Exploring the future of work, learning, startup ecosystems & film. Principal @join_ef Alum @ycombinator (S15) @trygigster @rossintel
Bangalore/San Francisco
Joined March 2010
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Akash Venkat retweeted
Week #4 of building @NeuralMiles in public: Just one month of building, and we are ready to deploy on testing sites!!!
To get a sense of what is needed for real sites, we successfully tested digging a specific area via tele-ops.
Updates:
a. Completed digging an area via camera feed to understand operational issues
b. Benchmarked latency from different locations
c. Simplified hardware designs
d. Signed 2 NDAs for site deployments and business outreach
Failures:
a. Tried tele-operating from the US but faced problems, so we need to iterate!
Tele-operating machines from labour-abundant geographies will provide an immediate remedy to constrained operations in many western countries without high capex needs, while building a solid platform to build autonomous algorithms with the data collected.
Would really appreciate intros to companies involved in mining and construction excavator operations! If you are following our journey and can help, our DMs are always open.
Except the fact that @deepboliya fell with his laptop while testing 🙃, it has been a great week of learning!
NeuralMiles(EF S26)
#robotics #startup #physicalAI #autonomy #construction #mining
Casual 7M in revenue run rate added during the fundraise by the formidable @vaibhav514g :)
Akash Venkat retweeted
Ather is actually a very interesting story, and I think this tweet misses a big part of it.
Look at who funded Ather.
Sachin Bansal invested ~$53M, starting as an angel and then leading its Series C and Series D. Imagine an Indian founder personally putting that much money behind a hardware startup leading growth rounds.
Then there's Hero MotoCorp. A traditional Indian two-wheeler company backed a startup that could eventually compete with its own business. Hero started investing in 2016 and kept doubling down. Today, its stake is worth billions of dollars.
Between Sachin and Hero, nearly 50% (~$250Mn) of Ather's capital came from two backers you would hardly expect to make such investment. Think about how unusual that is. Neither was a traditional VC fund, yet both played a huge role in getting Ather to where it is today.
And it's not just Ather. Mukesh Bansal invested ~$1.5M in Skyroot Aerospace very early. That bet is now reportedly worth ~$80M, including partial exits. Nearly 60x.
I think we should spend more time celebrating people who are actually making these contrarian investments rather than criticizing those who aren't. The former encourages more people to take risks. The latter doesn't really help anyone.
Why are Indian VCs so afraid to take risk and make money?
Someone should go to India and take risk and make money.
Our story - corememory.com/p/a-new-india…
"Finding the 'ugly duckling' that's going to become a swan" is a pretty honest description of the job. But at a human level, there is real joy in seeing 'undervalued'/misunderstood folks compound as they realize more and more of their potential.
Lonely and Right
Keynes said picking stocks is like a newspaper beauty contest: you don't win by picking the prettiest face, you win by picking the face everyone else picks.
Ninety years on, that's still the most honest description of my job which is sad because I think I know beauty.
Now the tortoise and the hare. Everyone tells it as "slow and steady wins." That's not the story. The story is that for 90% of the race the tortoise is losing. Badly. In public. The hare is at the cocktail party. The hare's LPs are thrilled. The hare is on the list. Then the tortoise wins at the end, and we tell it to children, and we leave out that the tortoise spent the whole race feeling like a fool.
So be honest. Would you rather win 90% of the race and lose at the end, or lose 90% of the race and win? Counted in years of your one life, the hare has the better deal. That is venture capital right now. The young partner in a venture firm would rather be the hare and jump to the next firm than be lonely as the tortoise.
The consensus deal is the hare. Expensive, crowded, fun. Everyone at dinner knows the company. The marks go up, the capital piles in, and for years you feel like you're winning, right up to the moment the music stops, which is the only moment that counts. The contrarian deal is the tortoise. It pays only if you're contrarian and right, and you find out which at the end. Until then you're on no list, nobody at dinner has heard of your company, and you look slow. Early and wrong feel identical from the inside. That's the whole difficulty.
So the real trade was never consensus versus contrarian. It's finding the thing that's contrarian today and consensus in eighteen months.
Buy the tortoise, then watch it grow legs. Cheap entry, then the multiple expands when the crowd arrives, and then you're back at the cocktail party being congratulated on your foresight by the same people who thought you were lonely.
Lonely and right, then crowded and rich.
That's the business. Finding the ugly duckling that's going to become a swan - that's where the alpha is.
What's contrarian now that I think flips? Held loosely:
1. Outcomes, not tokens. Everyone is selling the enterprise intelligence by the pound. The enterprise doesn't want intelligence. It wants the claim processed, the invoice reconciled, the ticket closed. One in five companies has AI in production; one in fourteen has rebuilt the factory. Whoever sells the other thirteen a finished outcome, priced like a result and not like a utility bill, wins the decade. It looks like a services business on the way in. So did Amazon.
2. Security that gets paid for the breach that didn't happen. The attackers have agents now, so every company on earth is about to be robbed at machine speed, and the defender's product is still a dashboard. The bet is the company that stops selling the dashboard and starts selling the result: we handle it, we're on the hook. Security and insurance were always the same business. Someone is about to notice.
3. The specialist labs. Not the frontier, which is consensus at a trillion, but the labs that do one thing for one industry at a hundredth the price.
4. The boring half of the physical world. Robotics is loud. Inspection, maintenance and warehouses are quiet, and the revenue lives in the quiet part.
I may be wrong about all four. That's rather the point. If I were sure, so would everyone, and then it wouldn't be a trade. It'd be a cocktail party.
Akash Venkat retweeted
New Substack post with @soumitrashukla9. Work has never been just about the income earned sans effort costs, as typical in economic models. Key aspects of people’s identity, their meaning, and sense of agency are all wrapped up with what they do for a living.
There has been a lot of conversation lately about how technological change, particularly AI, may disrupt this relationship, from Ezra Klein's column on his own mathematician father’s grapplings with losing meaning in mathematics, to Francis Fukuyama's post on AI, agency, and recognition. In this piece we take a historical perspective on how technological transformation has disrupted identity and meaning by affecting jobs, and how workers and society responded to these changes.
Much of the debate around AI and jobs has centered on wages and material consequences, but losing one's work is also intertwined with how people view their place in society. Most people know about the Luddites—skilled weavers and knitters—who protested the devaluation of their mastery. What’s less known is that the Luddites were actually experts in using the machines as well, and that their protests prompted prominent economists like David Ricardo to rethink the role of automation in society, and later inspired the Arts and Crafts movement. A century later, when recorded sound displaced 20,000 cinema musicians, their campaign against "canned music" and two-year recording ban pressured labels into funding live performance.
Soumitra and I have been working on versions of this essay for months. He led the piece, and we’re both really excited to get it out there.
Link to the post here: aleximas.substack.com/p/tech…
The most revealing part of this conversation between Jon Stewart and Jacob Coxon wasn't Coxon's warning about A.I. doom, but his description of A.I. utopia, and Stewart's incredulous response. I wrote about it — and Francis Fukuyama's "Last Man," and A.I. easily solving the problems that bedeviled my father's field of mathematics for decades — here: nytimes.com/2026/10/18/opini…
Akash Venkat retweeted
Data allocation is a new dimension for capital allocation: keeping data internal vs exposing it externally (and how) is akin to returning capital vs letting it compound (and how).
The best CEOs will always be brilliantly creative allocators.
Akash Venkat retweeted
I read this somewhere and agree with it: "Too many AI startups are selling picks and shovels. Not enough are actually mining the gold."
Akash Venkat retweeted
What are the odds this post gets 1 million views?
@v4vihan's answer: make a market on it.
8 years after starting a quant trading firm from his college dorm and scaling it to nearly $100M AUM, he’s now building @Omnibook_xyz: a way to turn financial views into trades.
Already on testnet:
- 25,000 registrations
- 4,000 daily active traders
- 40% week-over-week volume growth
2 weeks ago, Vihan pitched Omnibook at Demo Day.
Watch the full pitch on the Demo Day portal.
Big congrats to @alexnhaus and Edmundo. Helicon is doing incredible work!
Helicon is building factories to industrialize composites.
We raised a $16.5M seed round led by @alley_corp (@abemurray /@JonesBrannon) with participation from @joshbuckley, @chalfs, @gokulr and other top investors.
We’re ramping production at our factory in Los Angeles to deliver against millions of unmanned systems that need to be built domestically.
We're building a world class team. Join us. helicon.tech
Akash Venkat retweeted
Helicon has raised $16.5M to industrialize advanced composites, with backing from AlleyCorp, Josh Buckley, Mike Chalfen, Charlie Songhurst and others.
Helicon is building a new generation of factories that combine specialised manufacturing models and robotic assembly to produce advanced composite structures at scale, making it possible to manufacture faster, iterate more quickly and reduce reliance on highly specialised manual labour.
Last April, we filmed @alexnhaus and Edmundo in San Francisco, just before they pitched at EF Demo Day, to hear about their vision for the future of manufacturing.
Six months later, they're taking that vision to the next level.
Proud to have backed Helicon from day zero at EF.
Akash Venkat retweeted
"I believe the world is about to witness a tidal wave of technological innovation emerging from India."
The world has a very outdated view of what India is, and I think many people are going to be utterly shocked when this wave that you're describing @anandmahindra finally reaches them.
"This robot was made in India? Even the actuators?!"
"This specialty chemical was discovered and manufactured by an Indian company? And they've got a patent for it too?!"
"The electric vehicle I love driving so much was built indigenously in India? Along with the battery cells?!"
So much has changed in just ~5 years. It's mind-boggling even for someone like me who has called India home for nine years and has been laser-focused on startups. Young people are entering adulthood with no self-imposed mental limitations. No ceilings. They believe they can do anything, build anything, be anything.
I believe they can too.
The Indian startup ecosystem is on steroids.
And what excites me most is that this isn’t just about software. It’s about deeptech. Manufacturing technology. Hardcore hardware. Cutting-edge chemical processes.
A new generation of Indian entrepreneurs is building the capabilities that could allow us to leapfrog entire stages of technological development.
These videos, courtesy @ashleevance & @RuntimeBRT, offer just a glimpse of what’s being cooked up in labs, workshops & factories across India.
And right on cue, Rajan Anandan, former head of Google India, & Saurabh Srivastava, one of India’s pioneering entrepreneurs & institution builders, have released a book calling India a ‘Lift-Off Nation.’
An apt description.
Because I believe the world is about to witness a tidal wave of technological innovation emerging from India.
And it’s being powered by young entrepreneurs who are daring to build things we once assumed could only be built elsewhere.
My salute to them.
#MondayMotivation
Akash Venkat retweeted
The email Larry David sent when he caught wind that @blakejharrisNYC wanted to write his biography.
Larry David is one of America’s unlikeliest icons, and one of history’s purest founders.
In our latest profile, Correspondent-at-Large @blakejharrisNYC—who spent six years talking to Larry for his new book—explains what makes Larry so special, and why it took him so long to break through.
Larry was a mediocre student with a middling social life. His first two jobs—selling bras and driving cabs—ended quickly. At 25, he called his mother from a hotel room: “Ma, nothing good is ever gonna happen to me.” She urged him to become a mailman.
Soon after, he found what he thought would be his calling: stand-up comedy. His material was funny, but he rarely finished a set. If somebody in the audience looked at their watch, or didn’t laugh when he thought they should, Larry stormed off.
14 years later, aged 39, he got a break. He was asked to write his first sitcom script for a big new Showtime series. Only an eighth of what he wrote survived the edit, but the episode was still his. He was broke, and the writing credit could have sparked his career. But Larry refused to put his name on it.
Even with Seinfeld, Larry nearly hit self-destruct. The pilot performed worse than almost any other show NBC had ever tested. Still, the network ordered four episodes, one of the smallest orders in TV history. Larry barely made it through them.
When the studio installed a showrunner to oversee the scripts, Larry endured the man’s notes, then said “NO. I’m not doing one thing,” and walked out. In the summer of 1991, with the show finally starting to work, NBC wanted Jerry and Elaine to get married and have a baby. Larry quit in the parking lot and wished everyone good luck. Jerry stood with Larry, and NBC caved.
Larry’s friend and comedian Bill Scheft put it this way: “He doesn’t [act this way now] because he has fuck-you money. It’s the opposite. He has the fuck-you money because he’s always acted this way.”
Read the full profile by Blake, author of Emperor of Nothing: The Making of Larry David.
colossus.com/article/many-fa…
Agree with this 💯
New on the blog:
— Room for ambition
A late-night conversation at a startup office in Finland has stayed with me for almost twenty years.
Some thoughts on ambition, getting good at something, and the people around you.
karrisaarinen.com/posts/room…
Akash Venkat 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.
Akash Venkat retweeted
7/ These findings show that representation of conscious and suppressed content alternates every ~ 60 ms. Thus conscious perception does not reside in a specific brain region, but rather a specific temporal phase.
Akash Venkat retweeted
I was utterly taken with YOU CAN SEE EVERYTHING. It's a movie about the impossibility of really knowing another person, and a masterpiece of inward-folding cinematic misdirection, like an Escher painting about Escher paintings.
It is also a rather stunning act of empathy.
I was totally floored.
reason.com/2026/10/16/the-el…
Akash Venkat retweeted
At 2am, Vaibhav kept noticing the same person was still working: Amish.
They weren’t cofounders yet. They were building completely different things. But after enough late nights watching each other operate, Vaibhav knew Amish was the person he wanted to build with.
A few months later, they’re building Saturn Labs.
On the way to Demo Day, Isabel jumped in a Waymo with Vaibhav to talk about finding his cofounder, moving from India to SF, and why being here became essential to building the company.
Full conversation in thread.