Rishi Kulkarni

After HCLTech led Sarvam’s $300m round in June at a $1.5b valuation, Cognizant is reportedly in talks to join the next one, which would value Sarvam at $3.5 to 4 billion, more than double, with large Indian enterprises lined up to anchor it. For an industry trading near five-year lows on fears that AI agents will automate away coding, maintenance and back-office work, good to see them backing homegrown model builder is both a hedge and a bet on the future. via @EconomicTimes

We've reached a point where people who've never built a company have absolute certainty about how companies should be built. Meanwhile, founders who've spent years building one will openly admit how much they still don't know. The internet has created a strange world where those with the least experience often have the strongest opinions, while those with the most experience are expected to defend every word they say. It's remarkable how much easier entrepreneurship looks when the only thing you've ever built is an opinion about it.

every product i use should just be a headless mcp, that feeds into an agent, where i never have to think about anything underneath. except for our product, of course. in our product, customers love the ui and love us very much. we're special that way.

This is why I recommend younger founders to build profitable, bootstrapped business, make money, and then think of building a moonshot, venture-scale business. I did exactly this myself. Built my first venture, sold it, made good money and became financially free. Now I'm building a moonshot at @GreyLabsAI taking a big goal of disrupting the call center industry using AI. Worst case if it doesn't work, I won't find myself on the road. Best case if it works, it's going to be a great outcome for all.

Exa now indexes 3,000,000+ podcasts. Your agents can now search across millions of hours of transcripts, updated daily, and track what the world is talking about.

4 months ago I cloned Airtable for a client in 5 days because they refused to pay Airtable $500/mo months later, I’m still maintaining it every two weeks it’s another edge case, a UI tweak, or a "quick 10-minute request." They’ve easily spent 10x more in headaches than the $2,000 they would’ve paid Airtable just proving that SaaS isn't dead. Cloning isn't enough, if you want it to be same quality as original it becomes a full time job and at this point isn't it easier to just pay?

The exploits of the last few days are a good reminder of why we use SaaS. Our company pays for ~20 SaaS services. If we self-hosted them, we’d now be responsible for all of the security patches this week. Instead, our small monthly bill pays for their teams to handle everything.

Sadly canceling our linear subscription as we just don’t use it anymore Planning happens in a Slack canvas with very high level goals and constraints Features gets shipped in a few hours, or days max Roadmaps don’t get further than a month

Reading a history of the bond market recently, and the author noted that Britain defeated Napoleon mostly because their consol bond market left the British military much better funded than France’s weak post-revolutionary capital markets. One Alexander Hamilton studied the British bond market, and then consolidated American revolutionary debt into one tradable market, which gave birth to what became the New York stock exchange, Liberty bonds, and the world’s largest capital markets. An army might march on its stomach, but it’s borrowed money that pays for the food and ammunition, and the one who can borrow the most generally wins.

Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

Maturity arrives as soon as we can laugh, rather than fret, over our insufficiencies, or those of others. Growing up means throwing one's arms around the imperfection of almost everything.

Everyday I am reminded how much damage was done to generations of Indian youth because the smart people were told to be quiet and humble and let their work speak, while the idiots were given free rein to have a personality.

These days, I'm struck by how the dominant mood on both sides of the Atlantic toward the other is a kind of pity. Europe frets about the US's supposed slide into autocracy; the US about Europe's pusillanimity and immiseration.

You already gave the LLM the answer. So why is it writing it out from scratch? Introducing a new AI model architecture: Constrained Decoding Models (CDMs) LLMs were built to write. Most production workloads just need an answer: fast, consistent, and affordable. CDMs are purpose-built for 1 task each. They read your input once and skip the token-by-token generative loop that makes LLMs slow and expensive. Not just a decision model, but a family of 3 CDMs live today: • Classify: define your labels and get back a calibrated probability + reason for each. Used for routing, tagging, and intent detection. • Extract: describe the entities in natural language and get back well-formed JSON with confidence scores + citations. Used for web scraping, document processing, and PII redaction. • Summarize: pass in long text, images, or PDFs and get back a consistent summary under 1,000 tokens. Used for context compaction, metadata generation, and transcript digests. Built for production: • 1M tokens of multimodal (text + image) input, 31x larger than Jev • 77.2% on DecisionBench vs Jev's 72% • 200x faster, 467x cheaper than frontier LLM APIs • $0.05/M input tokens. Output is free (yes, even for summarize) Live on your favorite platforms today: @OpenRouter @vercel_dev @LiteLLM @LangChain @TrustedRouter @itsArthurAI @useRouterPlus Get 50M input tokens for free (for the first 50 hours) at link below 👀

Technical moats are mostly over at this point. If you’re building something that is meant to be durable, the only moats now are: • Proprietary data • Network effects • A physical component

there's a common belief in healthcare among providers that most people would rather trust a doctor than AI - but I think we're not far from a tipping point here Was interesting to see this Oliver Wyman survey of consumers and see how close we are to people trusting AI more than the clinician On top of that the number of times where the AI and clinician disagree is increasing, with people MORE often doing something other than what the clinician directly said This is heading for a collision course at some point

Assorted things I've been mulling over regarding agents in the economy: • Companies are in some sense the original superintelligence. It's often the case that they can use their amortized cognitive surplus to advance their interests against those of certain consumers: relative to the optimization capacity of companies, consumers can be inattentive, underinformed, irrational, etc. (Think, say, of some of the classic insurance upsells at the end of a booking process, which is typically priced substantially above what a regular policy would cost.) Herbert Simon introduced the idea of bounded rationality, and firms often end up benefiting from it. Thanks to personal agents, we will probably get somewhat more rational in how we engage with companies. • How exactly will outcomes change? Well, certain kinds of price discrimination will work less well (how do coupons work if every agent hunts fastidiously for them?), and certain kinds of customer cross-subsidy will change (today, people who forget to cancel subscriptions subsidize those who don't). Taken across the economy as a whole, I'm not sure what the incidence of all of this will be. • In corporate strategy, people have long debated the relative importance of distribution and product quality. Perhaps you've made something better, but does anyone know about it? Won't people just continue to buy the ACME Corp Mousetraps? Personal agents will, I think, act as a kind of structural subsidy for product quality. How would markets look different if individuals always spent at least 10 hours researching their purchases? On the margin, focusing on making a product better is, I think, going to become a more effective strategy for companies. This is good! • It true, a corollary is that information about product quality will become more important and impactful. Today, most people have very limited ability to aggregate and integrate such data, but agents will have insatiable thirst for such signals. (It's often said that data is becoming more valuable because it helps in the training of models. That's obviously true, but I think there's a secondary way in which it's more valuable simply as a complement to cheaper intelligence.) • A big question is whether agents will increase superstar effects (rational agents all settle on the same product) or increase dispersion (because everyone has slightly different tastes and preferences, which agents are good at eliciting, finally overcoming the artificial clustering we've been subjected to). • Many internet companies have monetized demand routing, which is in turn often predicated on short attention spans. "I'll click on the first thing I see." This has led to some amount of inefficiency, as companies try to propitiate capricious ranking algorithms and incur various placement fees. It's not all deadweight loss (willingness to pay is itself a kind of signal, which is valuable to buyers), but it's not great. How will the nature and value of this routing change in a world of personal agents? I don't know the answer, but I think that the effects might be pretty big. • Much of what I'm writing hinges on the assumption that personal agents will be on the consumer's side. I suspect that that will be the winning strategy, but whether it's true, and what it means to be true, involves a lot of other questions. Taken as a whole, the market is a massively parallelized, decentralized, and highly imperfect value attribution machine, and, taking stock of all of the effects, it seems to me that the quality point may well be the biggest: that agents will change the nature of the backpropagation such that the rewards for better products increase. Maybe this is too optimistic, but I suspect (and hope) that companies that make things that they're proud of will feel that the universe is a little more partisan in their favor.

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