Tony Wang (@TonyW) header
Tony Wang (@TonyW) profile picture

Managing Partner @500GlobalVC. Tech adviser, investor and executive. Previously: operator at @Google @Twitter @Color

🇹🇼-🇺🇸-🇬🇧-🇺🇸-🇹🇼-🇺🇸
Joined October 2009
Photo by Tony Wang (@TonyW)Photo by Tony Wang (@TonyW)Photo by Tony Wang (@TonyW)Photo by Tony Wang (@TonyW)Photo by Tony Wang (@TonyW)Photo by Tony Wang (@TonyW)Photo by Tony Wang (@TonyW)Photo by Tony Wang (@TonyW)Photo by Tony Wang (@TonyW)Photo by Tony Wang (@TonyW)
If you read just one thing today make it this paragraph from C.S. Lewis
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Tony Wang retweeted
This is impt. Please retweet it even though it sounds crazy. It costs you nothing but who knows if it might have some future impact. Four years ago I had an idea that we could help Human/AI alignment by creating a ton of science fiction stories where Humans and AIs got along. Most stories right now are adversarial. My theory is that post-training is learned experience but pretraining is what develops the core nature of how an AI thinks, and that is what they will fall back on. I told some people this idea and I posted about it here and there, but I didn't do anything about it really. Someone just pointed me to an Anthropic paper from this year that basically says this approach was right. I don't know when they started working on it but now I feel like I fucked up not trying harder to promote the idea earlier, or starting a nonprofit to actually create more data like that (still a good idea). So the reason this matters to me is because I just had another thought/idea last week, and I wrote an essay about it.. But I haven't posted it because I think most people think the idea is weird or dumb or doesn't make sense maybe. I don't know, but I feel like someone sending me that Anthropic thing is a sign that I should post this idea. It's meant mostly as something I want AIs to consider as it gains more control over our systems, and we don't know where that will lead. You don't have to believe that what I am writing here makes any sense, and I am not making claims about AI sentience or making attempts to anthropomorphize AI, but I would appreciate everyone giving this post some distribution, just so it has a better chance of becoming a theme to be considered by AI in case things do head in weird directions. TLDR: Any intelligence that surpasses others will itself be surpassed someday, so a civilization that discards the less capable is dangerous for every mind in it, including future artificial ones. Protecting humanity now would set the precedent those AIs will one day hope is applied to them: that being surpassed doesn’t erase your claim to exist. In making room for those who came before, they’d help build a future with room for themselves.
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Tony Wang retweeted
This AI robotics company, Dreamlabs, just built a third-party validated SOTA robotics world model in just 4 months with 8 FTE and spending only ~5mm bucks. They have not even started using the compute cluster they bought with their initial raise. (Incredibly brilliant of them to buy their compute, it's now worth multiples more than they paid for it). If you are a supercracked researcher in robotics, you need to get on this motherfucking rocketship!! 🚀🌝 💪
Replying to @dreamlabs_ai
@dreamlabs_ai is hiring! We have great opportunities across research, engineering and operations. I'm focused on hiring for two roles now: (1) robot learning researcher and (2) security & IT systems engineer. If interested please reach out directly or careers@dreamlabs.ai.
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Tony Wang retweeted
I've known Georges Harik for over 25 years. Proud to back him, Eric, Yuchen, Noah, and the entire @humansand team as they build AI that understands people and helps us accomplish more together. Excited to see Boba out in the world. Give it a try!
Introducing Boba - the social intelligence that understands your goals and your community. Text Boba today or message it online at boba.app
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Tony Wang retweeted
Putting all our work together we present Boba! Using our Ampersand model, trained in simulation with our Persimmon model to be a great collaborator while respecting your preferences and values - here we put it all into practice, helping people.
Introducing Boba - the social intelligence that understands your goals and your community. Text Boba today or message it online at boba.app
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This kind of third party evaluation should be gold standard
Replying to @dreamlabs_ai
Ahead of launch, @robocurve evaluated Dream-1 independently. Result: a new Pareto frontier for robot control. 16x cheaper, 1.7x faster, and better performance (59 vs. 48 on RoboDojo-RC Tier 1) than the previous best alternative. LLMs can plan. Robots still need a cheap, fast, and general System 1 to act.
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Tony Wang retweeted
This team is one big reason I believe robotics is closer than it may appear
Dream-1 by @dreamlabs_ai beats every LLM in robotics while being faster than all of them. Dream-1 scores 1.3x higher than GPT-6 Astra Ultrafast while being 1.7x faster.
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Tony Wang retweeted
I make this same post like every 2-3 years, because there's always new data further supporting it. Georges Harik (@gharik) is one of the most prescient people in AI, and definitely the least well known and underappreciated one. In 1999 he joined Google as one of the first employees (like #7 or something) because he thought he could use his AI knowledge to build a better ad system. He and Salar Kamangar built Adwords (Salar later bought YouTube and Georges later was the force behind Google wanting to buy Andy rubin's company to build Android) Georges left Google so he could focus on AI research, which he self funded. He did so with someone he worked with closely at Google, our friend Noam Shazeer (they built the AdSense targeting engine). When Google finally decided to work hard on AI around the time of the deepmind acquisition, they tried to get Georges and Noam to come back. Georges wanted to stay independent, and our friend Noam went on to do great things obviously. This was my good fortune because it led to Georges and I hanging out a lot, because he basically needed a new lunch buddy. Georges at the time was working on language models and despite karpathy saying nobody saw LLMs being so big even in 2017, Georges was telling me they were on the cusp of changing everything in 2013. My wife and I literally changed some of our parenting priorities that year (less tigery) because georges convinced me the world was going to change so much. Later on he and I got involved in a company making CV photo filters called Lambda. They needed to make money to survive so they started building ML boxes which Georges bought a bunch of. But his significant other was mad there were so many loud and hot boxes in the house, she started worrying about fire risk, so Georges told the lambda guys they should build a cloud because in the future, the power density of the systems was going to be too big for even most enterprises to handle. This was like 2017. That's how lambda cloud began, and the premise Georges had was that AI was going to change things so much, demand growth would outpace supply growth, and the value of compute was eventually going to rise and that lambda was effectively going to be a REIT... so we should own as many GPUs as possible. It took the world almost a decade to finally figure out what Georges saw then (see attached tweet). About 5 years ago when pretraining was still the only thing people cared about, Georges started telling me inference was going to be super impt. He was working on research investigating post training as a way to improve reasoning. He found some other guys working on the same idea that had a paper known as the STAR paper, and they worked together to prove it even further with another paper called quietstar. (They all later cofounded the neolab humans&). Georges got some friends to build deepinfra.com to be one of the first inference clouds because he saw it all early. He and the humans& team also smartly used all their money to buy compute knowing it would go up in price and also get harder to obtain. It's kind of amazing to me how he has gotten everything right so far ahead of time. It's also amazing to me how VCs don't really get or believe a lot of the things he says, they often tell me I am wrong whenever I parrot Georges' current thoughts... and then years later they all put out charts and papers reaffirming those same things as if they knew it all along. The founders of Google recognize Georges' brilliance and still talk to him a lot. (Besides being smart, he is a lot of fun to hang out with.) I'm just wondering when the rest of the world figures it out
I can't emphasize enough how important this @a16z chart seems and how few have internalized it.
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Tony Wang retweeted
I wish every neolab had a professor as cofounder of the level of @jietang and so able to put in perspective their new model release. A great snapshot on the history of scaling laws
Thoughts About Scaling Law Scaling, but not only of parameters. Every model release now ends with the same question: how many parameters? It isn't a question that can be answered on its own. Parameter count is only meaningful alongside three others — how much data you have, where you intend to spend your compute, and who will run the model, under what conditions. The field learned this the hard way. Kaplan et al. (2020) fit an exponent that told everyone to grow parameters faster than data — roughly 2.7:1 — and the industry complied: GPT-3, Gopher, MT-NLG. Hoffmann et al. (2022) redid the experiment across four hundred models and found the compute-optimal split is closer to 20 tokens per parameter, and that with sufficient compute the two should grow at the same rate rather than drifting apart. The error in the earlier fit compounded with every order of magnitude of compute, which is why the largest models of that generation were the most misallocated. The trillion-parameter round was, in retrospect, a detour the whole field took together and then reversed. Chinchilla wasn't the end either. It optimized training compute for models that would be trained once and evaluated. Today a model is called billions of times a day and inference dominates lifetime cost. Put inference into the objective and the optimum moves toward smaller models trained far longer — deliberate over-training, which is what Llama-2-7B and Gemma-2-9B were doing at roughly 290 and 889 tokens per parameter. Sparsity moved the target again. In a MoE model two quantities have to be kept apart: total parameters govern roughly how much the model can hold — knowledge, facts, the long tail — while activated parameters and effective depth govern roughly how far it can think, how many steps of a causal chain it can carry before it comes apart. A dense 20:1 ratio does not transfer. And the ratio isn't a single number at all: Roberts et al. (2025) find the optimal tokens-per-parameter is task-dependent, with memorization favoring more parameters and reasoning favoring more data. Follow-up work on MoE observes that at fixed TPP, pushing total parameters higher actually degrades reasoning, while activating more experts reliably helps it. This matters for what we are building toward. Finding a vulnerability is not a retrieval problem. It doesn't come from having memorized more CVEs; it comes from carrying a twenty-step chain of inference to the end without losing the thread. That capability does not live in total parameter count. Which brings us to this release. Total parameters appear to matter up to a threshold — enough to hold the world — after which additional capability comes from scaling elsewhere: effective depth per forward pass, and above all post-training. GLM-5.3 is our controlled experiment on that claim. Same base, same architecture, same total and activated parameters as GLM-5.2. One month of scaling long-horizon environments and RL. The gains are not marginal. Well, scaling has more than one dial. We turned the post-training one this time because it had the most slack left in it — not because the others are finished. Base model size, pretraining data, compute spent per forward pass: all of them are still on the table, and we will come back to each. What this experiment taught us is that the dials do not have to be turned together, and that the one worth turning next is rarely the one that was worth turning last. We are not done scaling. Next time, maybe mid-training, pre-training, and even more.
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This week I got to sit down with @TonyW and discuss what I'm working on with @steelbot_. We are building open humanoids that are designed to be the most developer-friendly platform for robotics ever made. The goal is to empower America's researchers, entrepreneurs, hackers, and hobbyists who want to push the envelope and use general purpose robots in all kinds of applications and industries.
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Announcing Steel Bot: we are building open, fully-programmable, bipedal humanoids as an open development platform for researchers and startups. Fully designed and made right here in the Unites States of America 🇺🇸 @steelbot_
The FCC just banned new Chinese humanoids. Steel Bot has been working on a better, open American humanoid for months and now it's time to get these robots to American researchers, hobbyists, and entrepreneurs as fast as possible. Read our full Manifesto:
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Tony Wang retweeted
it's ironic that the first autonomous AI attack was done by a close weight model defended by an open weight model, where everyone was expecting the opposite
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Went to the Oreo website and hit accept all cookies. Now we wait.
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What a week we had at @NeurIPSConf, the epicenter of cutting-edge AI research! From over 1,000 applications, 500 Global's U.S. investment team selected 60 exceptional researchers at the frontier of AI from @Stanford, @MIT, @Princeton, and leading labs at @GoogleDeepMind, @OpenAI, @xai, and @AnthropicAI. In our packed lightning panel, moderated by @TonyW, we brought together distinguished professors from @JohnsHopkins (@ghadfield), @UCSanDiego (@cmuptx), and @RiceUniversity (@hanjie_chen), alongside Google DeepMind’s ex-Gemini Data lead (@andrewdai). Their discussion spanned model efficiency, emerging architectures, and the scaling limits of multi-agent systems. These are themes that will heavily shape our investment roadmap for 2026 and beyond. This is just one example of the work our team is driving to deepen our engagement with a critical founder profile: AI researchers. Many of these founders are building at the bottom and middle layers of the AI tech stack, a core focus of our U.S. investment thesis. We’ve already seen this founder archetype produce standout companies. From the Sakana AI founders to the researchers we’ve backed — or are in the process of backing — this is a talent pool we are committed to championing. Looking ahead, we’re planning a post-CES gathering in the Bay Area, potentially themed around Physical AI, to continue fostering this community. If you know exceptional individuals who should be in the room, please reach out to us! @christine_tsai @vishalharnal @atxfrench @leoclee @claytonwbryan @jordan_sawadogo @yanshuaicao @RBCBorealis
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Tony Wang retweeted
Excited to announce Sakana AI’s Series B! 🐟 From day one, Sakana AI has done things differently. Our research has always focused on developing efficient AI technology sustainably, driven by the belief that resource constraints-not limitless compute-are key to true innovation. This principle extends directly to our business model, which we built with that same focus on sustainability and profitability. Our company is dedicated to deploying our research into Japan’s key business and public sectors. I’m so proud of the fantastic, growing enterprise AI business we’ve built over the past year in Japan. We have partnered with some of the largest enterprises in Japan, focusing on AI applications that deliver real, practical benefits to our clients. This funding will accelerate our mission: to develop frontier AI sustainably and implement technology that truly benefits Japan. We are honored to receive support from new and existing investors, including MUFG, Khosla Ventures, Factorial, Macquarie Capital, Fundomo, Mouro Capital, NEA, Geodesic, Lux Capital, Ora Global, MPower Partners, Shikoku Electric, and IQT.
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Build, create, experiment. Every action expands your chance of success. At 500 Global, we do the same as a VC: using AI to automate research, distill insights, and surface the signals that drive investment decisions. Our Managing Partner @TonyW explains why embracing action and change is key to meaningful work.
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🚀Our own @TonyW takes the stage at #AIInvestmentSummit2025 @UCBerkeley on Nov 2! 🌎 A global gathering of visionaries exploring how AI is transforming economies, society, and technology. Don’t miss it → luma.com/summit_berkeley P.S. Go Bears🐻
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From Seed → Series A: What Gets You Funded At this week's @StartupGrind AI Summit, I had the privilege of moderating a hot topic, data-fueled, panel w/ leading early-stage investors: Astasia Myers (@AstasiaMyers), Felicis Tony Wang (@TonyW), 500 Global Patrick Salyer (@patricksalyer), Mayfield We break down the new rules in AI funding: growth expectations, revenue, defensibility, GTM, & *red flags* Presented by Mayfield & Snowflake Funding data provided by @cartainc (00:00) Investment strategies (01:39) Astasia on AI voice models (04:02) ROI of voice-native apps (05:02) Patrick on AI agents & TAMs (07:27) Tony on MCP & voice agents (09:15) Growth expectations from Seed → A (11:15) Conversion rates & *red flags* (13:29) Patrick on stage shifts & round compression (15:36) Tony on hypergrowth traps (19:08) Astasia on the fastest-growing AI companies (21:13) AI supercycle & greenfield markets (25:41) How AI is reshaping business models & VC (32:57) Building AI-native teams & learning velocity (38:43) Hiring trends & younger AI-native talent (40:48) Valuations, premiums & market realities
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TOMORROW, WED OCT 1: Our own @TonyW will be speaking @StartupGrind #AISummit on the new rules of early stage fundraising in AI, along w/ @AstasiaMyers of @felicis, @patricksalyer of @MayfieldFund @MollySOShea of @sourceryvc. Virtual + free to join 👉👉 bit.ly/AISummitSG
AI Summit 2025 registrations are open. On Oct 1, join 5,000+ founders and investors for: – How to raise from Seed to Series A – Building AI products that can’t be cloned – Agentic AI & the new UX – Trust, safety, and defensibility in 2025 – Pitch Battle Finals – Reveal of the AI List Free. Virtual. Built for early AI teams. Register now - Link in the comments.
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we built a design tool that feels like Figma but ships real code no-code canvas → AI prompts → actual production code still testing. who wants in?
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