Now: https://nitter.cf/t.co/RR0qSmUlex Previously: Co-Founder & Chief Product Officer at Latch ($LTCH)
Washington, DC
Joined March 2009
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we aren’t ready for the amount of munitions required on an autonomous battlefield
Quantum-Systems’ Reliant long-range drone can stay up for more than 10 hours and take off without a runway. Now the German drone maker has shown it armed with mortar rounds and grenades.
defence-blog.com/german-dron…
Dhruva Rajendra retweeted
My expectation is that integration costs will drop dramatically and we will see loads more integrators as a consequence
a16z's Erin Price-Wright says factory technicians can now retool production lines with AI in 20 minutes, a job that used to take an outside integrator 3 months:
"Historically, if you're setting up a line with a lot of automation, you really need to be producing a lot of one single thing. That's how you get the cost down, and how you justify investing in a pretty bespoke manufacturing line."
"But what we're seeing across our portfolio is that manufacturing technicians themselves have the intuition, and now the software tools, to change the economics of producing low-volume, high-mix on the line."
"A manufacturing technician, not necessarily someone with a college degree or who's studied software engineering, can now use these tools to reprogram and reconfigure their lines on the fly, because they understand the manufacturing process."
"Instead of calling a systems integrator, which takes 3 months and hundreds of thousands of dollars to reconfigure a line, you can have a manufacturing engineer use Claude and do that in 20 minutes. That's been a real game changer."
@espricewright
Dhruva Rajendra retweeted
Inputs, factories, systems.
Get all three right, and the impact reaches well beyond defense, to skilled jobs, revitalized communities, and the supply chains behind energy, housing, and everyday goods.
The 2026 American Dynamism 50 is here: our annual list of 50 companies, from within the a16z portfolio and beyond, building for America's future.
This year's theme is Production is Deterrence. "America's ability to prevent conflict depends on what it can build, how fast it can deliver, and how long it can keep going."
A new generation of defense companies is moving from prototypes to full-scale production, and the industrial base has to keep pace. This year's list is organized around three pillars:
1. Systems: the drones, ships, missiles, aircraft, and spacecraft that deter conflict.
2. Factories: the plants, tools, and software that build them.
3. Inputs: the materials and power they're made of and run on.
Get all three right, and the impact reaches well beyond defense, to skilled jobs, revitalized communities, and the supply chains behind energy, housing, and everyday goods.
Meet all 50: a16z.news/p/ad-50-2026-produ…
Dhruva Rajendra retweeted
11 year lead time for Patriot missiles btw
Switzerland ordered U.S. Patriot missile defense systems in 2022 and may wait up to seven more years for them. So it’s shopping for a second system too, and it’s down to an Israeli option or a French-Italian one.
defence-blog.com/switzerland…
can’t wait for a single person defense co
(run by a defense anon of course)
Surprise 1: Anthropic acquired a one-person biotech
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Surprise 2: that person is me
Surprise 3: I’m here to tell you all about it
Yes, this is what you think it is; after 6 yrs of increasingly loose anonymity: the Professor Oak reveal post
…and yes, it has gotten so boring around here that I’m literally doxxing myself to spice up the timeline
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Ok, allow me to introduce myself. My name is Cody Tranbarger. I mostly tweet, but occasionally find time to dabble in drug dev & starting/building biotechs. My past lives span the VC, crossover, startup continuum; Prof Oak has been my way of keeping a digital foot in those worlds, despite having my physical feet planted elsewhere.
The past 18 mo, ‘elsewhere’ was Mithril Medicine, a co. I founded to develop bsAbs for LAMA2-CMD & other ECM-related genetic diseases.
Mithril was born out of hands-on building with @MikeNGladstone, @JasonPaulRhodes, @LifeSciVC, & team @atlasventure. Somehow, I made enough half-decent calls along the way (even a broken clock is right twice a day, eh?) to convince them Mithril/I warranted a small seed. While I can hardly rationalize such an outrageously cavalier bet, I’m grateful for it, & I'd partner with Atlas again in a heartbeat.
In typical AV fashion, Mithril’s primordial form was virtual, decentralized, & ultra-lean. This stage tends to be short-lived, but for Mithril, it worked surprisingly well, for surprisingly long, thanks in part to the rapid maturation of the AI tools I used liberally.
Eventually, Mithril became a bit of an experiment itself - to find the upper bound of how far it could scale as a single operator + a bunch of Claude (+ a stellar bench of part-time consultants).
In parallel, I started spending time with @nc_frey, @an1lam, & @CoefficientBio team; and promptly experienced, for the 1st time, AI at the frontier. Soon thereafter, Mithril stopped looking like an experiment, & started looking like a preview of an entirely new operating model.
From that spark came “Pipeline-in-a-Person.” The idea immediately resonated when we shared it here back in April; and it was central to the shared vision that led to Anthropic’s acquisition of Mithril, & my joining the team.
Why join Anthropic? Three reasons.
(1) For Mithril, the programs continue, with more resources & infrastructure behind them, and as much Claude as I can put to work.
(2) For me, the chance to start several more programs, & find out how many one person can actually run in parallel - an almost unprecedented luxury for a curious drug hunter.
(3) For our mission, a path to institutionalize & scale this new way of working, then put it in the hands of all of biopharma, all over the world - which, candidly, I find the most energizing.
Will AI x Bio cure diseases? Unequivocally yes, imo… but not by the route that gets the most airtime. Rather than timeline acceleration & compression, I see orchestration & parallelization as today's low-hanging fruit.
Most drug hunters see a handful of go/no-go calls in a career, & spend entirely too much of the rest trading emails (my inbox can attest).
Give them the bandwidth to run several programs at once, and the time to focus on scientific judgment, and I’m willing to bet they’ll make better calls. And at scale, better calls translate to more drugs, higher hit rates, broader patient impact, & likely, a disease or 10 or 100 cured. I couldn't be more excited to build toward that future @AnthropicAI.
Finally, a heartfelt thank you to BioTwitter/X. The end of anonymity is bittersweet, but if anyone’s earned a proper introduction, you all have.
It’s difficult to overstate how much this app has done for me: a decade of education, plenty of friends, many job opportunities, several mentors, a few investors, even a co-founder (fmr). It was the generosity of this community that inspired me to start posting 6 years ago, after just as many years lurking. I hope I’ve been able to pay it forward, in my own small way.
But, to be clear, Prof Oak isn’t going anywhere. Same beat, same themes, and now, a front-row seat to drug development at the frontier. Stay tuned for dispatches from the trenches; and remember...
Gotta Cure 'Em All
Dhruva Rajendra retweeted
The U.S. military plans to buy millions of drones. One problem: it has a massive deficit of the materials needed to make the drones go boom.
I wrote about one effort to start solving the explosives shortage that has afflicted the U.S. for decades. @WSJ
wsj.com/politics/national-se…
super proud brother today. congrats @ashokaraj_ !
We’ve acquired Orchestra Bio, a platform that helps biopharma teams coordinate the complex process of drug development.
Together, we’ll build on Rosalind, our AI models and tools for life sciences research, to streamline those operations and help new treatments reach patients sooner.
Welcome @ashokaraj_ and team!
openai.com/index/openai-to-a…
Weapons development is legitimate research and engineering work
For those who think I’m joking, this is a real job posting 😭
job-boards.greenhouse.io/ant…
Dhruva Rajendra retweeted
I've spent years in China building robots. I can tell you this playbook won't work:
Pre-seed $: Invent the ultimate actuator.
Seed $$: Figure out mass production.
The hard part isn't inventing a beautiful topology. It's process engineering.
The real delta between the US and China is the ability to execute the enormous amount of physical x mental work required to iterate thousands of times on product designs and manufacturing processes. Partly because much of the tacit knowledge / management systems has left the US, but also because of the sheer human labor required.
This isn't something you can solve by buying traditional automation lines either. It requires:
1. Ideation driven by human intuition. Walking up and down the line, talking to assembly workers, and developing hypotheses about what and where improvements can be made. Raw human hours and attention spent understanding a specific manufacturing process.
2. Large batches of experiments across multiple variables. Coordinating experiments that can easily involve 50+ people, issuing instructions, and tracking the execution and results of each experiment.
3. Purpose-built test design to identify key metrics. For example, a change in gear hobbing profile might require accelerated aging tests on a dyno, while a change in bearing tolerance might require NVH testing.
4. Data + human intuition-driven analysis. Interpreting test results and deciding whether to implement a process change or run the next set of experiments.
5. Strict process control. Reissuing work instructions to the line and ensuring every process is followed to the letter.
The best manufacturers in Asia, from Foxconn to TSMC, have built their competitive advantage on systematically turning thousands of physical experiments into better manufacturing processes.
Their moat isn't just production scale. It's the organizational machinery that makes manufacturing learn and improve faster than anyone else.
I don't think most people in SF clock this.
Say you're manufacturing actuator gears in-house in the US. After you've made your first article, will you hire a team of 10 process engineers to coordinate the 50 technicians running concurrent experiments with designs, processes, and materials?
To understand wear patterns across every pin in a cycloidal drive after 10,000 hours? To reduce backlash from 15 arcmin to 5, then to 1? Just to keep pace with China's progress over the next few years?
Do you have the skill set and management systems in place to do this?
That's why our approach isn't just to automate manufacturing. It's to automate the process of learning how to manufacture better.
Automate simulation and design optimization.
Automate assembly and physical testing.
Automate the design and execution of process experiments.
Feed real-world experimental data back into simulation to continuously improve sim-to-real accuracy.
The goal is to turn what currently takes 50 engineers coordinating thousands of experiments into a closed-loop system that continuously learns and improves.
Then the same robotic line mass-manufactures the product.
PS: Coming back to actuator design, many actuator designs are already commoditized in China. The project in the video is open source, and CNC machining cost in China is just $27 (watch the video).
I hope people understand this reality. I'll link more examples in the replies.
This is all true: but the top prize is the whole workflow. Robots are one component. Models are another component. To own the process itself, handling edge cases, and delivering outcomes by selling results, is how a company will capture the lion's share of maximized value.
State of Physical AI right now:
1. Capital is pouring in. Startups are coming out of stealth and raising hundreds of millions.
2. We’re moving from demos → deployments. Reliability, uptime and actual ROI are starting to matter more than flashy demos.
3. Specialized robots will likely find PMF much sooner than general-purpose robots. We’re already seeing them deployed across agriculture, factories, mining and industrial inspection with companies like @carbonrobotics, @PathRobotics, @maticrobots and @GeckoRobotics.
4. Physical agents feel like the next frontier. Not just completing a single task, but owning entire physical workflows. @DynaRobotics is doing some really interesting work here.
5. Robot design is getting much more interesting. Form factor should follow the job, not necessarily the human body. Really like dyna's Taku design here.
6. Simulation + evals are becoming a critical part of the stack. Testing, validating and iterating on real hardware is slow and expensive, while measuring real-world reliability is still largely unsolved. Companies like @antiochrobotics are working on this layer.
7. Physical AI is an incredibly difficult industry. Models, hardware, simulation, evals, manufacturing and deployment all need to work together before we see robots operating reliably at scale.
Still early, but feels like we’re entering the most interesting phase of robotics yet.
Dhruva Rajendra retweeted
We need to get the shops with 10-19 employees in a position where they can produce 3x-5x their current output without requiring a corresponding increase in headcount.
Many of these shops supply OEMs & make key parts/ components that go into bigger things/ equipment.
Over the next 5-10 years, the bigger companies/ OEM’s will increase their domestic production output. That means they’ll need more of the parts/ components to assemble the finished goods.
I expect ~80% of the shops with <10 employees will disappear over the next decade. Few are willing or able to make the drastic transformation that will be required to compete & succeed in a world with widespread production automation.
The work from the 80% closing & the increased demand needs somewhere to go. Highly automated shops with 10-19 employees that employ technologies & systems built around the decades of human knowledge, will be able to ship 3x-5x what they do now. And they will do so without the administrative bloat, bureaucracy & analysis paralysis that companies with 100-150 people experience.
We need to give the shops in the 10-19 employee range, this coalition of willing, driven, & obsessed manufacturers, more support.
(Note, I’m specifically not talking about defense suppliers)
America has 16,876 machine shops.
83% employ fewer than 20 people. Just nine employ more than 500.
New defense companies are moving from prototypes to mass production, and every missile, drone, ship, and rocket they ultimately produce needs to run through one of these small shops.
That friction is a national security liability.
Connor Love and Collen Larson on the next generation of American manufacturing that gets built by smoothing it: a16z.news/p/the-case-for-the…
Dhruva Rajendra retweeted
There's a looming physical AI crisis.
I'm as AI pilled as it gets and we must keep accelerating
But ...
I know "The" Cyber and I know AI.
For every $1,000 spent on AI in 2026, about $1 goes to securing it.
73% of Americans say AI companies are not doing enough to prevent serious harm.
In '24/25 AI went from helping useful tooling for red teams to critical infra for the most advanced cyber operations we know of publicly.
In '26 agents began demonstrating adverse behaviors at scale while pursuing incrementally more challenging goals.
Robotics today is where AI Cyber was in '24 and the same intelligence (arguably better) is moving into the machines.
@robocurve: Fascinating testing using real robot arms, Numerous LLM's completed harmful tasks such as stabbing a doll or mixing bleach and ammonia. nitter.cf/chooi_jeq/status/21011…
@olivier_boschko: Root access over Bluetooth and the cloud. One hacked humanoid can infect others within range. nitter.cf/olivier_boschko/status…
@AlexRobey23 and the RoboPAIR team jailbroke three LLM-controlled robotic systems, including a Unitree Go2 robot dog, 100% of the time. (almost 2 years ago btw) nitter.cf/AlexRobey23/status/184…
IMO the window is '26-'28 before deployment gets ahead of the risk. It took ~20 months from one of the best known AI discovered exploits to models breaching production systems in some of the largest foundation model labs.
Cybersecurity has become a market because the harm is measurable. Physical AI risk still sounds like science fiction when you say it out loud.
We can make robots safer while accelerating deployment. We just have to put serious resources behind it.
What to know what people take seriously? Follow the $$
Where does money invested into the AI buildout actually go?
For every $100 flowing into the supply chain:
- $50 to chips
- $20 to power
- $15 to networking
- $15 to cooling, buildings, and land
More charts in State of Markets II: a16z.news/p/state-of-markets…
Dhruva Rajendra retweeted
i hear (frequently) from people who try to over-intellectualize reindustrialization through various lenses in finance, economics, labor, technology, "free" market competition, etc. it's quite simple and doesn't require hardly any justification at this point. let me paint you a picture: the final crescendo of super intelligence is total mastery of atoms, and a semi-likely multipolar world of superpowers. the intermediate step that's missing right now is to build as much net-new tech-enabled capacity as possible. the transfer of tribal and process knowledge into compute is critical and very similar to how we've learned to control self-driving cars and robots. this is not something even the smartest model can predict/simulate with the level of necessary precision for real production, and these are glaring open deficiencies in all frontier models until they have data (live factories) to feast on. that learning rate will increase vastly as a function of overall tech-enabled capacity, and potentially displace frontier labs because they lack production experience and actual capacity themselves once intelligence is commoditized. the faster we do all of this the faster we create the potential for actual abundance.
Dhruva Rajendra retweeted
Over the last 4 years, lots of people have asked me "why Forterra" or "why do you care about the ground domain so much."
Well, we came from the infantry and despite the hype around drones/maritime, wars are still decided on the ground. It is the dirtiest, most dangerous layer of warfare where human beings have always had to be the blunting force.
So @JoshAraujo and I wrote a paper about it (yes, ex-Marines can write.)
forterra.com/why-we-build
Dhruva Rajendra retweeted
Robotics is about to discover why there aren’t 43 Teslas.
Over the next 3-5 years, the fundraising story for Robotic companies will shift from lab demos to the factory floor.
Demos → paid pilots → volume production → profit.
Each step will eliminate companies that looked unbeatable at the previous one.
I helped scale manufacturing at @Tesla and @Rivian. Twice through that chasm.
It’s bloody hard. The demo, while necessary, barely hints at what’s coming.
Dhruva Rajendra retweeted
Physical AI requires deep knowledge of the real world: the industrial base as it is, and what it could become if the vision is executed.
Most importantly, it is human-centered. You have to build a coalition.
Physical AI is a community play.
We are fixing this problem at Deterrence inside the defense industrial base (and grateful for the Tesla alumni helping us, thanks @elonmusk ).
Human learning is too precious to let atrophy: we can preserve it and expand it, if we use machines correctly.
