Emma Qian

One thing founders rarely price correctly is leverage. Capital is one form of leverage. Environment is another, and it only works once execution is already underway. At sclr labs, we’ve built a selective founder residency by embedding our venture-scaling work within Landmark’s premium infrastructure. The result is a fully serviced operating environment in W1 - without the commitment or cost of a private office - designed specifically for founders who are already building, shipping, and preparing for their next phase. Residency is offered on a subsidised basis to reflect our decision to invest infrastructure, operating context, and access into a small number of companies we believe are already in execution. As January takes shape, selected teams are joining across the coming weeks, with others starting through February and March. We’re deliberate about who the environment is right for because leverage only compounds when the fundamentals are already there.

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TechCrunch covered the trend. Let’s talk about the truth behind it. A few hard truths from actually being in the trenches of this “venture zombie” work: 1. The work isn’t diligence, it’s reconstruction: - Early-stage VCs optimize for potential. - Hold-forever investors have to optimize for reality: broken systems, misaligned teams, faulty pricing, shaky GTM, and unfocused products. 2. You have to rewire founder psychology, not just cap tables: - Most “zombies” aren’t dying from lack of opportunity, they’re dying from the residue of venture conditioning: chase TAM, ignore margins, hire ahead of PMF, optimize for the next round. - Evergreen acquisition requires un-teaching bad habits and re-anchoring operators to fundamentals: cashflow, unit economics, customer retention, and repeatability. 3. The arbitrage isn’t cheap price, it’s time preference: - VCs need velocity and outliers. - Evergreen buyers need durability and cash yield - That mismatched time horizon is where the real value surfaces. You’re not betting on a future markup, you’re betting on transforming a company into a compounding asset. 4. Most people underestimate the operational drag: - Turning these companies around means stepping into the weeds: rebuilding finance, revamping GTM, fixing data infrastructure, restructuring teams, restarting product discipline. The “ton of work” is literal. 5. The winners will be operator-centric, not capital-centric: - The capital structure matters. But the real moat is the operator bench behind it. Funds that treat these acquisitions like PE-lite won’t survive. The ones that bring real hands-on turnaround muscle will. This shift isn’t about buying zombies, it’s about accurately assessing how much value gets trapped in early-stage companies that were built for a venture world that no longer exists. https://lnkd.in/gpSjtUaJ

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It feels almost poetic: the year that began with market-wide panic after DeepSeek R1’s surprise January drop is ending with the equally disruptive December launch of DeepSeek V3.2. In January, R1 cracked open the idea that aggressively scaled RL - not just larger and larger pre-training runs - can push a model into frontier-level cognitive behavior at a radically lower cost. Now, months later, V3.2 bookends the year with an even louder message: open-source is no longer trailing by quarters, it’s operating on a near-synchronous innovation clock. And in some benchmarks, it’s outright leading. We now have a publicly available model with gold-medal performance across IMO 2025, CMO 2025, IOI 2025, and ICPC-level tasks. No Western lab has open-sourced anything in that tier. It’s early. Independent benchmarking will come, along with the usual debates about framing, cherry-picking, and reproducibility. But you don’t need perfect clarity to see the shape of things. DeepSeek’s story has always been about discipline. While the frontier race spirals into billion-dollar training runs and million-token contexts, the team has stayed focused on a narrower, almost stubborn question: how far can you push intelligence per dollar. This model delivers frontier grade performance at a fraction of the cost (30x cheaper than Gemini 3 Pro, 50-75% cheaper than prior Deepseek models). Defending against a cost advantage is easy if you can point to a performance gap. But if a competitor matches your performance and undercuts your price, the defense collapses. That’s the corner V3.2 pushes frontier labs toward. Most of the world - nations, small enterprises, scrappy startups - will never train trillion-parameter models. And crucially, they don’t need to. They need models that are: - cheap to run - fine-tunable on commodity hardware - good enough to support agents, search augmentation, and code workflows - predictable on inference cost V3.2 sits precisely at that intersection: high-enough capability, low-enough cost. This is why a growing number of Silicon Valley startups are building on Chinese open-weight models. The logic is straightforward: they can download the weights, fine-tune locally, deploy on smaller hardware, avoid vendor lock-in and keep the price of inference predictable. For a startup with limited runway, this matters more than a marginal accuracy edge. DeepSeek’s trajectory transforms “Chinese open-source” from a curiosity into a default path for cost-sensitive builders. The innovation frontier is being pulled sideways, not upward. Today: ▪️ U.S. frontier labs chase maximal capability - climbing vertically up the y-axis. ▪️ Chinese labs chase maximal cost-performance - scaling horizontally across the x-axis. The model with the highest peak will win prestige. But the model with the widest base will win global adoption. DeepSeek V3.2 shows that efficiency is not a consolation prize, it is a competitive moat.

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Hello internet, especially folks operating & working with agencies. I have some bad news for ya. Well, technically, it's not me, I'm just the messenger of Y Combinator... The future of agencies is AI-Ready. So YC announced their request for startups for this spring. One of the cats? AI-Native Agencies. Agencies are now software companies. Operating on software margins. TLDR Old agency model: Humans do production (time & consistency) AND taste/judgment (expertise) New agency model:  AI does production (consistent, fast, scalable). Humans do taste/judgment. Make production a software problem? Get software margins.  Not too complicated. We broke this down here: https://lnkd.in/dgMiWT2c HELLO AGENCY OWNERS! Are you operating an AI-ready agency? DM me. We will interview you. Free ad space for your services. Give me all you've got on how you work with AI.

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YC like most VCs out there lost it. Instead of financing businesses that would add meaningful value to the world, it's all about financial metrics and agentic hype. Gems keep coming from Kyle Harrison's blog. As I wrote about here and on our Build in Public blog: it's mind boggling to me how markets have become so irrational and short term in their thinking at a time when the pace of innovation and change, and the prospects of returns over five, 10 and 20 years, has got greater than less... It's all about the rush to unicorn, decacorn, centicorn... Whatever. All about which big SF firm backs you... All about finance. Financial metrics. In that sense it's worse than banks before the GFC - all about partying and making money with "casino economics". But I believe, eventually, this will all come crashing down. I believe there will be rewards for those willing to ignore this cacophony of AI hype and lean ARR obsessions and stay on the road less traveled by. 🛣️

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Are any founders AI/agent maxxing their ops stack so that they can prevent being frame mogged by YC founders working on similar things??? If so comment below. Looking to deploy a lot of capital into any AI infra projects especially that dominate distribu channels. Clavicular is an LP in this fund.

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Here's a problem we see all the time in early studios: they copy-paste a VC fund's operating cadence and wonder why nothing feels right. Weekly deal flow reviews. Portfolio monitoring calls. Valuation-focused LP reports. All borrowed wholesale from a fund playbook. But a VC fund's primary activity is deal selection -- source, evaluate, check, board, wait. A studio's primary activity is venture creation -- generate, validate, recruit, build, launch. Those are fundamentally different jobs. When you run a studio like a fund, things break in specific ways: Your meetings are wrong. You're reviewing deal flow instead of venture build progress -- milestones, blockers, resource allocation. The work is building, not sourcing. Your team is wrong. Studios need builders at every level -- operators, designers, engineers. Not just investment professionals. You can't build companies with a team designed to pick them. 📉 Your metrics are wrong. Entry valuations and markups don't tell you anything useful. Cost-to-build-a-venture, time-to-market, and venture survival at 18 months -- those are the numbers that actually measure whether the studio is working. Your LP reporting is wrong. Report operational progress -- what's building, what's launching, what's been killed and why. Not just portfolio movements. Here's the lesson -- the studios that struggle most are the ones that adopted VC templates without modifying them for fundamentally different work. The portfolio companies don't exist until the studio creates them. Not a fund that happens to build things. A builder that happens to have a fund. The operating rhythm should reflect which one is primary. 🎯

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When YC accepted us, we felt like we won a lottery. But on the very first day, YC humbled us. We walked into YC with a strategy. A deck, a model, a set of assumptions. Within minutes none of it mattered. The only questions were: “Who is paying you?” and “How fast can you get more of them?” It was not advice, but correction. Everything we brought into that room was theory without cash behind it. I thought we had a “plan.” What we actually had was a story that would not survive contact with a market. That moment rewired how I think. Most founders do not lack strategy. They lack customers. They build reasoning instead of traction. They defend diagrams instead of invoices. YC forced us to do the opposite. Get real paying customers. Get proof in dollars, then speak. That shift killed a year of imagined work in a single interaction. After that, we stopped debating roadmap and started chasing revenue. Execution is real only when money moves.

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Lots of viral posts lately about VC behaviour to YC startups! YC has an internal tool called Bookface that rates every investor who's ever invested in a YC company. Every time you invest, the founders leave you a review. Five-star rating just like Airbnb. Easy to work with, super helpful" or "Nightmare investor, would not recommend." When you talk to a new YC startup, they (or at least, most) check your reviews before meeting. For me and Lobster Capital, this creates a forcing function… We have to be (genuinely) helpful, respond quickly, and make the fundraising process easy. Bad behavior gets you blacklisted not just from one company but from the entire ecosystem. Most investors don't know this system exists until they've already tanked their reputation with a few companies. Then they wonder why they can't get into the good deals anymore. Your reputation in YC compounds over time. Invest in 100+ companies, add real value, get good reviews. Suddenly founders from the current batch are reaching out to you instead of the other way around. For founders the best part is this creates accountability…. Too often in venture, investors can treat founders poorly with no consequences. At YC, bad behavior has a permanent record.

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