David Reche

Only ~3% of startups get into YC. Even fewer make it onto their Top 100 AI list. A while ago, StackAI did. 18 months later: - 100K+ AI agents deployed - enterprises in finance, healthcare, and defense - a team of 30+ from MIT, Meta, NASA, BCG How did we get there? By: - talking to users daily = fast feedback loops (early signals make the long-term vision more convincing) - building ugly (seriously, don't worry much) things fast - hiring slow, shipping fast, the right team multiplies velocity - staying uncomfortable - the moment it feels stable, you’re stagnating If you’re trying to get into YC, here's my 2 cents: - show real traction (even if it’s small), not hype - show technical depth, not noise - build a product that solves real pain, fast, scalable, and grounded P.S. StackAI is just getting started. We’re hiring, shipping weekly, and working with some of the most ambitious teams deploying AI into real operations. If you’re building something, or even just thinking about applying to YC, do it. You don’t need it all figured out. Just enough momentum, a real problem, and the courage to hit submit. Good luck! 🍀

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Sam Awrabi just raised $10M as a first time solo GP to invest in AI. He's just 31 years old 🔥 Here’s the playbook Sam Awrabi used for Banyan Ventures: 1. Pick a sharp position — only AI-native companies, from chips to SaaS. 2. Show traction early — 14 startups backed pre-fund with $490M+ in combined revenue. 3. Brand the method — Escape Velocity Investing, a 5-step filter to spot winners. 4. Keep the model clear — $200–425K checks, 36+ startups, up to 10% ownership. 5. Leverage founder edge — AI GTM since 2018, scaled teams, raised $70M+. I got access to Sam's pitch deck 🔥 Comment deck below to get the link!

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With OpenAI's App SDK launch, everyone’s asking: “Will employees now demand ChatGPT-style interfaces for all internal apps?” This focus on the UI, however, misses the true potential. The real opportunity isn't just to change how our apps look, but to fundamentally change how they work. This requires us to drive a much deeper architectural evolution—from today's API-first model to a future that is agent-first. In an agent-first model, we would move beyond simply connecting systems with APIs. The architecture would break down large applications into a team of specialized, smart agents. In fact, apps will natively become agent-first — meaning they are no longer just services waiting to be called; they expose digital personas that can handle complex jobs, like a 'procurement agent' that manages suppliers or a 'finance agent' that helps close the books. This agent-first world would be coordinated by a new layer of intelligence: Orchestrators. These are not monolithic, central brains. Instead, they form a distributed network designed to understand context at every level. There would be orchestrators that understand your individual role, others that grasp your team's objectives, and those that hold the entire organization's goals and strategy. More importantly, they would all be aware of the company’s "people graph"—who works with whom—and its "process graphs"—how work actually gets done. For this network of orchestrators to coordinate effectively, they need advanced ways to communicate. They must be able to negotiate complex tasks, delegate work with clear accountability, and share deep contextual understanding—not just data, but the why behind a request. Existing protocols like A2A are a good starting point for basic agent tasks, but they are not sufficient for this level of strategic coordination. A2A is primarily designed for peer-to-peer task execution, not for the hierarchical negotiation and rich context-passing required between orchestrators. This points to a clear gap: we need to define orchestrator-specific capabilities. This could mean extending A2A to support these needs or developing a dedicated Orchestrator-to-Orchestrator (O2O) protocol that operates at a higher level of collaboration. Communication protocols, however, would only be half the battle. We would also need the ability to teach these orchestrators. They must be able to learn specific business rules, workflows, and priorities. The system must be designed for us to coach them and provide feedback, so they get smarter and stay aligned with how we work. The final goal, then, isn't a new UI. It is to build a smarter, more adaptive organization. The future of enterprise technology depends on creating this network of intelligent agents, guided by a layered system of orchestrators that we can teach and trust.

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This company might be the fastest-growing enterprise AI company in the world. In January, they were doing $1B in ARR. Now? Over $4B... in just six months. And they're not done. The Financial Times reported that Anthropic is raising a new round at a $150B+ valuation - more than double their last round. Even more surprising? They're considering sovereign wealth funds from the Middle East — a reversal from their earlier stance of rejecting such money on ethical grounds. CEO Dario Amodei reportedly told employees: “No bad person should ever benefit from our success” …but admitted that’s a hard principle to scale a company on. Why does this matter? Because in the AGI race, principles, talent, and capital are all colliding - in real-time. Anthropic has: - $8B+ committed from Amazon - A strategic partnership with Google - 80%+ of revenue from business customers - Claude, which is gaining serious ground in coding and RAG workflows But it’s still an uphill battle. OpenAI leads on consumer usage with 500M+ weekly ChatGPT users. Meta is handing out $100M researcher offers like candy. xAI is shipping fast with Musk’s distribution firehose. And yet - if Anthropic pulls this off, it won’t just be a valuation story. It’ll be a case study in how to build a $100B+ AI company without playing by Silicon Valley’s old rules. This is the quiet giant that might end up rewriting the map.

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Breaking: Sam Altman is no longer CEO of ChatGPT. Sam is still CEO of OpenAI overall. But Fidji Simo (ex-Instacart CEO) has stepped in as CEO of Applications now running ChatGPT, product, and operations. he is moving to mainly to long-term research, compute infrastructure, and capital planning. Even the best founders eventually face a choice: keep running operations or focus on growth work on the company > work in the company Structure isn’t weakness, it’s power. he's handing off his day-to-day, so he can free himself to work on stuff that no one else really can

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So many startups are building AI agents. But if you want to raise funds or get acquired: Companies like Amazon, Google, and Microsoft want: INTEGRATION They're all expanding their AI agent stacks. But instead of actual agents, they’re investing in: ✅ Agent interoperability ✅ Agent orchestration ✅ Developer toolkits These are the control layers. They decide how agents get built, routed, and monetized. That’s where strategic value compounds. Build something that: → Orchestrates the agents → Enable them to talk to each other → Embeds them inside enterprise systems already in place That’s where acquirers pay strategic premiums: Technology that scales and integrates thousands of custom agents. 𝐖𝐡𝐨 𝐢𝐬 i5invest: We are a corporate development firm with access to 150K+ top decision-makers in Strategy, Business Development, and M&A. We provide innovative tech founders with insights, expertise, and access to our network to take their companies to the next level. #growth #tech #strategy #startups #artificialintelligence Image Credit to CB Insights

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💸 Everyone’s talking about outcome-based pricing in AI software right now. It sounds great on paper: instead of charging per seat or per token, you only get paid when you deliver results. It’s clean. It aligns incentives. It solves a lot of problems with SaaS bloat and unpredictable AI usage bills. ⚠️ But that simplicity hides a deeper complexity: In a sense, usage-based pricing is already a form of outcome-based pricing; just one where the model decides what the outcome is. You pay per token, per function, per interaction. However the "outcome" is inferred, often implicitly. Meanwhile, human-defined outcomes, like "issue resolved" or "churn prevented", are explicit, interpretable, and contract-worthy. The trouble comes when those two diverge. If the model believes it completed the task, but the human disagrees? Or if a user drops off mid-flow and the vendor counts it as a resolution? That dissonance creates billing disputes, mistrust, and risk. In the latest edition of Tokenomics I am trying to dive deeper into that dissonance. To help think through that, here’s a simple 2x2 framework I’ve been using: What are you charging for? 🛠️ Work Completed – Task finished, regardless of impact 📈 Upside Captured – Business result delivered Who’s your buyer? 💼 Cost Center – Efficiency-driven, budget-sensitive 📊 Revenue Driver – Growth-oriented, outcome-focused Putting it together you get four pricing archetypes 🔹 Ops Backbone (Cost Center × Work Completed) → Think: support automation, per-resolution → ✅ Clear ROI, but limited alignment 🔹 Skeptic’s Gambit (Cost Center × Upside Captured) → Think: fraud prevention, chargeback recovery → 🧾 High bar for proof, slow cycles 🔹 Volume Multiplier (Revenue Driver × Work Completed) → Think: meetings booked, emails sent → 📬 Easy to scale, hard to tie to success 🔹 Skin-in-the-Game (Revenue Driver × Upside Captured) → Think: upsell revenue, new pipeline closed → 🧠 Deepest alignment, toughest attributionThe Outcome Pricing Matrix Outcome based pricing isn't just about measuring success, but also about agreeing on who defines it and when. Most AI startups today are landing in hybrid pricing: platform fee + usage. But if you’re serious about moving toward outcomes, you need a clear POV on where you live in this matrix, and what failure modes you’re willing to accept.

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My AI predictions (2025-2035) - Agents (agentic software will replace saas & humans at most tasks) - APIs (agents need APIs to interact with each other & legacy systems) - Most humans will act as tools for agents - AI UX will shift to Canvas + Chat I'm building these agents: 1. seobotai.com AI Agent for SEO, that can: - research topics & keywords - generate articles - find relevant news and knowledge on internet - build up backlinks - come up with pSEO ideas & implement them -...basically act as your entire SEO department 2. listingbott.com can do the boring job - finds all relevant places on the web (directories, forums, launchpads, business sites...) - lists you everywhere, filling out all those boring forms - does it in a way that's safe with zero risks (slowly over time) 3. unicornplatform.com - a website builder with an agent store - one click to install any agent from the store - the end goal is that the "super agent" will use the other agents for you and your job is to act as a CEO, guiding it 4. tinyadz.com runs ads for me (on it's own publisher network) It creates the campaigns, targets it at the right audience, sets the budget, does A/B testing...Literally zero work from my side 5. wrapifai.com can build small apps (lead magnets or mini tools to drive seo traffic) 6. marsx.dev is my AI Coding tool that me and my comakers used to build all these agents 7. indexrusher.com - gets you indexed by bing(helps with LLM search optimization, so that chatgpt knows you) - helps with google indexing(up to 20 pages a day) - saves you time, often drives SEO traffic up - offers one time fee (pay once, use for ever) 8. There are many more agents I run internally and I'll launch them at some point. My end goal is to automate literally everything. The next launch is SocialBot (the first ai agent for social media that doesnt create ai slop) More about my startups: johnrush.me

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𝗢𝗽𝗲𝗻𝗔𝗜’s Product Leader just revealed the playbook for 𝗔𝗜 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 and why it decides who wins and who fails 🤯 Chegg lost 90 percent of its value because it waited too long. Jasper raised at a $1.2B valuation but collapsed under costs and commoditization. Duolingo lost trust and users by forcing AI in without strategy. AI does not forgive bad decisions. Features get copied in weeks. Costs spiral faster than revenue. The market punishes hesitation brutally. That is why Miqdad Jaffer, 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗟𝗲𝗮𝗱 at 𝗢𝗽𝗲𝗻𝗔𝗜, created a step by step guide to AI product strategy https://lnkd.in/dn2SCSUb Inside you will find: ▪️ The brutal economics of inference costs and why SaaS style pricing breaks in AI ▪️ The only three moats that matter in AI which are data, distribution, and trust ▪️ The 4D Framework that stress tests your direction, differentiation, design, and deployment ▪️ Pricing and positioning models that protect you from death by API wrapper Most AI startups fail not because of technology, but because of their strategy. 🎁I am sharing the full guide and frameworks here: https://lnkd.in/dn2SCSUb

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This breakdown of how 12+ companies use credit-based pricing in SaaS is a practical guide for modern monetization. It highlights four patterns, from pure-credit models like Clay to credit add-ons like Monday.com, and shows how to balance flexibility, transparency, and value capture in pricing. Worth reading if you are mapping AI or SaaS pricing strategies: https://lnkd.in/dJvtVsj3

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Controversial take (at the risk of getting kicked out of future YC demo days): What if getting into Y Combinator actually made your outcome worse? I've invested in 6 YC companies since 2014. 5 out of the 6 were pre-YC or early in the program, one was post-demo day. This includes winners such as Deel and May Mobility. Here's what nobody tells you: Most of what accelerators provide, you can get for free: - Mentorship? You are going to find the best mentors through your network - Network? Warm intros through investors who actually take board seats - Demo day? Most deals come from direct outreach, not demo days - Validation? Having revenue is better validation than any batch The worst part? YC invests in 400+ companies per year. They don't have time to actually help you beyond the program. You're a lottery ticket in a portfolio. The 2-3 breakout companies subsidize the other 197. You're probably one of the 197. And worse. Because they only make money from the breakout companies they would rather optimize their outcomes by pushing everybody to $20m-$40m post money SAFEs to optimize for the winners... Even though it's the wrong valuation for you and can kill your company. Yes, getting over your skis on valuation can kill your company. And no, YC doesn't care because while your company is dead they make more money on the winners. Here's when accelerators make sense: - You're 22, first-time founder, need basics - You have zero network in Silicon Valley - You need the brand to unlock initial meetings Here's when they're a terrible deal: - You're a second-time founder (you don't need the basics) - You already have investor relationships - You're giving away 7% for what amounts to a 3-month crash course Here's my controversial recommendation: Skip the accelerator. Take that 7% and: - Hire an exceptional early employee - Keep more equity for the people actually building - Raise much more money for the same equity to fuel your business If you absolutely must do an accelerator, negotiate. In most incubators/accelerators the 7% isn't set in stone, especially if you are further along. Some accelerators take 5%, some do notes that convert later. Is 3 months of mentorship worth 7% of everything I'll build for the next decade? For most founders with any serious experience, the answer is no. But everyone's too afraid to say it. #YCombinator #Accelerators #StartupAdvice

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