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I recently left Stripe to co-found a new VC-funded startup—tada! I’ll share more soon, but right now I want to talk about the future of my beloved craft: computer programming. Because I know exactly who will thrive in this brave new world of generative coding. The latest trend for tech CEOs—of companies both large[0] and small[1]—is to loudly pronounce that they don’t expect to be hiring programmers for much longer. Is this an accurate projection of the future, or is it blustery bandwagon PR? These are the same folks who trotted out the Jevons paradox[2] when DeepSeek sent the NASDAQ into a nosedive. In a market like software—where demand can surge when costs drop—we shouldn’t expect efficiency gains to kill off coding jobs. That said, everything is changing—fast. I’m currently the ~only programmer at my new startup, but over the past few days, I’ve “written” ~40,000 lines of code…with 99% of it generated by AI. Does that mean I’ll never hire another programmer? Of course not. (In fact, if you’re a cracked engineer looking for your next gig, DM me ;) ) What I will loudly announce is that the economics of software are shifting—radically, inevitably, and permanently. LLMs can turn a 0x engineer into a 0.5x, and a 10x engineer into a 100x—or even 1,000x. It’s akin to jumping from assembly to high-level languages, but on a far larger scale: not only are existing programmers supercharged, but everyone with natural language skills gets a baseline ability to code. What does that mean? Expect highly personalized software at the 0.5x end—people building quick, custom solutions for themselves—and watch 100-person tech companies compete with (and displace) today’s 100,000-person giants at the 1,000x end. The realm of possibility has just expanded in every direction. The programmers who’ll thrive in this new world are the same ones who always do: those who delight in creating something from nothing, armed with just a keyboard. For them, the future is brighter than ever. Yes, this AI era could unseat big incumbents. But no, it won’t leave humans sidelined. Instead, we get to build a new age of prosperity—together. [0] https://lnkd.in/gEfrgJHk [1] https://lnkd.in/gbTzPatA [2] https://lnkd.in/gQ2KDDhi

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Evergreen advice to those in SF: don't let the rat race kill you. From time to time, a friend of mine moves back to SF for their startup or fund. They put their head down for 2-3 years, work very hard to invest in their future, neglecting their friends and family and mental health. They pick up their head from the sand, realize that in a power-law, non-Normal Distribution sector (venture-backed startups) they introduced much more risk to their career and personal life than they thought. To be clear, I have been this person before. I don't regret it, made lasting connections and success, but I learned a lot the hard way. The better approach is to have a 7-10 year horizon on your entrepreneurial career. Over that time-scale, if you stay in the game, you will be successful. Your fund would have returned, your startup would have had some sort of exit. Otherwise, you are very very likely to crash and burn, on the professional and personal front. Don't take my word for it - just find anyone that's been in this game longer than five years and they will relate these same stories of personal ruin. This does not mean you shouldn't pursue your dreams in SF / SV and the startup ecosystem, it just means you need to be a bit more shrewd, understand your are in a non-Normal Distribution space. There is no 50th percentile outcome like if you chose a traditional profession. Power-law dynamics. It requires thinking a bit more long-term, prioritizing yourself and family along with your business. If you don't, the meat-grinder will get you. SF/SV culture eats everything for breakfast, including you.

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Today we're announcing that Eventual has raised $30M in Seed and Series A funding from CRV and Felicis as well as Y Combinator, M12, Microsoft's Venture Fund and Citi and others! Jay Chia and I founded the company 3 years ago in my basement after hitting the same wall every AI team faces: trying to process images, video, and documents at scale with tools designed for ad clicks and bank transactions. Since then, companies like Amazon, Cloudkitchens, Essential AI, Together AI and other Fortune 25 companies have adopted Daft as their data engine. – Why? – Three years ago, Jay and I were debugging the same problem at DeepScale (Tesla Autopilot) and Lyft Level 5: brilliant AI engineers wasting months forcing Spark to handle video and LiDAR data instead of building breakthrough applications. It honestly feels surreal that what started as pure frustration has become the infrastructure processing petabytes daily at Amazon and powering mission-critical workloads across autonomous vehicles, AI labs, and enterprise data processing. Daft improved Amazon’s most critical data job efficiency by 24% and has saved them 40,000 years of compute time annually and Together AI replaced custom Ray/Polars pipelines with simple Daft queries for 100TB+ datasets while being 10x faster. The journey ahead feels even more surreal. Companies are building AI applications that don't just process existing images and video - they generate massive amounts of new multimodal data at machine speed. We're no longer limited by what humans can create, and we're building the engine that will power it all – Thank you – Jay Chia and I, along with our incredible team, are so grateful for all the support from our community. We'd like to thank our earliest supporters... Brad Flora from Y-Combinator and Shruti Gandhi from Array Ventures, Raymond Tonsing from Caffeinated Capital and Jeremy Fiance from the House Fund. Our seed investors... Brittany Walker from CRV and Timothy Chen from Essence VC. Our users... Patrick Ames from Amazon,  Maurice Weber from Together AI, Alexander Filipchik from CloudKitchens, Andrew Hojel from Essential and Tony Wang, currently at Anthropic.  And our newest investors... Astasia Myers from Felicis, M12, Microsoft's Venture Fund and Citi. -- What's next? -- We're launching early access to Eventual Cloud - the first production platform built from scratch for multimodal AI workloads. Sign up now. We also have open roles across Engineering, DevRel, Design and Product Marketing. Link in the comments below!

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It's been six months hosting founder events in the SF Bay Area, and it's been quite a journey. Here are some of notes from which might be useful for anyone building a community here. 01. The SF Bay is non-zero-sum place in a surprising way. It's cutthroat, but it's also efficient. Goals either align or they don't. But when they do, partnerships happen without any contracts and negotiations. Purely on vibes and trust. 02. The work goes in finding the fit. Complementary goals, not competing - which stakeholders want what you have (ideas, reach, something new) and what do they have that you need (space, brand, capital)? You figure this out through conversation and observation. Then there are infinite ways to split the pie. 03. But, you have to attend before you organize. I have been attending events for 2 years. I watched what worked and what failed. People simply copy formats like happy hours and panels and wonder why it flops. Context matters and you cannot design what you have not experienced. 04. Community compounds, but only if you do it consistently. We started small. 30 people on a virtual call. Today, we have 2,500+ subscribers on Luma. Growth is a side effect of doing something worth repeating. 05. Avoid Tech Weeks and any other large-scale events. We ran events during both SF and NY Tech Week with the same format we ran monthly and drop-off spiked 20-25%. Don't compete with OpenAI's DevDay, unless you are Anthropic. 06. Founders, investors, and other thought leaders are more than willing to contribute with their presence at events. It just has to make sense for them, and you need to be authentic in your approach. Like I said: Silicon Valley is a non-zero-sum place, and it's not like other places where people ask for something in return (well, mostly). 07. Followup with people after the event. Some of our best connections came from referrals from people who attended our events. It also helps you find partners or even ideas for your next one. The high-level view is that people are hungry for IRL connections. Remote work created a void for even a second place. If you can build that genuinely, there is no limit. The Anthropic Cafe in NY is a case-in-point. Community is a long game with short turns. These are all my personal experiences from just two years in the Bay, but don't hesitate to reach out if you're starting off on this journey.

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Too much AI noise out there? I’ve filtered it down to the 6 updates that actually moved the needle this week 👇 → Uber Co-founder Garrett Camp explores a new robotaxi venture as Uber expands AV partnerships with Waymo → Meta aggressively hires top OpenAI researchers to bolster its superintelligence efforts → Reddit, Inc. updates policies to restrict AI companies from scraping user content for training → OpenAI forced to remove "IO" branding after court grants restraining order to startup IYO → Anthropic wins partial victory in copyright lawsuit, court rules training ≠ infringement → Apple reportedly in early discussions to acquire Perplexity AI to strengthen generative AI stack 🧠 Want the full breakdown + my predictions? Check out the latest AI with Aish blog post here: https://lnkd.in/dMA4umrR PS: I do the reading, so you don’t have to. No jargon, no fluff, just sharp, relevant AI updates 💙 Co-author: Harini Anand + AI 🤖 〰️〰️〰️〰️ ♻️ Share this with your network to keep them in the loop 👩‍💻 Follow me (Aishwarya Srinivasan) for weekly AI insights, product breakdowns, and industry signals that matter

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TL;DR: We’re pivoting - from building an AI SRE to building Cursor for pharma and medtech companies. Here’s what we’ve learned. The past few months have been a roller coaster for Topaz T. and me, and I didn’t post as much as I’d like We started Vespper (YC F24) from a personal pain: as a developer, I was thrown into endless on-call shifts: “Customer X is experiencing issues, what’s going on?” When you’re on call for dozens of services, failure points are everywhere. As a result, I really appreciated teammates who jumped in with helpful insights: - “RabbitMQ is exploding” - “Add some-feature-id to ALLOWED_FEATURE_IDS” When GPT-4 arrived I wondered: what if a digital on-call teammate did that - scan gazillion logs and dashboards and surface the issue? That spark led me to meet Topaz, start a company, and join Y Combinator. YC was wild: hackathons in an Airbnb, bug-fixing, customer calls, and learning from people like Sam Altman, Tony Xu, Kyle Vogt, and Parker Conrad. We grew fast, but after demo day we felt that something is not working. The product wasn’t creating enough value. I've realized two things during that period. First, in B2B the product must deliver real, tangible value (save or make money). Second is momentum. I found a great post by Erez Druk called ‘Guess It Until You Make It’, and after speaking with Erez, I learned we should decide and not wait. So we made the hard call to pivot. Before Vespper I worked at Viz.ai and saw how much work regulatory, clinical, and quality teams do just to get to market - and stay there. After months talking with experts in pharma, medtech, and biotech, the picture became clear: - Preparing submissions (IND, BLA, NDA, 510(k), CSR, narratives) is painfully manual. Even “simple” drafts take weeks/months. - Timelines are extreme. INDs take months. NDAs/BLAs are 4–5x longer. For late-stage assets (e.g drugs), delay costs ~$1M/day (!). - External search is fragmented. FDA/EMA workflows are slow and scattered. We looked at the current tools and saw more gaps: - Legacy tools are rigid and outdated. Hard to adapt to real data and evolving workflows. - Current AI tools collapse on massive corpora needing retrieval, grounding, and traceability. A submission package often contains thousands of pages (~2 GB and more). That’s the opportunity we’re tackling. In the past weeks we’ve built an AI-powered workspace for regulatory and quality teams: - Generate first drafts of complex docs directly from data (IND, BLA, CSR, narratives, 510(k)). - Search across internal files and external databases (FDA 510(k), MAUDE, etc.). - Use a ChatGPT-grade copilot for anything regulatory. It’s early, but experts already report meaningful time and cost savings versus generic AI tools. We’re excited to share more soon. Next month we’ll be at a few conferences - details to come. Whether you’re in life sciences curious about our product, or just want to talk AI and startups, feel free to DM me! 😊

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The SF Chronicle just quoted my hot take on the AI bubble. Turns out my wildest prediction was spot-on (down to the exact number). I told them how I meet first-time founders (non researchers) with no revenue, no product, no team, and no deck and yet they are easily raising $5M+ on $25M+ caps for oversubscribed initial rounds. This was impossible 5 years ago. Back then, only proven founders with exits could raise large seed rounds. But now, engineers leaving OpenAI or hot AI startups get $100M+ for their first startup with zero proof of concept. I’ll be the first to claim: “Yes, it's a bubble”, but it may NOT matter because VCs are okay with having 1000 failures if one of them becomes a trillion-dollar company. A decade ago, one 10x exit could justify 10 failures. Then came the decacorns. One could justify 100 failures. Now, in the trillion-dollar era, one winner can justify 1000 failures. And, this is happening in real time. OpenAI is now preparing for an IPO targeting a $1 trillion valuation. They are doing $10B in annualized revenue but lost $12B just last quarter. This proves my point about why investors keep believing despite the exorbitant losses or sky–high valuations. In fact, Sam Altman himself warned that someone would lose a phenomenal amount of money. And the Bank of England and the IMF both warned recently of a market correction. So yes, it’s a bubble. But in the meantime, we’re all living in it and benefiting from the trillion-dollar potential of AI companies and the thousands of overpriced startups that it can support. And my trillion-dollar analogy is playing out right now with OpenAI's IPO plans. What do you think? Will OpenAI be that one massive winner that justifies the 1000 failures, or will this end like the dot-com crash?

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Observations in Silicon Valley Preseed rounds done in SF has a 2-4x markup versus deals done outside of SF Met 2 companies building the exact same product & with similar rev < $100k Company A: SF based, younger founders out of college with less experience in the space, TS at $25m post Company B: Midwest-based, older founders with connections in the industry, TS at $6m post Same product, same traction...that’s the SF premium

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Unpopular opinion: The RL hype is a red flag for technical depth. When I hear founders or VCs casually dropping "reinforcement learning" in pitches, it's usually code for "we don't actually understand our problem space." Here's the reality check: RL only works when you have: - Perfect simulation environments - Extremely fast iteration cycles - Clear reward functions - Tolerance for massive compute costs That's why it succeeds in games (perfect simulation) and search optimization (Massive compute capacity at Deepmind AlphaGo/Anthropic/OpenAI) but fails spectacularly in the messy, slow-feedback real world. The graveyard of "RL-powered" startups tells the story. They burned through $$$$ trying to apply algorithms to problems that needed simple heuristics or supervised learning. Most of the time, a well-tuned evolutionary algorithm will outperform your fancy RL setup while using <1/10th the compute and delivering results you can actually explain to stakeholders. The next time someone pitches you an RL solution, ask them: "Have you tried solving this with a basic optimization approach first?" Watch how quickly the conversation changes. I am surprised when people are surprised that Direct Preference Optimization (DPO) can exceed PPO-based RLHF [1] or Reflective Prompt Evolution Can Outperform Reinforcement Learning [2]. [1] https://lnkd.in/gCY3bcTQ [2] https://lnkd.in/gQRwyQyM

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SF techweek, Day 1/5 I thought “Designing for AI-native products” would be an engineering talk. Something about APIs or model tuning 😂 But it turned out to be about design. Real design. Designers at Amplitude, Webflow, and Flexxi Design shared how AI is changing the craft itself. William said we used to start from user problems and find solutions. Now we start from what AI can do, then work backward to find valuable problems. I’ve built a lot of prototypes. With tools like 21st.dev or open libraries like shadcn, even a non-designer like me can make something that looks decent. But shaping a good experience still needs human thinking. AI can suggest patterns, not empathy. Kyle talked about UX turning into convo. Buttons become sentences. Flows become context. The interface is handled by AI, but intent still belongs to people. And Allie said AI has raised expectations. Even in B2B, users now expect the polish of B2C. That really resonated. So yes, AI speeds things up. But it also makes great design rarer. Because design is no longer about screens. It’s about translating intent between humans and machines. AI learns fast. Humans still know why something feels right. What SF techweek events are you joining?

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Based on performance in interviewing.io interviews in 2024-2025, here are the top 10 companies where the best engineers work. 1. Hudson River Trading 2. Two Sigma 3. Square 4. Cruise 5. Twitter 6. Coinbase 7. Optum 8. Google Cloud 9. Tesla 10. Netflix (We've hosted ~200k technical interviews in our lifetime. A lot of the time, we know where our users work, and that let us identify which companies have the best-performing engineers.) This list draws on interviews in 2024-2025, and it looks wildly different than the last time we pulled it in 2022 (link in the comments), when Dropbox was at the top. Crazy how quickly things change. Caveat: This obviously assumes that people at those companies are doing mock interviews. If a company has very low attrition but has great engineers, it probably won't appear in this list.

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In SF - All the ‘idea people’ want technical cofounders. All the VCs who want to start something are scouting for them as well. You can literally start a matchmaking service here and build a unicorn! Why are technical cofounders so rare? - The cushy jobs at Nvidia, FAANG, OpenAI etc makes one risk averse. When you’ve 400k+ cash comp and millions in stock options, you literally have golden handcuffs. Seen so many technical folks who are afraid to quit. - Many have burnt hands in doing indie projects before and know how tough it is to get even $100 in revenue. - There are too many idea people who don’t have a plan. Funding? Anyone can raise. Having a viable idea and clear vision? V few have it. - If they’re determined enough, they’ll themselves start a startup without needing a non-technical person. It’s a rare combo to have determination + tech skills though. Zuck and Gates are exceptions. - Sometimes, the big tech and recruiters give technical folks an illusion that they’re rarest people on earth. This creates a sense of entitlement in some. They want ‘work life balance’, a team to lead and high comp to make up for ‘opportunity cost’ of leaving their job. That makes a lot of technical people unfit for being cofounders of an early stage startup. - Visa issues are a pain. Technical folks want to focus on their craft and aren’t keen to risk visa status for startups. - Many are genuinely happy in their roles. Many new age tech companies give them autonomy, cut BS and let them focus on their work. What else does a technical person need? Your best bet is to find a technical friend or a friend of a friend. Random events and meetups rarely result in such relationships. Portals like YC’s cofounder match are helpful too. Once you find them, you’ve to lock them in with a compelling vision. Why should they quit a job they love? When should they stop coding and worry about coding and 10 other things? If you’ve a clear value prop, you can get your own technical founder. Steve got Steve and we got iPhones. So put that work in, find your match and change the world :)

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