Which startups are most likely to get an investment from top tier VCs? I looked at ~60 attributes across 5000 startups and these were the 15 strongest. #1# is being from a top tier fund. MBA is a negative signal Full list of signals below:

There is an absolute barrage of inference providers popping up that get to $1M/mo extremely quickly. This is the rough economics of inference providers: Even if you rent on-demand B300s at $8/GPU-hr, many open source models (GLM 5.3, Kimi K3, DeepSeek 4.1 Flash / Pro) can get you >$10-20/GPU-hr at reasonable throughputs (100tok/s). DeepInfer from DeepSeek directly shows they can get $38/GPU-hr on off-peak hours. Every chip / model has a different throughput / interactivity curve, but there is a lot of arbitrage between even high rental spot prices and token prices. New chips (Vera Rubins) also change this meaningfully. Your ability to be making gross margin relies on your utilization and how good your stack is. Gateways like OpenRouter, Vercel and more help solve that problem by aggregating demand to you. May inference providers discount their tokens to get longer term commits too. I've even heard stories of selling back Chinese model tokens to Chinese labs! One thorn in the economics of these players is that many Chinese open source model labs are now asking for rev share if you're at a certain scale, espp for pre-release access to their models for day 1 support. That further pushes the threshold to be in the money. The barrier to entry seems low and falling. There are even stories of smaller ones who can "vibe code" a way to stand up OSS models on a cluster they rent. And as a result, we're seeing 10s of such companies pop up almost every week and immediately skyrocketing revenue at low-ish gross margin and raising rounds on the back of that. Unsurprisingly, because the actual service provided seems commodity, this eventually drives up compute prices and drives down token prices. In theory, it would seem like neoclouds who have the lowest cost of compute and themselves have started inference businesses would win the race to the bottom (Crusoe Coreweave Nebius and GMI all have inference businesses), with larger players (Modal Fireworks Baseten) being second because they are well capitalized to make bigger compute commits cheaply and have demand (high utilization), but the reality is the pie is growing so quickly that even upstarts are absolutely crushing it and growing aggressively (inference dot net, DeepInfra, Wafer, Relace, Morph). Strategies vary across the board: neoclouds want to serve inference, inference players want to be neoclouds and do higher margin reserved instances / post training, and some want to go heterogeneous hardware or creatively flexible compute constructs. The risks are: do open source models continue to be competitive? Will their creators rent seek more? Will their shape change compute needs? How do different chips change economics? Who gets compute in a supply crunch? Below is a aggressive sketch of how gross margin scales with utilization for various models / chip sets. For now, there are many inference providers and they’re all ripping!

This is serious. I just saw with my very own eyes that even non-technical people can exploit CVEs like PHP2Shell using certain AI models and I am actually frightened. Once you figure it out, it is downright trivial to do. The digital infrastructure of the world is at risk.

Meta Muse's retention is out, and it's almost as good as ChatGPT! It's higher than ChatGPT at D7 (>30%) and just lower at D14 (22%). Not quite in the elite D14 tier of WA (67%) YT (59%) Insta (54%) or TikTok/Snap (37%), though. Muse is one of the highest retaining AI products!

I’ve had 3 diff people tell me they are 100% sure this “fastest to $1B annualized revenue” is 3 diff companies. Very likely more than 1 data co has recently hit that mark, many of which are on Pavlov’s List. Many more will get there in the next 6mos. Total data spend is >$15B.

OpenAI’s math release is, in the words of Opus, “the single most consequential mathematical release ever” * LLMs have how made substantial progress on 4 of 7 Millenium Prize problems: Navier-Stokes (claimed), Riemann, Hodge and Birch-Swinnerton-Dyer. Poincaré was solved in 2003. The two left are P v NP and Yang Mills. *conditional on verification On average, each OpenAI result used only 3hrs of thinking compute on their new unreleased models. Two years ago, models said 9.11 > 9.9. Today, we have results that the smartest human minds have not been able to achieve in their entire lives. It is clear that data, compute and algorithms scale. Every model generation (3mos) has made substantial intelligence progress. It also becomes incredibly hard to not believe that all knowledge work will change monumentally over time. The things AI can not do in the foreseeable future are very likely context-bound (don’t have access to the right information) than intelligence bound. Some might argue they are also creativity-bound or judgement-bound (what should I work on), although it can be argued that future generations could solve for this (given, say, the advancement in research taste for models over time). AGI is defined as surpassing human capabilities on virtually all cognitive tasks. By most interpretations of that definition, we are there. The domains humans are still better than AI, such as, robotics / physical world control (data-bound?), natural science research (data-bound), some creative domains like writing, movies, music (creativity-bound), super long tasks (context-bound), choosing problems to solve (creativity-bound) and maybe human relationships management (meat-proxy bound?). In many ways, we have achieved AGI.

Google is an incredible company that I’ve always rooted for that trips over its own bureaucracy in frenetic times. They ironically do a middling job at their first priority, but crush it at their second or third. We’ve seen this with Search, Google+, Assistant, Cloud and now even AI. Why? The internet would be unrecognizable without Google. This is a company that has Search, Chrome, Android, Workspace (Docs/Sheets), Drive, Play Store, YouTube, Gmail, Maps, Photos, Translate, Gemini, Calendar, Meet, Chromebooks, Waymo: some of the most widespread and most undermonetized products in the history of the world. It is inconceivable for most of us to live life without software Google has made available for $0. And truly, having worked there, this is a company that continuously prioritizes their users over profits. Even as a casual reader, it might be trivial for you to imagine 100s of ways to monetize all of Google’s products. But they usually don’t. They don’t just launch experiments because it’s high engagement, but only when it’s actually good. So how does a company like this falter? My observation has been that these frenetic eras either attract or condone the worst behavior out of execs that trickle down to the rest of the company. The most “ambitious” L3-L7 people who feel stuck finally see a company-wide priority and their eyes light up with the gleaming prospect of a promotion. A lot of people in big orgs’ entire sense of self esteem is wrapped around their level. They will do a lot for an N+1. “Did you know Sergey is personally working on this?” “Sundar referred to my project in the all hands” In turn, everything in these orgs become a knife fight for getting the most “high impact” projects, fighting for credit, flagrantly hiding concerns around juiced metrics. More work goes into a promo packet than the actual project. Goodhart’s Law kicks in and the metrics measured for a promotion are abused beyond measure. On the other hand, second and third priority things work brilliantly. It attracts people with genuine interest, sincerity who are willing to play longer term games. Many products have grown and thrived when left alone. When there is immense pressure for a number to go up, it eventually does but often at the cost of product quality (the small product polish things don’t get me a promo), core innovation (why take on a high risk bet if I can get promoted for copying oai/ant) and cultural cohesion (a lot of bad blood amongst people in the org, tons of reorgs). There is a certain type of individual that thrives in this environment, and they are typically not very likable nor “Googley”. This is why an incredible number of people leave or allow jesus to take the reigns as they cash in the bag. I lament that every time this happens, we, the billion users, lose out on yet another beautiful Google product.

We’re at a point in late stage AI model capitalism where you know the frontier has to win in a bunch of benchmarks to launch. These numbers mean nothing. The thing to trust is the price. If they price it high, it’s a good model. If they don’t, it’s benchmaxxed.

America: a 250 year tribute. i made this 5 minute documentary completely orchestrated by opus with a montage of the remastered versions of the most iconic moments of US history

Higgsfield just entered the ranks of iconic startups that have reached $1B in revenue run rate. Alex Mashrabov (@alexmashrabov) is an absolute force of nature and we’re so fortunate at Menlo to have led the seed round.

Only 53 shows on IMDb with 9.0+ and 25k+ reviews. Only 10 premiered in the last 5 years. I watched one of them, a 1 season 16 episode Korean show called When Life Gives You Tangerines, and it’s the most beautiful family drama tear-jerker ever. One of the best shows ever.

“I think about time in model generations. Each one takes 3-4mos. 3 gen ago, I was writing code by hand. So when people ask me what’s going to happen in 2yrs, that’s 8 gens later. I have no idea.” Sensei @karpathy on the future of AI models.

Most people are wrong here. It IS actually possible to communicate between two airgapped computers with temperature changes, but the data transfer rate is too slow (1-8b/hr) to be effective. The underlying point Noam makes is true though: there are a deluge of crazy airgapped comms techniques like EM emanations from DRAM clocks that get a lot faster (300kbps). This isn’t sci-fi. We vastly underestimate what AI can do with perfect knowledge of all of this literature.

The deep amount of anxiety people in tech are feeling is off the charts. Had a couple conversations with people of various ages and the themes are: - Every day there are 5 new earth shattering new tech breakthroughs. I can’t keep up. Should I be keeping up? Am I falling behind? - What does my career and skill mean in the age of AI? Is software engineering a 30yr long career any more? Is product management? - Is my only hope to start a company? - At the very least, it feels like I should join a startup but which one? Everything seems either overvalued or has no moat. - Okay I joined a startup. Is my company’s equity worth anything or will this be another Airtable or Miro? - Startup is doing well. How in the world am I supposed to find a significant other? - Will these immigration policy shifts allow me to be here long term? - Will I ever be able to afford a house and a life in the Bay Area amidst the biggest wealth creation event of all time? - Okay, I figured all of that out. What is my purpose? I’m wealthy but no one really cares. I don’t really know what to do with it. Am I supposed to start a company? - Alright I found peace in kids. But I’m worried about them. How does one raise kids in this climate? What are the rules of this new world order? Many of these concerns are of course not limited to tech or the Bay, although it feels particularly concentrated here (versus what I see in NYC). Maybe some people will read this and feel anxious themselves, and for that I apologize. For the rest, just know that it’s not just you who feels this way. It’s a lot of people!

The deep amount of anxiety people in tech are feeling is off the charts. Had a couple conversations with people of various ages and the themes are: - Every day there are 5 new earth shattering new tech breakthroughs. I can’t keep up. Should I be keeping up? Am I falling behind? - What does my career and skill mean in the age of AI? Is software engineering a 30yr long career any more? Is product management? - Is my only hope to start a company? - At the very least, it feels like I should join a startup but which one? Everything seems either overvalued or has no moat. - Okay I joined a startup. Is my company’s equity worth anything or will this be another Airtable or Miro? - Startup is doing well. How in the world am I supposed to find a significant other? - Will these immigration policy shifts allow me to be here long term? - Will I ever be able to afford a house and a life in the Bay Area amidst the biggest wealth creation event of all time? - Okay, I figured all of that out. What is my purpose? I’m wealthy but no one really cares. I don’t really know what to do with it. Am I supposed to start a company? - Alright I found peace in kids. But I’m worried about them. How does one raise kids in this climate? What are the rules of this new world order? Many of these concerns are of course not limited to tech or the Bay, although it feels particularly concentrated here (versus what I see in NYC). Maybe some people will read this and feel anxious themselves, and for that I apologize. For the rest, just know that it’s not just you who feels this way. It’s a lot of people!

I’ve heard 100s of startup pitches this year. Here’s the good, the bad and the meh recurring trends from my experience: The good: - High raise amounts and valuations. More inflated for strong teams pre-revenue in hot areas (robotics, bio, personal agents) - Insane topline growth numbers, many growing >10x this year. By the time a round closes, a high valuation seems justified. - Tons of M&A It is what it is: - Revenue run rate = last month revenue x 12 - Small teams - Seat based < Platform fee < Usage based billing seems to be the trend for most products - Tranched rounds - Best teams have extremely fast product iteration speed - SaaS products positioning themselves as AI tools to seem appealing to investors The bad: - Low or negative gross margin if reselling tokens. Non-standard accounting - High “Contracted ARR” that’s not live yet - High revenue concentration - Expensive compute, limited availability, delays. Excess compute reselling - Many wrappers with very thin tech differentiation: “self improving harness”, “multi model / router”, “agent swarms”, “computer use” - Overworked employees. AI generated - All spaces seem very competitive - Areas can seem hot one moment before being not

DeepSeek might be the new king of open-source models. It obliterates GLM 5.3 and Kimi K3 on benchmarks while being 4x and 10x cheaper. An insane $0.3/M in, $1.2/M out. And it’s #6# amongst humans on Codeforces! This is exactly what startups with coding products needed.