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📈 Speaker Spotlight: Viki Huang What if AI's biggest payoff isn't productivity — but longer, healthier, better human lives? Viki Huang is an investor at Prosperity7 Ventures, the $5B global growth fund backed by Saudi Aramco, where she backs AI, semiconductor, and enterprise companies from Series A to pre-IPO. 🔹 Investor at Prosperity7 Ventures ($5B Aramco-backed growth fund) 🔹 Focused on AI, semiconductors & enterprise software (Series A → pre-IPO) 🔹 Prior: business development at AI-chip startup Tetramem · deep-tech investor at Atlantic Bridge 🔹 Duke MQM · Columbia economics & mathematics Her bet: the next era of AI won't just optimize work — it'll extend healthy lifespans and elevate daily living. Hear Viki's investor outlook at AGI Summit SF 2026. 📅 July 18–19, 2026 📍 Palace of Fine Arts, San Francisco 🎟 Tickets: 🏷️ 15% off with code GenAI-26 #agisummit# #aiareall# #venturecapital#

50 Sites That Show What Billionaires Buy and Read for Free🤯 1.) → Current portfolios of famous investors 2.) → Quarterly fund trades 3.) → Stock transactions of US politicians 4.) → Executives' own company stocks 5.) → Making fund filings readable 6.) → Companies' official reports 7.) → Buffett's letters since 1977 8.) → Video archive of Buffett meetings 9.) → Howard Marks' investor memos 10.) → Research from Ray Dalio's firm 11.) → Dalio's principles for free 12.) → Jeremy Grantham's market notes 13.) → Quantitative fund research 14.) → Damodaran's valuation data 15.) → Valuation professor's blog 16.) → Morgan Housel's money writings 17.) → Mental models for decision-making 18.) → JPMorgan's free market guide 19.) → Innovation-focused fund research 20.) → Venture capital essays 21.) → Value investing lecture notes 22.) → Columbia's value investing archive 23.) → Fed's economic data archive 24.) → Fed decisions and statements 25.) → European Central Bank data 26.) → Reports from the central bank of central banks 27.) → Countries' economic indicators 28.) → Companies' long-term charts 29.) → Company financials for free 30.) → Companies' market cap rankings 31.) → Stock screener and heat map 32.) → Portfolio backtesting 33.) → Charts and market analysis 34.) → Professional market screener 35.) → Company activity reports archive 36.) → SEC's investor education site 37.) → Passive investing encyclopedia 38.) → Financial terms dictionary 39.) → Free finance guides 40.) → CFA research publications 41.) → Fund and stock evaluations 42.) → Companies' visual analysis 43.) → Archive of selected investment theses 44.) → Seeing what's inside funds 45.) → European fund backtests 46.) → Investment writings with data 47.) → Market history notes 48.) → Investor psychology writings 49.) → Explaining the economy with charts 50.) ourfiniteworld? → (see note) The rich don't hoard information. They just don't tell you where it is. Save it, follow @Liyana_Coder you'll need it. Not investment advice. @Liyana_Coder

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)

Many of the great VC firms and solo GPs started out with funds of $10M or less. These micro VC firms have a risk appetite which is crucial to the industry, and play an important role in seeding risk capital where larger firms cannot. "Micro VCs play a pivotal role in democratizing access to entrepreneurial finance by allowing ventures to get funding from a new type of investors. Moreover, micro VCs encourage more kinds of limited partners to access the VC industry." - Micro VC (2022) Unfortunately, the associated regulatory costs make operating at this scale incredibly difficult in the UK. Admin and compliance often consumes about $1.4M of a $10M UK fund, versus just $450k in the US. As a result of this high cost, the median fund size in the US was $20M in 2024, in the UK (with just 3% of the volume) it was $88M. So, the UK not only has lower total venture capital volume, it also has fewer small funds — the worst of both worlds. This is a major bottleneck, but there is a fix. If you're interested in hearing more, drop me a DM. (Credit to @cupazhou and @samhuleatt for the graphic below, from The Side Letter.) Micro VC paper:

We're excited to announce that Beldex has secured a new round of funding, led by @Sigma_VC and supported by leading venture capital firms. This investment will accelerate product development, ecosystem growth, and our vision of building privacy infrastructure for Web3.

This is the capital of robotics! 🌁 Silicon Valley is home to so many physical AI companies that you could spend a month visiting them and still not see even 10% of them (trust me, I tried). It took me 3x to create this map as it did to create any other. And the truth is, it's still incomplete. Let’s see why this is the case. So, the Bay Area is the world's leading ecosystem for robotics startups, bringing together top AI talent, top universities, experienced founders, and unmatched access to capital. The region is anchored by Stanford University and University of California, Berkeley, two of the world's top universities for AI and robotics. They produce a constant stream of researchers, founders, and breakthrough technologies. The Bay Area is also home to many of the companies shaping the future of robotics and AI, including @Figure_robot, @physical_int , and major AI labs such as @OpenAI. This concentration of talent makes it easy for startups to recruit experienced engineers and collaborate with leaders in embodied AI. Not mentioning that it  is also home to leaders such as @NVIDIARobotics , whose headquarters and leadership in AI chips power much of today's robotics revolution, and @Tesla, whose work on autonomous driving and humanoid robots has created a deep pool of robotics, AI, and manufacturing talent. Perhaps its biggest advantage is access to capital and ambition. The Bay Area has the world's deepest network of venture capital firms, serial entrepreneurs, and technical leaders who are willing to fund bold, long-term robotics companies. In the comments I'll post the companies from the ecosystem. ‼️ Note that Bay Area has >300 robotics companies, research labs, and innovation hubs, so this is a curated selection of the notable product companies, not an exhaustive census! P.S. I'm constantly working on improving these maps, so if your company is missing, please DM me with basic info about the co, and I will include it in the next release. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Hedge fund managers, venture capitalists, and tech executives are pulling their kids from public and private schools to enroll them in AI-tutoring startups that pay staff to stop calling themselves teachers and face no state requirement to report how well any of it works. Alpha School, now in a dozen cities and expanding to nearly two dozen more this fall, charges $75,000 a year in San Francisco for two hours of AI tutoring followed by project workshops, using software that tracks how closely a child pays attention. Forge Prep in New Jersey charges up to $36,000 and promises graduates a $200,000 payout if they start a company instead of taking a job. Billionaire Bill Ackman has publicly backed Alpha. Neither school reports outcomes to any state, and Forge's own founder admits there is no data showing things are going well. Alpha sells home-schooling software on top of tuition. Forge's startup fund assumes graduates will found companies rather than join a labor market its own AI curriculum is designed to help automate. Alpha's spokeswoman says its families are overwhelmingly finance, venture capital, and tech workers, meaning the industry building the AI disrupting entry-level jobs is now selling itself a private curriculum to land its own kids on the ownership side of that disruption. Alpha's in-person staff voted to reject the word teacher, and its remote coaches monitoring the AI software are scattered across the globe. Stanford's Victor Lee says dropping the title diminishes the professionalism teaching requires. Stanford's Caroline Hoxby, who studies these programs rather than sells them, says there is negligible scientific evidence behind any of it. My take: The same investor class funding AI tools built to gut entry-level jobs is now buying its own children a private exemption from those tools' consequences, for $75,000 a year. Public schools absorb AI disruption unfunded and unreformed while this cohort builds itself an unaccountable alternative marketed as personalization. This is what class stratification looks like once it gets rebranded as coaching, guides, and life skills.

How did China build humanoid demand before the market existed? @UnitreeRobotics shows the playbook: a four-rung ladder where each step funds the next. 1. Research sales fund tooling costs 2. Consumer/commercial fund volume manufacturing 3. State procurement funds fleet deployment at scale 4. Industrial pilots feed real-world training data Unitree’s quadrupeds climbed all four rungs in 7 years. Their humanoids are currently on step 1. US players like @Figure_robot and @Tesla skip straight to step 4, industrial labor. It’s the highest-stakes market, relying on venture capital to cover the massive R&D gap until the tech matures. Which path hits industrial scale first: the ladder, or the sprint? Deep dive through Unitree’s IPO prospectus and @UBTECHRobotics public filings in the report below:

“The continent should immediately create the market conditions for its own AI labs to start, scale and win,” writes Judith Dada, a venture-capital investor, in a guest essay