I'm stoked to see people engage with neuroscience though. My breakdown: neuroai.science/p/are-flies-…
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Kording Lab 🦖 retweeted
I must be among an extremely small group of people (n=1?) that have both 1) trained a frontier LLM and 2) designed and synthesized custom viruses in a lab with my own two hands. And I think that the takes on AI killing us all by creating dangerous viruses is total bogus.
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Kording Lab 🦖 retweeted
I propose Stanford NLP as an independent third-party evaluator under @DarioAmodei’s 3 step plan. For important parts of the work, universities would be better than any other organization (see below 🧵👇), and, of university groups, @stanfordnlp would be the best one to choose. 😊
We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: darioamodei.com/post/we-must…
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Kording Lab 🦖 retweeted
I applaud this but any mature audit ecosystem has *auditor conduct standards*. Was surprised to learn a METR investigator for the HF/OpenAI incident is married to an OpenAI board member, many access contracts are highly constrained etc. PCAOB in accounting exists for good reason!
This kind of ongoing accountability is going to be open up a lot of really valuable possibilities for safety, and I'd love to see similar things elsewhere:
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Kording Lab 🦖 retweeted
it's not "out-of-control" at all. openai had all the control in the world (i.e. they could always literally turn off all their machines across all data centers.) they decided not to based on carefully calculated trade offs between finance-economy-reputation-customers-etc. just like any other co'a e.g. anthropic
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Kording Lab 🦖 retweeted
I've just posted a new preliminary paper: “Calibrating AI automation and its labor market impacts.” I calibrate a model in which AI-driven labor automation is endogenous and tied to the accumulation of AI capital, using data-center and hyperscaler investment to discipline its growth. Preliminary and incomplete. Much more to come. Paper: papers.ssrn.com/sol3/papers.…
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Kording Lab 🦖 retweeted
This is incorrect. Rather, it's an MLP trained policy that can do these things, not the fly connectome itself (let alone the fly brain, which is the connectome + dynamics!). When you replace "fly" w/ "MLP" you realize how not impressive this is...
if you haven't caught up to the fly story, read this: - a fly can experience the absence of love - a fly can be bisexual - a fly can drive a car - a fly can play a game - a fly can post tweets
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I find a weird thing with AI and attribution. It will credit the inventors of something. But it will almost never credit the source of an idea (that is short of having made it) or of a communications idea. And obviously scientists like that. The text is convincing. Logical.
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But in reality, the communications pattern that the user sees as explaining their idea had an original. RLHF removes it. Because users prefer to believe that its their idea and their clear communication. And science has no way of litigating.
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Now lets be clear, they may also steal things that go beyond that but I think @OpenAI and @AnthropicAI have deeper knowledge about this.
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