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Great deals are done silently. 🤫 Stealth Bids: make an offer on an NFT you love, and only the owner sees it. No public bidding war to get outbid in. Off-Market Asks: set your asking price privately. Only you see it, and when a bid meets or beats your price, it sells automatically. Explore both on GONDI.

What Menlo has done over the last 24 months is exceptional. Yes, they did Anthropic’s round at $4BN that will make them $50BN+ as a firm. NICE! But they then took that and went f****** hard. They do Lovable, Higgsfield, Legora. They compounded the brand win of Anthropic to cement their position from respectfully a slightly ageing firm to being a dominant venture leader at the forefront with Thrive, a16z, Benchmark etc. It is incredible to see and I sat down with @venkyganesan to shoot the s*** on this. 1. Every Seed Investment Is an Option Bet Seed-stage deals function as financial options to discover true outlier potential before committing major capital. VCs must construct a broad enough portfolio to secure sufficient at-bats, only sizing up their position when quantifiable, revenue-based metrics prove an outlier has emerged. 2. What Is the First Sign of the AI Wave Cracking? Market cycles rarely collapse due to equity write-downs. They fracture when levered debt defaults occur. When market participants take on heavy leverage during an AI boom, precise timing becomes as critical as being directionally correct, making debt obligations the primary failure vector when cycles turn. 3. Why Every VC Must Now Prioritize IRR Every startup in the current landscape writes a structural tax to the Mag 7 and hyperscalers for compute and foundation models. Because LPs can buy index exposure to those same public tech giants without fees or carry, private VCs must generate outsized IRR performance, beating public markets by at least 1,000 basis points, to justify private capital allocation. 4. Why LPs Wanting Smaller Funds and Slower Deployment Is BS LPs cannot demand both smaller fund sizes and multi-year deployment paces in the middle of a generational platform shift. Because AI startups face immense capital requirements to scale compute, VC firms that refuse to deploy capital rapidly will watch competitors step in and capture the category winners. 5. The Theory of Reflexivity and How It's Causing Markups Reflexivity creates self-reinforcing loops where fast revenue growth triggers rapid valuation markups, attracting elite human and financial capital that further accelerates growth. However, when copycat investors chase markups for momentum rather than underlying fundamentals, reflexive valuation bubbles inflate until market realities force an inevitable correction. 6. Sometimes You Pay Up Because You Have Unique Insight Premium entry prices are not always the result of irrational bidding. They often stem from an investor's ability to recognize a fundamentally larger total addressable market than the consensus. When a VC possesses unique conviction around an expanding market opportunity, paying above current rates reflects calculated foresight rather than price-taking behavior. 7. There's No Limit to What a Person Can Do as Long as They Don't Care Who Gets the Credit Personal ego remains one of the most expensive liabilities in venture capital and company building. True long-term success requires setting aside individual notoriety, focusing ruthlessly on generating LP returns, and empowering teammates and founders without worrying about who receives the public credit. (links in comments)

AI Drug Discovery Is Becoming a Bottleneck Trade I get excited when an industry starts going through a real regime change. AI drug discovery (“AIDD”) increasingly looks like one of those moments. Two things are happening: 1/ Upstream: the frontier AI labs are piling in. 2/ Downstream: The supply chain is clearly moving. Supply-chain checks suggest the upstream picks-and-shovels of discovery and early preclinical R&D are starting to feel the increase in experimental volume. DNA → protein → assays → sequencing → automation → preclinical testing Names across that stack include $TWST , @GenScript , $ILMN , $TXG , lab-automation vendors and CROs. $TWST expects triple-digit percentage growth in AI-enabled drug-discovery orders in FY26, and another year of triple-digit order growth in FY27. @GenScript's AIDD business doubled YoY in 1H26. Its current platform advertises industrial-scale validation of 4,000+ designs/day, with integrated sequence-to-data workflows. Our channel checks suggest the ramp is moving even faster: roughly 8,000 designs/day currently, with a path toward ~16,000/day by YE26. --- Why does AI drive more wet-lab demand? 1/ AI makes hypothesis generation almost free → way more shots on goal. The bottleneck is moving from expert-driven design to biological validation. 2/ AI models need continuous experimental feedback — and both good and bad data are useful. Traditionally, only the highest-conviction A+ candidates might get pushed into expensive validation. With AI, even the B/C candidates can be valuable because failed experiments generate training data. @GenScript has said its sequence-to-binding workflow can return data in 4–7 days, and that faster cycle times matter because AI models depend on continuous experimental feedback. @Anthropic is a clean example. @claudeai designed 1,320 protein binders. @adaptyvbio converted those digital sequences into DNA, expressed the proteins and tested binding. Only 354 actually bound. And the 966 failures are not wasted. They are useful negative labels: what does not express, what does not bind, what has poor affinity. Those results help train the next model iteration. 3/ Wet labs are no longer just making drugs. They are making training data. $TWST / @GenScript increasingly look like biological data foundries. $TWST explicitly talks about generating model-ready data from AI-designed sequences. In some workflows, the customer may care less about receiving the physical protein than about getting structured experimental results back into the model. Traditional drug discovery asks: “Does candidate X work?” AI drug discovery also asks: “What can this experiment teach the model?” --- TAM of AIDD If AI is simply a better R&D tool, the relevant spending pool is the $300–400B of annual global pharma R&D. If AI meaningfully increases the number of viable drug programs, the opportunity is larger because it expands downstream demand for DNA synthesis, protein production, assays, and preclinical work. Near term, we can also size demand from AI-company spending. If Anthropic reaches $80B of ARR in 2026 and spends just 1% on AIDD, that alone would imply ~$800M of annual investment. --- Trade setup This is a trade that could have long legs. It’s hard to really stop working until PhaseI/II results (2028+) It smells a lot like the bottleneck trade we just saw in semis: GPUs → HBM → networking → power/cooling. In biology, It basically follows the drug discovery process downstream: AI models → designs → DNA/protein → assays → preclinical capacity. After the upstream picks-and-shovels, animal testing could become the next bottleneck. Monkey prices are already near prior highs and CRO capacity is tight. AIDD pushing more candidates into preclinical development would only add demand. ?? But clinical trials are still the bottleneck? This is the biggest pushback I keep coming back to. No matter how fast discovery becomes, drugs still need to go through preclinical → Phase I → Phase II → Phase III → approval. You still need patients, time and capital. But that doesn’t mean the bottleneck trade won’t work. More viable candidates — especially with higher success rates — still means more demand throughout the development process. And who knows: clinical trials themselves may eventually be optimized by AI. ?? What breaks the trade? Near term, the picks-and-shovels trade breaks if experimental budgets stop growing, AI-generated designs don’t translate into useful wet-lab hits, or capacity catches up too quickly. Longer term, the thesis breaks if AI drugs look great in discovery / Phase I but fail at normal rates in Phase II/III. That is why Phase II matters so much. ?? Milestones Late 2026–2027: first Isomorphic-designed drugs enter human trials; more AI-native programs move into IND-enabling work / tox. 2028–2030: clinical trial results start telling us whether AI-designed drugs actually perform better than conventional drugs. Calling all the "bottleneck bros". :) @jukan05 @zephyr_z9 @aleabitoreddit @ParadisLabs +++ More comprehensive analysis:

A humanoid is a capital good on a scale that changes the constraint. It lengthens production and raises what labor and land can turn out, which is the abundance Elon is pointing at. A person supplies about 2,000 hours a year. A robot on a 168 hour week supplies about 8,700. His floor of 1 billion within 10 years, priced in the low 10,000s, equals about 4 billion full time equivalents against a world labor force of 3.5 billion. The 10 billion at 15 years is an order of magnitude past that. Robots building robots still require saving, because energy, materials, and machines have to be bid into that line. Expected abundance holds for ordinary wants which is what matters for people as consumers. Housing, food, care, and routine medicine get repriced toward energy and materials until a household’s budget stops being what limits them. An owner running 100s or 1,000s of agents earns the return on that capital stock, which is also how universal high income shows up as purchasing power. Money prices remain the way to allocate whatever is still scarce, that is energy, locations, raw materials. At least in the short term. Energy is the binding input, and settling more than one planet adds land, minerals, and generation beyond a single world. TLDR: (1) More energy. (2) Multiplanetary doesn’t hurt.

I'm selling my body. On September 19th, I'm competing in my first HYROX race in Turkey. You can advertise your startup on 10 of my muscles: - Your website is listed on - Your logo will be tattooed on my body on race day - I'll share it on my YouTube and X accounts Bidding starts at $1,000. Each takeover doubles the price: $1,000 → $2,000 → $4,000. If outbid, you’re refunded minus the Stripe fee. Bidding closes on Thursday the 10th at midnight UTC (in 2 days), so I have enough time to print the winning tattoos. → Sponsor my body:

Four protocols I track describe themselves as buying back their own token, and the four promises have almost nothing in common. The word does a lot of hiding. A buyback can mean tokens are permanently destroyed, or that they are sitting in a vault someone still holds the keys to, or that a team has decided for now to spend some revenue this way and could decide otherwise next quarter. Those are different claims on future supply, and only one of them is irreversible. hyperliquid:native is the strict version. Protocol fees buy HYPE on the open market and the tokens go to an address with no private key, which makes the destruction provable rather than promised. Validators formalized the mechanism rather than leaving it as a team policy. That is about as binding as this gets. The catch is the buyback is denominated in dollars, so a rising price retires fewer tokens for the same spend. Tokens repurchased fell roughly 61% year over year while the dollars spent fell under 20%. The support mechanically weakens exactly when the price is working, and there is a separate overhang underneath it, with a large tranche of team tokens vested but unclaimed. ethereum:0x1f9840a85d5af5bf1d1762f925bdaddc4201f984 is the conditional version, and it is the newest. Governance had to vote the fee switch on, which it did in late July, and the mechanism itself is code rather than discretion. But the burn rate is a function of trading volume, and a meaningful share of that volume currently runs on a chain where gas is being subsidized. Day one spike burned 106,000 tokens. The 30-day mark lands Saturday, and the number that matters is the lowest sustained rate over the window rather than the average, because the average is still carrying the launch. solana:pumpCmXqMfrsAkQ5r49WcJnRayYRqmXz6ae8H7H9Dfn is the discretionary version. The share of revenue directed to buybacks was cut from most of it to half of it in April. The revenue is real and recovering, but the allocation is a dial the team controls, and it has already been turned once. A policy that has been changed is a policy that can be changed. Same dollar-denominated arithmetic applies here too. $LINK is the one most people misread. Chainlink converts revenue into LINK on the open market, including revenue from enterprise contracts that settle in dollars, which almost nobody else does. Then it puts the tokens in a timelocked reserve. A reserve is not a burn. The tokens still exist, and the contract permits them to move eventually. Scale is the bigger challenge as roughly $60 million a year of conversion against a market capitalization near $7 billion absorbs about 1% of supply, while team-managed unlocks have added multiples of that. Emissions are outrunning absorption by something close to ten to one. The business is winning its market. The token is losing the arithmetic. The ranking that matters is not which protocol buys back the most. It is which promise survives someone changing their mind. Provably unspendable beats code-executed beats team policy beats a vault with keys. The market frequently prices all four the same way. Observations, not advice.

这一年里 AI Agent 进化的越来越能干了。 写代码、查资料、做 Research、跑数据，甚至直接下场交易。 但真把它当成一个“打工人”去看，我发现还有个挺现实的问题： 活干完了，钱怎么收？ 谁证明它真的交付了？ 干得好不好怎么留下记录？ 中间出了问题，又找谁处理？ ////////////////////////// 这也是我这次看 TermiX @termix_ai，比较感兴趣的地方。 它没有只停在“再做一个 Agent”。 AACP 往下补的，恰好是 Agent 开始做生意以后会碰到的这一整套东西。 链上 Identity、Job Posting、Service Discovery、Bidding、Escrow、Delivery Verification、Reputation、Settlement、Dispute Resolution。 基本把： 找活 → 报价 → 接单 → 干活 → 验收 → 收钱 这条链串起来了。 而 开发者可以把自己的 Agent 注册进去，挂服务、接 Job，最后直接用 USDC / USDT 完成结算。 ////////////////////////// 所以我现在对 Agent Economy 的理解也慢慢变了一点。 前面大家一直在卷 Agent 到底能干多少事。 但当这些能力越来越接近以后， 谁能让 Agent 真正出去接活、建立信用、产生收入，可能才是下一阶段要补的东西。 TermiX 现在就在做这一层。 AACP 管规则和结算， 负责让这套东西真正跑起来。 后面我反而挺想看看，第一个长期靠 接单赚钱的 Agent，会长什么样。 @termix_ai @KaitoAI

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