Anagh Prasad

Investor at Accel. Math & Street food romantic.

Check out All tweets/retweets are personal

[Anagh Prasad @anagh_prasad]

Okay here's the hot-take: @ChatwithGPT or any LLM-based bot is NOT a replacement for search engines, aka "@Google Killer" that's doing the rounds on @Twitter.

Here's why I think so:

[Anagh Prasad @anagh_prasad]

@prathoshap WOW.

[Varun Nair @_varunnair]

If anyone tells you they are bearish on all robotics data co., send the misinformed person here

[Varun Nair @_varunnair]

https://t.co/2UQASWWXqk

[Anagh Prasad @anagh_prasad]

So much positive energy ✨ May the hustlers win!

[Darshita Patankar @_darshi]

Hi, it's Darshita. I am a designer.

And I'm pitching myself in public 'cause I'm done with a year full of anxiety.

Everyone says design is dead and AI killed it. But the real parasite was hiding in plain sight.

So here's my film, DESIGN IS DEAD. She's bold, fearless and very PINK 💖

Hiring? My DMs are open.

[Anagh Prasad @anagh_prasad]

https://t.co/u69cMgcGj1

[Ronnie Screwvala @RonnieScrewvala]

Deep-tech hardware, engineered in India, solving a problem for builders around the world 👍

Happy to be part of @origin_autonomy’s journey via Unilazer Ventures

[Vaibhav Tulsyan @xennygrimmato_]

@tusharmakkar08 is one of the smartest people I know.
Undoubtedly @origin_autonomy is going to be a generational company.

[Accel in India @AccelIndia]

Construction is 13% of global GDP, yet its productivity has fallen behind the rest of the economy since 2000.

The industry never automated because robots need predictable conditions, and job sites don’t offer them. Every project has a different layout, different materials, and a different stage of work. Recent advances in physical AI let machines handle that variation.

We’re investing in @origin_autonomy to bring it to the built world. Origin has assembled the largest indoor construction dataset anywhere and used it to train robots that adjust to every new space. They’re already deployed across the US.

Between them, the founders cover business, AI, and hardware. @yogesh_origin built and sold his first company to Amazon, @tusharmakkar08 led core AI teams at Eightfold, and @ashishxdaga has spent a decade in robotics.

@anagh_prasad and @subratamitra explain how Origin’s model works and why we backed it: https://t.co/npffNPz9Bf

[Anagh Prasad @anagh_prasad]

@origin_autonomy is finally out of stealth. And boy, it'd be huge!

[Yogesh Ghaturle @yogesh_origin]

Thrilled to announce that @origin_autonomy has raised $20M in total funding, co-led by @Accel and @ZacuaVentures.

Excited to be building this with @ashishxdaga, @tusharmakkar08 and the super talented team at @origin_autonomy. We’re building general-purpose robots for interior construction.

Special thanks to @johnkoetsier at @Forbes for covering our journey.

News Article: https://t.co/YxjoyLdAfi

[Yogesh Ghaturle @yogesh_origin]

A sneak peek. Because keeping this to ourselves was getting difficult. 🤐

[Anagh Prasad @anagh_prasad]

The next decade in 🇮🇳 will be hot hot hot 🚀🚀

[Abhay Karandikar @karandi65]

A significant milestone for India’s deep-tech ecosystem.

Delighted that ₹1 lakh crore Research, Development and Innovation Fund (#RDIF) has announced its first cohort of 14 deep-tech Alternate Investment Funds (AIFs) and 5 Focused Research Organisations (FROs) as Second-Level Fund Managers (SLFMs).

[Anagh Prasad @anagh_prasad]

Just video data is *really* unlikely to lead models into autonomous manipulation. We'd see a not-so-gradual shift in data spending towards Teleop data and/or building ridiculously good world models with accurate contact physics.

[Jiahua Dong @JiahuaDong]

[1/7] We trained a world model on 30,000 hours of first-person video.

It learned exactly where the hands go. What happens to the things they touch, much less so.

Going from 300 to 30,000 hours did not narrow the gap, and a 4× larger model barely did either.

[Animesh Chouhan @animeshsingh38]

this is what college students in india should be doing instead of political theatrics.

[Vance Spencer @pythianism]

The winning robotics data business has a much higher terminal value than investors are underwriting today.

Robotics will be a long race with many twists and turns, but when the ChatGPT moment hits, the winning data player will be in a very strong strategic position to capture token value. They will have large revenue, a strong balance sheet, talent density, deployments experience, and hardware supply chain knowledge. When it comes time to blitzscale they will be amongst the players that can capture the most value.

The reason this is misunderstood today is because most people are using traditional LLM data businesses as a prior for robotics data businesses and what their terminal value could be. I posit this is incorrect framing. Because the model and harness are integrated in LLMs, there is very little room for data players to play meaningfully in the token value flow in the long term.

In robotics this is exactly the opposite, the model and harness (hardware) are separate, the pretraining era is just beginning, there are many more dependencies on distributors/deployers, and there is a large surface area for other players than just models to capture value. Open source will also play a larger role earlier in the robotics race, as all prior LLM learnings will be incorporated as the race is run. This also creates opportunities for long term infrastructure players.

There is a massive amount of terminal value in winning robotics data and participating in the path of the industry from a position of strength.

From first principles:

The endgame of robotics will take place over the next decade. Empirically, today, no player has enough data, enough model intelligence, or sufficient hardware to facilitate a fast revenue takeoff in the field. No player has reasoned through the right sales motion either, which can take years to develop pilots and move to production. Serious players are correctly focused on scaling up pretraining/deep learning vs. who has the best model, deployment, or hardware today.

The robotics data business takes participants on a global hunt to build data, supply chains, and hardware. Along the way it also teaches deployment and deep knowledge of SOTA model development. Winning data participants also get the first crack at the still nascent robotics RL market, which will likely be the next large revenue stream after pretraining. As they compete to win data, data players must develop incredible talent density across both Asia and the US. The winning data business will have both Asian and North American DNA. Canada 🇨🇦 encompasses an interesting cultural mixture of both of these parts of the world. Bullish Canadian Robotics Mafias, more specifically Waterloo/Toronto.

So:

What would constitute a strong strategic position to capture value once the ChatGPT moment of robotics hits? You need a large revenue base, a strong balance sheet, and talent density + experience across data, deployments, hardware, supply chain, and RL. The hyperscalers, the frontier labs, and the winning data player all qualify.

Anyone under time pressure in what will be a long race is in a weaker strategic position in my view. Given the costs of integrated hardware+software+deployment, this would actually be most robotics-native players. They will depend on VC interest, which can be fickle.

[Anagh Prasad @anagh_prasad]

Good to see some adult energy finally entering the chat..

[Satya Nadella @satyanadella]

https://t.co/MjwFy73LOF

[Anagh Prasad @anagh_prasad]

@cosign @ShreenabhA this guy's just 22 & fresh out of CMU. But it's been 11 yrs since he published his first book, 6 yrs since his work was endorsed by @gdb, & 5 yrs since @narendramodi awarded him india's highest honor for teens. One of a kind!

[Anagh Prasad @anagh_prasad]

The best crisp articulation of robotics data landscape I've read so far! Worth that bookmark.

[Varun Nair @_varunnair]

Robotics Data is either barely understood or misunderstood. Here is a map for all robotics or data enthusiasts. https://t.co/QonDZyUIby

[Caleb @caleb_friesen]

I used to drive past this spot twice a day on scooter from 2019 to 2024 to drop my wife off and pick her up from office.

It never got this bad.

Something has gone horribly wrong in Bengaluru's waste management system. That, or there is zero enforcement of illegal dumping laws.

[Anagh Prasad @anagh_prasad]

https://t.co/6HRvFy6vcA

[GREG ISENBERG @gregisenberg]

https://t.co/ycmy4OWzKJ