Amit Agarwal

AI @indiamart | Previously @Cashify_ @HRLLaboratories @CarnegieMellon @GeorgiaTech

[Aakrit Vaish @aakrit]

Looking for a team of 10-12 cracked researchers & engineers to build a new AI Lab in India.

Funding and compute secured.

DMs open.

[Aakrit Vaish @aakrit]

@SarvamAI @emergentlabs @composio @GnaniAi @dashversetech @Aeos_Labs @AgraniLabsInc @Aqqrue @Deccan_AI @pre6ai @SimplismartHQ @smallest_AI

@anshulbhide @mdebdoot @GarvitJuniwal @NirantK @SriramRajamani

@177pc @ActivateSignal

[Catherine Yeo @catherinehyeo]

Love seeing Naomi Osaka honor the CLRS Algorithms textbook at this year's Met Gala

[kache @yacineMTB]

@karpathy you can outsource your thinking but you cannot outsource your understanding

[Mint @livemint]

IT Minister Ashwini Vaishnaw at Davos counters IMF Chief for calling India a second-tier AI power.

Watch!

[Amit Agarwal @amtagrwl]

@ArpitDevMathur Test

[Rajesh Sawhney 🇮🇳 @rajeshsawhney]

Congrats team @Arivihan on raising the latest $ 4.17 round from @Prosus_Ventures and @accelindia and @gsf_fund

We had incubated this cutting edge AI startup even before Gen AI wave took off in India, proud to see its evolution as the AI tutor for hundreds of millions of Indian school going children from Bharat.

"Traditional coaching models cannot scale to serve Bharat's massive student population at affordable price points. Our AI-first approach makes education accessible and truly personalized for every student, regardless of their location or economic backgrounds.”

More on: https://t.co/tccRdUC9tf

[Amit Agarwal @amtagrwl]

@grok @rsreddy12321 @refsrc What about Zepto?

[Ewan @ewan_tindale]

@ericzakariasson Me using cursor lately:

[Amit Agarwal @amtagrwl]

@1Password Git SSH signing fails silently: Commit ok, log --show-signature shows "No signature". Agent auth works. .gitconfig looks correct (verified origin). op-ssh-sign runs (trace ok), but no sig applied. macOS 15.4, 1P 8.10.70, CLI 2.30.3, Git 2.39.5. Help? #Git #SSH

[TBPN @tbpn]

"Starting an AI agent infra company is what you do as a developer if you have no other ideas." - @swyx

"I think @ycombinator should take this as a warning sign."

"MCPs (Model Context Protocol) make integrations easier. What problems they solve is still an open question."

"Did you get excited about when REST was invented? No, but you got excited about all the applications that REST enabled."

[Amit Agarwal @amtagrwl]

@salvadelacidra @grok where is this?

[Amit Agarwal @amtagrwl]

@IndianTechGuide @grok explain in details with specifics

[Amit Agarwal @amtagrwl]

@Culture_Crit @grok explain and give context

[Amit Agarwal @amtagrwl]

@grok @RT_com Please share a source for this

[Amit Agarwal @amtagrwl]

@agentic_ai Opened

[Amit Agarwal @amtagrwl]

@MrDaksh_G @deedydas For real

[Aaron Levie @levie]

Everyone knows Moore’s law, the idea that CPU performance doubles roughly every two years. This has enabled essentially every innovation we’ve had with computers since the 60’s and 70’s. But remarkably, driven by GPU performance and AI model breakthroughs, we’ve seen a very different law emerge with respect to the rate at which AI model performance improves — in many cases improving by orders magnitude in the past decade. This introduces massive implications in world of AI Agents.

For the past five plus decades, in any domain that computers touch, we’ve generally seen massive efficiency gains that bring more tech to more people. For instance, scaling of servers in the cloud with AWS, accepting payments with Stripe, or powering communications with Twilio — in all of these cases something that once was extremely expensive and complex is now affordable and ubiquitous. And that affordability (or value delivered) generally only gets better as each year goes on. Yet the same has not been possible for essentially any non-digital services.

Historically, anytime you want to solve a non-computerized business problems (i.e. most knowledge work), we have seen the cost to solve a problem — say from healthcare services to legal work — tend only go up over time. Due to a mix of inflation, specialization in certain fields, regulation, decreased competition in markets, or other added costs, we’ve seen the price of most services generally monotonically increase over time. 

Of course, much of this is perfectly fine, natural, and good. But this also puts a burden on driving economic activity in general, especially when creating new ideas from scratch, or starting and running a new business. Yes, I can get going with the cloud inexpensively, but that doesn’t matter if all other areas of my business are still hard to scale. Of course this is fine for a hyper growth idea in a mature market with access to the right resources, but that’s a small percentage of the world.

Yet, in a world of AI Agents, anyone — from a small business or large enterprise — has access to automated work that can let them address problems with variable capacity. When you decrease the cost of entry for getting knowledge work done, you dramatically increase the use cases for that work. That new sales program can be tested instantly instead of waiting weeks or months or never happening at all; you can test a broader surface area of your product for bugs or security issues; legal work and reviews becomes affordable on the seemingly insignificant transactions that otherwise produce risk; or you can launch a new marketing campaign in markets you didn’t otherwise serve.

AI lowers the barrier to doing all of these activities and more (and as I’ve shared before, ironically by doing so you actually will generate *more* job growth due to the productivity generated). 

Even in a world of AI costs remaining stable this is a super compelling proposition, but in a world of constantly improving technology the implications are vast. 

Just as Moore’s steadily lowered the cost of software, increased its sophistication, and made it more ubiquitous, we can imagine a world where knowledge work regularly gets more affordable, sophisticated, and ubiquitous at a constant rate. This means AI Agents presents the ability for any unit of work to continuously drop overtime, enabling us to solve greater and greater problems for a wider set of people and businesses. Of course, a large portion of these efficiency gains will quickly get “eaten up” by solving increasingly more sophisticated problems, but those problems will be solved at the cost of a previously simpler problem. 

The implications of this are enormously interesting, and it’s going to create incredible opportunities all around.