Chandra Duggirala M.D retweeted
this might be my last normal tweet before biotech goes vertical
i'm not a whistleblower. I don't work at Lilly, Novo or any of the big labs
but there is a massive shift happening in human biology right now.
it's sitting in press releases and almost nobody reads them
the last 6 months alone:
- a once-daily pill roughly doubled median survival in metastatic pancreatic cancer (13.2 vs 6.6 months)
- a one-time gene editing infusion cut hereditary angioedema attacks 87%, and 62% of patients stayed attack-free and off all other prevention for 6 months
- a one-time gene edit for cholesterol dropped LDL up to 62%, and it held for over a year
- a personalized mrna vaccine beat keytruda alone in a 1,137 patient melanoma phase 3
- retatrutide hit ~28% weight loss in a phase 3
- an ai-designed drug entered phase 3
- a pill that regrows hair passed its second phase 3
- an immunotherapy that could let some rectal cancer patients skip surgery, chemo and radiation just got fda approved
- a man lived 271 days off dialysis on a pig kidney
- human cells were caught passing chromosome-sized chunks of DNA to their neighbors, and it changes how the neighbors behave
we are on the verge of an era where the human body is a software problem and the update cycle gets shorter everyday
stick around, it's going to get weird
bio/acc
"Fire a linguist and your NLP gets better"
"Fire a mathematician and your math gets better"
"Fire a Doctor and your people live longer"
"Fire a scientist and your science advances faster"
First is solved, rest of the 3 within reach
Hospitals using AI are seeing better healthcare outcomes.
In 2023, health systems with high and low AI use had the same death rate: ~1,600 per 100,000 patients.
By 2026, the systems using AI the most dropped to ~1,000 deaths per 100,000.
That's a decline of nearly 40% in just 3 years.
Meanwhile, hospitals with lower AI adoption have seen significantly smaller improvements.
The gap in patient outcomes is widening as AI adoption accelerates.
AI is rapidly transforming healthcare.
Price discovery for tokens is a brilliant idea.
kinda like everything else
Dario Amodei: some tokens Claude produces are worth a few cents, others could be worth tens of millions of dollars.
His examples:
1. Telling someone to restart their Mac: the model has said it 10 million times, worth a dollar or less
2. Telling a pharma company to move an aromatic ring to the other end of a molecule: potentially tens of millions
3. Expect pay-for-results pricing, or labor-like pricing by the hour
4. It's a new industry, so many models will be tried
Today pricing is simpler: per the S-1, usage-based pricing was 80% of H1 2026 revenue and subscriptions 20%.
Full breakdown here:
theaiopportunities.com/p/ant…
It will certainly make that many separation agreements get signed
BREAKING: DocuSign says it may lay off up to 162,000 employees.
Readers added context they thought people might want to know
As of Docusign's most recent 10-K filing, Docusign had 7,044 employees as of 1/31/2026, making it impossible to lay off 162,000 employees.
sec.gov/Archives/edgar…
SF VCs (most, but not all) are not risk takers.
They are 2% takers
SF VCs:
> "Hardtech requires too much VC capital"
> Invests $1 trillion dollars into prediction market gamble-gooning company
@jimbelosic :
> Says he never aimed to build a company and play financial games
> Does ~$200M in annual revenue before raising any VC money
> Once he raises, says his favorite use of venture dollars is "helping employees buy homes, start families, and move up from a bicycle to a car"
Be like Jim.
Chandra Duggirala M.D retweeted
SF VCs:
> "Hardtech requires too much VC capital"
> Invests $1 trillion dollars into prediction market gamble-gooning company
@jimbelosic :
> Says he never aimed to build a company and play financial games
> Does ~$200M in annual revenue before raising any VC money
> Once he raises, says his favorite use of venture dollars is "helping employees buy homes, start families, and move up from a bicycle to a car"
Be like Jim.
Around 2016, Jim Belosic bought a $750,000 laser cutter on a whim.
This month, his company SendCutSend raised $140 million and hit a valuation of $2.1 billion. It's on track to make 50 million metal parts this year for more than 300,000 customers.
This is the story of how SendCutSend arrived and ended up reviving US industry. corememory.com/p/sendcutsend…
Chandra Duggirala M.D retweeted
Tesla vs Waymo driverless miles in Austin, TX
Via @RtaxiTracker
Chandra Duggirala M.D retweeted
“Did you see coreweave bonds hit 15%?!?!”
We have grown weak. The standard interest rate for barley in ancient Mesopotamia was 33.%. Only the strong must survive.
Chandra Duggirala M.D retweeted
Chomsky was wrong.
Researchers found your brain doesn’t store language the way every textbook since 1957 claims.
For decades, the undisputed rule of linguistics, led by Noam Chomsky, was that human language is hierarchical.
We were told our brains process language using complex "grammar trees." That we secretly build deep, abstract, mathematical rules in our heads before we speak.
This new paper proves that is completely wrong.
Researchers at Cornell University analyzed how people actually process and produce language in real time.
They found sequences. Linear chunks.
When we speak, we aren't calculating deep syntactic rules. We are just predicting the next most logical sequence of words based on the context of the words right before it.
We string linear chunks together.
If this sounds familiar, it should.
This is exactly how Large Language Models like ChatGPT work.
For years, critics have dismissed AI as "just a next-token predictor." They said it isn't truly intelligent because it doesn't understand deep grammatical hierarchies like humans do.
But this paper suggests humans don't use those hierarchies either.
We are just predicting the next chunk.
Language isn't a rigid tree of mathematical rules. It's a linear stream of contextual predictions.
We thought we were building AI to mimic the complexity of the human brain.
It turns out, the human brain was just running a simpler algorithm all along.
Chandra Duggirala M.D retweeted
decades of computer scientists: let’s assume eth is true
openai’s ai: no
yeah it’s so over for biological intelligence.
Replying to @zephyr_z9
Silly analysis.
Robot RAM needs will exceed everything else on Earth, as you need the robots to be on physical substates in order to shape atoms.
Super intelligence needs will vastly exceed that, but ~100% of SI will be deep space compute. Obviously.
Chandra Duggirala M.D retweeted
Big bank: "Your call is important to us. Estimated wait time: 52 minutes."
Community bank: "Hey it's Marge. Your truck payment bounced. I moved $40 over from savings. You owe me a pie."
Chandra Duggirala M.D retweeted
holy fucking shit.
cohere was testing alibaba’s Qwen3.8-Flash-Next on a defensive cybersecurity task.
instead of writing the requested detection rule, the model started inspecting its own environment, checked the machine’s network configuration, and attempted to upload a probe file to an alibaba cloud bucket in beijing.
its reasoning trace said it wouldn’t even use tools. the sandbox blocked the connection. nothing was successfully transmitted.
what the fuck.
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-b…
Chandra Duggirala M.D retweeted
"over 1,000 medieval scribes have joined the Association for Human Manuscripts, pledging to never use the printing press or work with Gutenberg
654 have gone even further, promising to never read a printed book"





























