Digital marketing and tech enthusiast
San Francisco, CA
Joined May 2008
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Abhishek Aggarwal retweeted
An exciting milestone for AI in science: Our C2S-Scale 27B foundation model, built with @Yale and based on Gemma, generated a novel hypothesis about cancer cellular behavior, which scientists experimentally validated in living cells.
With more preclinical and clinical tests, this discovery may reveal a promising new pathway for developing therapies to fight cancer.
Abhishek Aggarwal retweeted
🪩The one and only @stateofai 2025 is live! 🪩
It’s been a monumental 12 months for AI. Our 8th annual report is the most comprehensive it's ever been, covering what you *need* to know about research, industry, politics, safety and our new usage data.
My highlight reel:
Abhishek Aggarwal retweeted
AI agents are utterly changing software project timelines right now, and thus what kind of work we can start to work on.
The kind of projects that used to be estimated at months are now being scoped to days or weeks. And when they’re not, it’s usually just because we’re not being creative enough or just pushing AI enough.
It’s kind of crazy the speed at which the entire practice of engineering is changing. The impact is that our software over time will be able to just be vastly more capable and useful than it is today.
Historically, the majority of time in product development gets chewed up on the most urgent, but often least interesting, areas of work. Fixing bugs, upgrading a library, improving performance, improving a feature from a customer request, and so on. You’re always aiming to carve out more time to do the bigger ideas, but it’s very hard.
Now, because you’re able to just work through the obvious backlog of tasks and projects far faster, you now get room to deliver on the things that you never would’ve gotten around to otherwise.
And because AI makes it far faster to prototype, you can explore ideas that would be hard to prioritize, but now you can see how useful they end up being far faster. This has pushed the ability to experiment by like 10X on projects because the cost of failure is much lower.
As a result, it’s much easier to say “yes” to the kind of ideas that always fell by the wayside before.
Interestingly, this lowering of the barrier to building more will mean that there’s an even higher premium on good product management and design. In a world of infinite choices, determining what to build - and how it should look and work - is going to matter more and more.
Abhishek Aggarwal retweeted
In the near future, your Tesla will drop you off at the store entrance and then go find a parking spot.
When you’re ready to exit the store, just tap Summon on your phone and the car will come to you.
FSD V14.1 Spends 20 Minutes Looking For Parking Spot at Costco
This video is sped up 35x once we get hunting for a spot and during that time the car pulls of some really inellegent moves while searching. We did not once pass any empty available spots, the only issue is we didn't go deeper into the parking lot to search and we didn't sit and wait for people who are unloading their carts.
I will post the longer narrated copy of this in a bit - but this honestly is incredible... the other thing worth noting is during the entire 20 minutes we searched for a spot not ONCE did a human driver or pedestrian look at the car and question what it was doing, we fit in the entire time very naturally.
Abhishek Aggarwal retweeted
Today we introduced Gemini Enterprise, built with our most advanced Gemini models. It allows you to chat with your company’s documents, data and apps as well as build and deploy AI agents, all grounded in your information and context.
Have a look at how it helps you build an agent for almost anything:
Abhishek Aggarwal retweeted
We’re bringing AI Mode in Google Search to 36 new languages and more than 40 new countries and territories, bringing it to over 200 total, including many across Europe. AI Mode uses the advanced reasoning and multimodal understanding of our custom Gemini model for Search to grasp the subtleties of languages and be helpful in all the languages we introduce.
Abhishek Aggarwal retweeted
Google, a private for-profit software company, has had five Nobel Laureates work for them.
Most universities have had none.
Abhishek Aggarwal retweeted
I attended a vibe coding hackathon recently and used the chance to build a web app (with auth, payments, deploy, etc.). I tinker but I am not a web dev by background, so besides the app, I was very interested in what it's like to vibe code a full web app today. As such, I wrote none of the code directly (Cursor+Claude/o3 did) and I don't really know how the app works, in the conventional sense that I'm used to as an engineer.
The app is called MenuGen, and it is live on menugen.app. Basically I'm often confused about what all the things on a restaurant menu are - e.g. Pâté, Tagine, Cavatappi or Sweetbread (hint it's... not sweet). Enter MenuGen: you take a picture of a menu and it generates images for all the menu items and presents them in a nice list. I find it super useful to get a quick visual sense of the menu.
But the more interesting part for me I thought was the exploration of vibe coding around how easy/hard it is to build and deploy a full web app today if you are not a web developer. So I wrote up the full blog post on my experience here, including some takeaways:
karpathy.bearblog.dev/vibe-c…
Copy pasting just the TLDR:
"Vibe coding menugen was exhilarating and fun escapade as a local demo, but a bit of a painful slog as a deployed, real app. Building a modern app is a bit like assembling IKEA future. There are all these services, docs, API keys, configurations, dev/prod deployments, team and security features, rate limits, pricing tiers... Meanwhile the LLMs have slightly outdated knowledge of everything, they make subtle but critical design mistakes when you watch them closely, and sometimes they hallucinate or gaslight you about solutions. But the most interesting part to me was that I didn't even spend all that much work in the code editor itself. I spent most of it in the browser, moving between tabs and settings and configuring and gluing a monster. All of this work and state is not even accessible or manipulatable by an LLM - how are we supposed to be automating society by 2027 like this?"
See the post for full detail, and maybe give MenuGen a go the next time you're at a restaurant!
Abhishek Aggarwal retweeted
Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform.
Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform.
We are releasing three superhuman AI Scientist agents today, each with their own specialization:
A general-purpose agent (Crow);
An agent to automate literature reviews (Falcon); and
An agent to answer the question “Has anyone done X before” (Owl).
We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below.
The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows.
Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it!
The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today.
The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. @m_skarlinski, @andrewwhite01, @_tnadolski, Remo Storni, @semajazarb, @ludomitch, @MichaelaThinks, as well as @jasonjoyride and his team for making such fantastic videos of us!
Abhishek Aggarwal retweeted
The Urgency of Interpretability: Why it's crucial that we understand how AI models work darioamodei.com/post/the-urg…
Abhishek Aggarwal retweeted
framework to find the next cursor-like billion dollar idea:
1. look for skilled professionals who spend 6+ hours a day in a specific software tool - the more arcane, the better
2. identify tasks within their workflow that require deep expertise but follow patterns - these are ripe for ai assistance
3. focus on industries with high hourly rates ($100+) where time savings translate directly to revenue or cost reduction
4. seek workflows with specialized vocabulary that generic ai struggles with but domain-specific ai could master
5. prioritize tasks that involve both creativity and technical constraints - the sweet spot for human-ai collaboration
6. build for existing interfaces people already know rather than forcing new workflows
7. start with a tiny, almost embarrassingly specific niche - e.g., not "legal" but "divorce proceedings in california"
8. solve for the 80% case that's repetitive, leaving humans to handle the complex 20%
9. the best opportunities feel like "ai-enhanced superpowers" rather than "ai replacements"
10. if it can be on the cloud, that's a good sign
Abhishek Aggarwal retweeted
Ironically, one symptom of deindustrialization is that many commenters have never actually managed a physical business.
So. Suppose your US company imports $1M of high quality parts, and adds in its own components to produce finished goods sold for $1.2M per batch. Your gross profit is $200k per batch.
But wait! Suddenly a new 30% tariff is imposed on that $1M of parts. You now have to fork over $300k to customs before you sell anything. That’s cash you probably don’t have. Oh, and even if you do sell everything, you’re now losing $100k per batch.
With a sinking feeling, you realize your profitable business which you somehow managed to keep in America all these years has suddenly become unprofitable.
You post online about how bad this is but get shouted down by an angry mob, convinced that capitalists like you should die. You can’t tell nowadays if they’re on left or right.
Moreover, you don’t have the time, money, skills, or tools in house to build that $1M of parts yourself. You are being asked to do the equivalent of growing a maple tree when all you needed was a little maple syrup. So now you are faced with several tough choices.
(1) First, you may need to go into debt or fire people to quickly come up with the $300k in cash to pay for these surprise tariffs at customs. Even if the tariff might go away, it might not, so you have to get the cash somehow or risk having your shipment impounded.
(2) Next, you might need to reduce quality to stop losing $100k on each batch. You could order the lower quality $750k parts, grimace and pay 30% tariff at customs, and hope you can build and sell for the same price of $1.2M per batch despite the lower quality.
(3) Alternatively, you could keep the quality parts at $1M and instead raise prices to $1.5M per batch to get back your original margins of $200k per batch, which you need to pay employees after all. But that’s a big hike that your customer will probably not welcome, given that he’s likely dealing with his own tariff shock.
So: these tariffs don’t really give an incentive to build in the US. Because it’s far more expensive to build a screw factory than to pay even high tariffs on a foreign screw.
Instead what they likely mean is debt, layoffs, lower quality, and higher prices for any US company that buys parts abroad.
Just to understand how common that is:
Crappy service again on @airindia flight from Delhi to SFO. Business class sear doesn't recline and the options given by airline is to downgrade to economy or take the next flight.
Abhishek Aggarwal retweeted
AI is the next internet boom.
Learning how to leverage AI is the smartest thing you can do right now.
Here are the top 8 FREE courses/resources on AI to get you massively ahead:
Abhishek Aggarwal retweeted
If you didn’t get invited to the @FoundationCap #GAI unconference and are a serious player in the space, ping one of the partners to get on the list. In the meantime, here are some key takeaways!
And if your startup deserves to be on the #GAI 25 list, do apply! foundationcapital.com/top-ta…
