Jason Tan

The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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So, it turns out there is actually no meaningful correlation between how quickly a generational startup gets to $100M in ARR and the magnitude of its eventual success. Decided to pull this data because every time you turn a corner in SF, there is another VC debating whether the "triple-triple-double-double-double" paradigm is dead because a handful of ripping AI startups are sucking the air out the room for everyone else. That would suggest that nuking to $100M overnight is the only way to win But if the past is any indicator, that’s probably… not true. There have always been companies that blow traditional growth benchmarks out of the water and ones that grow more steadily over time. (We’ve all seen the “Fastest to $100M” charts). But when you actually compare that speed to the size of eventual outcomes, there is really no correlation. In fact, if you look at the value of these companies at their respective peaks, there is actually a slightly inverse relationship between speed and success. So, a couple takeaways: (1) $100M is a milestone (albeit an important one), but it’s not the final destination. We are talking about truly generational companies. At that level of scale, the real north star looks more like ten billion than hundreds of millions, and in case you forgot, $100M is just 1% $10B. (2) Speed is not the only playbook. Time to $100M isn’t destiny, and some of the biggest outcomes took longer to get there while others around them grew faster. To be abundantly clear, getting to $100M that quickly is an extremely impressive feat and indicative of companies building incredible products. That should not be discounted, and we will 100% continue to look for these kinds of investments. Too far in the other extreme is also true (limping to 100M with decelerating growth is probably not… great). But none of that detracts from the fact that it is a long road to success in this industry. There are many ways up the mountain, and it takes time to build something enduring!

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YC has quietly built a parallel tech ecosystem. Not just a network - an entire operating system for startups. Think about it: A YC founder can build their entire stack using only YC companies. Infrastructure, analytics, payment processing, marketing tools - it's all there. At friend-and-family prices. When you launch a product to the YC network, you get something priceless: A group of technical founders who actually understand what you're building. They become your first users, your debuggers, your evangelists. This is why PostHog is now used by 65% of new YC companies. This is why Brex dominated fintech... and why each YC batch makes the next one stronger. A true compounding advantage.

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The smartest YC founders I know are NOT raising VC money. Here are FIVE unique things they're doing instead: 1) Firing their least technical co-founder. 2) Importing/exporting used Herman Miller chairs. 3) Selling their YC office hours on Craigslist. 4) Auctioning screenshots of their Stripe dashboard as art. 5) Using Central to eliminate nonsense HR/finance work and focusing on profitability - centralhq.com What else am I missing?

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This founder just sold his 1-person company for 9 figures ($80M upfront + earnout) in 6 months without splitting a dime. The untold story of Base44’s founder, Maor Shlomo and the journey to the first 1-person unicorn on the Lean AI Leaderboard. Yesterday, I got on a founder-to-founder call with him to hear the personal story behind this incredible exit, and how he managed to do this all by himself (he only hired his first employee last month). Before Base44, he had already built a traditional startup that raised $125M and employed 100 people. He knew how the old game worked: the fundraising circus and complex team management. So he decided to do it differently. While every AI startup raised large VC rounds, hired engineers and built MVPs for months, Maor built solo with AI as his entire team. But his secret insight was that AI should write as little boilerplate as possible and only focus on the unique business logic (that's how Base44 stood out). He used AI to write 90% of his frontend and 50% of his backend code. Then he did something that would give most CTOs nightmares. He pushed code to production every day without any code reviews, branches, or unit tests. While competitors spent weeks planning, he was shipping faster than they could debate database architecture. That’s how he quickly built something his well-funded competitors couldn't match: A truly batteries-included platform where complete non-technical users could build working applications. The results were insane: 250K+ users, $189K profit in May and $3.5M ARR in under 6 months. But it wasn’t all roses. He burned thousands on paid ads, which was a disaster. So he took a leap most founders wouldn’t dare. He started posting his failures on LinkedIn (using a tool for content he built in Base44). He shared the strategies that flopped and the embarrassing mistakes he made. His audience was hooked and the posts hit 100K+ views. He monetized the attention by creating a viral flywheel. Users got platform credits for posting about what they built with Base44, turning every customer into a marketing engine. It worked like magic. The Israeli tech community couldn’t stop talking about him. That’s when Wix approached him with an offer of $80M upfront + much larger earnout amount. --- This is the first company from the Lean AI Leaderboard to be acquired and I'm excited about what this means for the future. His advice for Lean AI founders: 1. Only build something that genuinely excites you 2. Spend 35% of your time optimizing AI tools and automation. The rest on execution 3. Find one distribution channel that works and go all in 4. Products get copied easily, so product thinking and differentiation become critical There’s a lot more to his story than I could fit here. If you want to know how he runs everything with AI, how he differentiated, how he automated his growth flywheel, share this post and subscribe to my Substack to get full access. 👇 Link in first comment

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Product-market fit genuinely feels different...and it'll show up in the data Demo to close rates are the easiest thing to look at for product-market fit right now. Vertical AI startups in our portfolio are seeing 50%+ demo to close rates, even with brand new account executives selling the product. It's happening in every market from AI takeoffs to AI CSR/Voice software to AI procurement solutions. When you see a demo to close rate over 50% chances are you have a jaw dropping customer experience and you should put your foot on the gas. If you're seeing something dramatically lower in mid-market or SMB ACV categories, it's worth revisiting your product and/or your pitch.

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A solo founder just sold his AI startup to Wix for $80 million.  It's a 6 months old startup! 🤯 This is so far the wildest acquisition of this year imo. Maor Shlomo, a 31-year-old Israeli founder, launched Base44 as 𝘢 𝘴𝘪𝘥𝘦 𝘱𝘳𝘰𝘫𝘦𝘤𝘵 after finishing reserve duty. He built an AI-powered app builder where anyone could create full-stack apps just by describing them in plain English — making software creation as easy as chatting with ChatGPT. And in just 6 months, Base44 hit: • 300,000+ users • $3.5M ARR • $189K monthly profit • Partnerships with eToro & Similarweb • Acquired by Wix for ~$80M And here’s the kicker: → Fully bootstrapped → $0 VC funding → A team you could count on 2 hands. How did a tiny, bootstrapped side project pull off such a dramatic sprint? There’s a buzz about the rise of “𝘀𝗼𝗹𝗼 𝘂𝗻𝗶𝗰𝗼𝗿𝗻𝘀” — one-person startups supercharged by AI. While Base44 didn’t reach unicorn status, its story is a compelling real-world example of how far a solo founder can go with AI prowess, the right product, and relentless execution. Maor didn’t build hype. He built something people 𝘯𝘦𝘦𝘥𝘦𝘥  And talked about it 𝘱𝘶𝘣𝘭𝘪𝘤𝘭𝘺. 👇 I broke down his full journey + behind-the-scene strategies + lessons for founders in my newsletter: 🔗 https://lnkd.in/g-QaXB42

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Here's how I think about recruiting at Clay in three analogies: 1. Recruiting is like a solar system 🪐 ↳ Like orbiting planets, Clay and candidates are both in constant motion. Recruiting is about timing and fit: sometimes alignment happens right away, and other times it takes years of circling back. For certain candidates, it may take multiple touch points over time before Clay and a candidate’s orbit come close enough to align. ↳ At the beginning, you start with a blank canvas, you try to fill in the colors as best as you can with interviews, references, etc. Then compare that picture with the spec for your role. ↳ I'm not making value judgments if someone is "good" or "bad". I'm just trying to understand their story, peek into their soul — figure out who they are! — to determine if Clay's role is a good fit for them at this moment. For example, someone may not be a fit if they need more support than Clay can give them at this stage of growth. 2: Recruit like you're fishing 🎣 ↳ Know when to chase with energy & urgency. Know when to ease up and let them come to you. ↳ It's a dance where you always need to sell and evaluate the fit at the same time throughout. The best people make that tension feel natural! ↳ People want to feel wanted. Bring high energy and enthusiasm even if you're hiring for a great role. 3: Interview like this emoji → 👯‍♀️ ↳ Interviewing shouldn't be combative, even though sometimes it feels that way. Ideally you and the company are working together to get the right information to determine if there is a fit. ↳ You get information at every part of the process. For example, in an offer negotiation, you learn more about the candidate by how they act and engage if there's conflict. Candidates also learn how companies act too (e.g. Clay does not make exploding offers). I hope this is helpful! Curious what people think and if there are any questions, feedback or ideas!

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YC has done a good job supporting ambitious deeptech projects. Seeds sowed over many years are starting to sprout in meaningful ways. Biggest support to founders (esp those building in the physical world) isn’t money - it’s the right advice, network, and peer support.

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I recently spoke with an early-stage AI app founder who was desperate to hire sales reps because he dreaded founder-led sales. This is one of the most common failure modes I see with technical founders—and it significantly impedes the path to product-market fit. Here's how to think about the right order of operations in early sales motions: Phase 1: Prototype & Validation In the earliest stage, the feedback loop between customer conversations and product roadmap must be extraordinarily tight—making founder-led sales absolutely non-negotiable. This phase is critical because you're identifying your true ideal customer profile (ICP) and learning how to effectively communicate your product story and address common objections. As you accumulate hundreds of demo repetitions (while refining your product based on feedback), you gradually assemble a winning process. Phase 2: Founder-led Sales Scale-Up Your mission here is to create the sales playbook that will guide future reps. You need sufficient pattern recognition to understand which messages resonate with which personas. I recall meeting Desmond Lim, CEO of Workstream, several years ago (not an Emergence portfolio company, but I deeply admire what they've built). He showed me the remarkable 60-page playbook he crafted documenting their entire sales process—before hiring a single AE. Every nuance. Every objection. Everything a new rep would need to succeed. While perhaps extreme, this perfectly illustrates the principle: scaling go-to-market requires mastering your ideal sales motion before delegating it. Phase 3: Hiring Initial Sales Reps Most founders default to sequential hiring—start with one rep, evaluate results, then proceed. However, we recommend hiring 2-3 sales reps with diverse backgrounds simultaneously, enabling you to effectively A/B test different profiles. Regardless of approach, ensure these early hires are "renaissance reps" with rapid iteration capabilities rather than purely "coin-operated" sellers. Mark Leslie has a great foundational article on the Sales Learning Curve provides excellent guidance. I'll link it below. So embrace the early sales work, even when it feels uncomfortable. It's fundamental to building a foundation for lasting success.

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