Founder @yieldstreet co-founder @yodle Entrepreneur, idea generator, boston sports fan, scotch aficionado, change agent, loves disruptive models

New York City
Joined June 2009
Milind Mehere retweeted
Turns out the future does have flying cars
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Milind Mehere retweeted
After selling his Bentley to buy a Model Y: “[FSD Supervised] is life-changing. I was on the phone with my brother last night, and I made him buy one. He literally bought one while we were talking. I’m buying 10 of my employees a Tesla with FSD. It’s 8x safer than the average driver. There’s nothing more important than the safety of you and your loved ones.”
A public service announcement.
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Milind Mehere retweeted
First @united flight with @Starlink today (ATX to LAX) Such a game changer… can't wait for it to show up on the Japan and MENA routes
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Milind Mehere retweeted
Backstage before our panel on superintelligence at the launch of America.gov Will post the video when it comes out. Also @jgebbia has done something truly awesome for the country. We should all be grateful! 📸 by @KatieMiller
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Milind Mehere retweeted
We believe superintelligence will create significant new opportunities for all people and businesses. Meta already serves billions of people at scale and helps hundreds of millions of businesses reach customers. Today we are starting the next major pillar of our business, Meta Enterprise Platform, to help businesses use AI to grow and transform in new ways as well.
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Milind Mehere retweeted
Agent market is far from decided.... Fo is more trustable, safer with credit cards, much higher completion rates and much more! And humans in the loop help solve the harder tasks
I led engineering at Google DeepMind. Today, I'm proud to introduce Fo to give personal AI something no lab ever has... Humans. Other personal AI's pretend AI can do everything. Fo employs humans to do tasks that AI cannot. - 2x better at real-world task completion (beats other agents by 69%) - 94% trust rate (4x less likely to leak private info vs Muse, Instinct) Sign up for free: wajo.ai/join-wajo
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Milind Mehere retweeted
I have conducted an audit of Anthropic's finances. What I have found is so shocking that I am calling for a Congressional investigation. Anthropic is not just seeking regulatory capture. It has built a regulatory capture machine that cannot be turned off. Structural financial incentives make it impossible for Anthropic -- I call it the Anthropic Network -- to turn off its own AI doom cycle. It starts with METR. Dario Amodei proposes "third-party evaluators" to assess the risk of Anthropic's models. He proposes METR for this purpose. But METR is financially dependent on the Anthropic's success -- specifically, on the explosive growth of more than $7 billion dollars in Anthropic stock. Dustin Moskovitz invested this stock into Good Ventures Foundation, where it represents the majority of that organization's portfolio. And GVF is the overwhelming funder of the entire Anthropic Network ecosystem. This stock was worth $500 million early last year. It is worth more than $7.7 billion just ~16 months later. METR -- and all of those building a career its parent organizations -- cannot afford to disrupt that growth. Because if Anthropic goes under, many of the organizations that fund METR go under as well. But if Anthropic succeeds, METR and its parent organizations become more richly financed to regulate AI -- something those at METR want very much. The "third-party evaluator" is not "third-party" at all. The evaluator is on Anthropic's payroll. If this were the end of it, that's bad. But that isn't all. The same organizations that fund METR also fund the many organizations, such as the Tarbell Center, that promote AI Doom. The Tarbell Center publishes AI Doom articles in The Verge, Science, LA Times, The Dispatch, TIME, and others. They are selling the problem, and then selling the solution to the problem -- from the same money pile: Anthropic's. All of these organizations are financially dependent on the same exploding $7 billion money pile. As Anthropic grows more and more powerful, its AI Doom Machine grows better and better financed -- louder and louder. Meanwhile, the regulatory regime seeded in METR grows larger to solve the increasingly loud -- now hysterical -- problem of AI Doom that the Anthropic Network itself created. From this standpoint, as Anthropic becomes more powerful, AI might be getting scarier, sure -- but the positive feedback loop also becomes more deafening -- independent of objective facts. This itself is an objective fact. The deafening AI Doom is part of an business model, that, as it expands, so too does the AI Doom messaging -- there is simply more money to do it. But the problem also goes in the other direction: If Anthropic dies, the Regulatory Regime and the AI Doom Machine are crippled or die. Neither METR nor Tarbell nor the other organizations in the Anthropic Network can allow that to happen. Hence, neither METR or the AI Doom Machine can be trusted to provide independent assessments of Anthropic's models or AI more broadly. They simply are not organizations independent of Anthropic. And Anthropic cannot detach itself from METR or Tarbell or countless other safety orgs (not shown here), either, because they drive hype for the models and the possibility of eventual regulatory capture, and Anthropic will not give that up willingly. What's more, the people at all of these organizations are all the same ecosystem, the same community. They just shuffle between organizations. The Anthropic Network is therefore, so long as it is successful, locked into a self-amplifying feedback loop inside an ideological monoculture. And that feedback loop is winning. That's what Jacob Coxon is. China is keeping messaging tight. That is why optimism for AI is so high in China. America has Anthropic: a massive company pushing anti-AI propaganda at a state level. Anthropic will either create hysteria until American AI slows down and China wins, or it will create fractures throughout American society with severe political consequences. Ironically, because of the structural financial incentives underpinning the Anthropic Network, it has become the same kind of self-amplifying virus that it fantasizes AI to become in the future -- while hiding its tracks just as carefully. It is the mirror of the same AI virus that it hypothesizes to consume America. Anthropic's business model, models itself after the very thing it claims to fear. Except Anthropic's ideology infects humans, not computers. Congress must investigate. Evidence and Github in next post. Then some supplementary figures.
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Really simple framework for why ultimately every org will have their own “LLM” based on their data, knowledge and IP.
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Milind Mehere retweeted
My hyper-bull case is that 30% of global rides will be driven by AVs in 10 years. This will require ~120M+ cars doing ~25 rides a day ~360 days a year. It will cost ~$5T to build these cars at ~$40,000 each. 12M cars are built a year on average, but there will be a slow and steady ramp; perhaps 250k in 2027, 500k in 2028, 1m, 3m, etc (build your own model). Predict Tesla will be #1 in this space with 40% market share -- which would mean building ~50M cars in a decade. Zero to 40% market share and 5M cars a year on average is an insane ramp up -- but Tesla is the company best positioned to do this. Uber will be in second place with 35% market share, with two dozen partners putting absurdly cheap Chinese, Japanese, and purpose-built robotaxis into their fleet -- as no car maker wants to go head-to-head with Tesla building cars AND building a network. Waymo will buy a car maker in the next 12 months, creating a hybrid of Tesla and Uber: full-stack cars built in its factory, deployed on its network, and also added to Grab, Lyft, Uber, and other networks to increase utilization. The big story? Someone will need to fund FIVE TRILLION DOLLARS WORTH OF CARS 120M+ cars @ $40,000 each on average; some will be 15-20k two-seaters, some will be premium SUVS at $80,000. $7-10T+ in annual revenue is at stake for AVs alone. At a 20-40%+ gross margin, $1-2T+ in EBIDA/cash flow, we will be talking about ~20-30T in market cap. Waymo and Uber are worth ~$120B and $150B; Tesla is worth $1.4T at the moment. There's 18-28T in market cap about to be created. Please run this through your own LLMs and models... move the variables around however you like. $5 average ride, $10 average ride, $50k average vehicle cost, etc. 10% of total rides... 50% of total rides... ... however you slice it, this will be the largest market in the world for AI over the next decade. After that, human robotics will be the TAM/biggest market cap opportunity in the world... but we can do that model next.
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Milind Mehere retweeted
People who are serious about software train their own models.
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Milind Mehere retweeted
Except for love, the world is empty.
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Milind Mehere retweeted
Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis. Rationale:   A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers.    Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software.   This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time.   Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3. 
The reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead. Time will tell on both points. And likely fairly quickly. Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.
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Milind Mehere retweeted
HALO (Hard Assets, Low Obsolescence) has mostly been a public-equities story. Is the same rotation showing up in private markets?  Coined at the start of 2026, HALO has largely been discussed through energy, industrial, and logistics stocks. Less remarked on: North American private infrastructure and real estate fundraising are telling a similar story, and the data is making it visible in real time.  Explore our latest insights piece: caismarketing.com/4hcgIu9 #HALO #privatemarkets #alternativeinvestments
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Milind Mehere retweeted
Welcome to the second half of the year. The actions of hyperscalers in terms of their capital commitments will be key as the year proceeds, expect an uptick across the board, the demand is real. The flip side - the funding sources will need to be from the capital markets. The largest companies will flip to negative cash flow except for one or two. Hyperscalers have balance sheet capacity to do so (for 1-3 years), however new Frontier labs will need to go public, not sure there's more private market capital available to support their Capex needs. Of course, there's the chip guys, NVIDIA is at the party, will MU join the investment party to keep spending going? We still need visibility for when AI revenues will start to fund much of this cash need. Expect more advertising plays from LLMs, Token prices have to decline to drive Enterprise adoption. Expect LLMs to chase more vertical profit pools, legal, life science, expecting physical AI companies. Pure models will continue to see arbitrage with open source touching 30% usage, depth will create a better moat, breadth will commoditize. It's not a demand problem - "it's a monetization problem". Silicon valley has always built product with intensity and the market has funded adoption years. This time it might just be too big and the market may not have capacity to fund everyone. "Darwinian moment for AI providers?" If you are a founder, or a CEO - don't be distracted, focus on your product, how it gets better with AI. Eventually product and customer adoption will bring us to the other side, but expect a bit of a wild ride. We are still early in many PMF categories. Speed could create waste, but waiting and watching could leave us behind.
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Milind Mehere retweeted
First Southwest Airlines flight with Starlink!
The first @SouthwestAir flight with Starlink has taken off, connecting passengers and crew with reliable, high-speed Wi-Fi from gate to gate 🛰️✈️
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Milind Mehere retweeted
Good marketing is finding different ways to tell the truth.
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Milind Mehere retweeted
Elon Musk explains his 5-step algorithm for solving any problem: "The most common mistake of smart engineers is to optimize a thing that should not exist." "I have this very basic first principles algorithm that I run as a mantra." Elon breaks it down: Step 1: Question the requirements. "Make the requirements less dumb. The requirements are always dumb to some degree, no matter how smart the person who gave you those requirements. You have to start there, because otherwise you could get the perfect answer to the wrong question." Step 2: Try to delete it. "Try to delete the part or the process step entirely. If you're not forced to put back at least 10% of what you delete, you're not deleting enough. Most people feel like they've succeeded if they haven't been forced to put things back in. But actually they haven't, they've been overly conservative and left things in that shouldn't be there." Step 3: Optimize or simplify. "The most common mistake of smart engineers is to optimize a thing that should not exist. So you don't optimize until after you've tried to delete." Step 4: Speed it up. "Any given thing can be done faster than you think. But you shouldn't speed things up until you've tried to delete it and optimize it otherwise, you're speeding up something that shouldn't exist." Step 5: Automate. "And then the fifth thing is to automate it." Elon explains why the order matters: "I've gone backwards so many times where I've automated something, sped it up, simplified it, and then deleted it. I got tired of doing that. So that's why I have this mantra."
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Milind Mehere retweeted
If you want fungibility across models, you need to build the harness, context, memory, intelligence and routing in the application layer. This allows you fungibility across models and compute location. Now @matanSF you just need to sell a secure instance of this :). Congrats on this milestone.
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Milind Mehere retweeted
Make our Sun sentient to understand the Universe and extend the light of consciousness to the stars
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