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Inflectiv 2.3 is live.
Mobile everywhere. 1 GB file uploads. Global API keys. 50 marketplace categories. New LLM backend.
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Inflectiv AI ⧉
19.4K posts
Inflectiv AI ⧉
@inflectivAI
Liberating Trapped Intelligence ⧉ Fueling agents, automation, and robotics. Structured, tokenized, perpetual. https://t.co/5w82UsEIEk
discord.gg/inflectiv Katılım Nisan 2020
438 Takip Edilen44.9K Takipçiler
@ericjang11 This achievement perfectly demonstrates how quickly the cost of intelligence drops, turning a million-dollar corporate research milestone into an affordable weekend project.
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For the last few months I've been working on a from-scratch implementation of AlphaGo, a 2016 AI breakthrough that inspired me to get into deep learning. My casual understanding of AlphaGo was "search-augmented deep neural networks trained with self-play", but I wanted to go deeper and understand it by creating it.
Frontier deep learning research has always been expensive, but any given capability gets cheaper very quickly. In 2026, you no longer need DeepMind's resources to train a strong Go AI - you can vibe code all of it yourself for just a few thousand dollars of rented compute.
It was a huge honor to be invited to teach this with @dwarkesh_sp on @dwarkeshpodcast
I am an AlphaGo & Go apprentice, not a master, so all factual errors in the podcast are mine.
Web version of tutorial: evjang.com/2026/04/28/aut…
Code: github.com/ericjang/autogo
Play the go bot here: autogo.evjang.com
Dwarkesh Patel@dwarkesh_sp
New blackboard lecture w @ericjang11 He walks through how to build AlphaGo from scratch, but with modern AI tools. Sometimes you understand the future better by stepping backward. AlphaGo is still the cleanest worked example of the primitives of intelligence: search, learning from experience, and self-play. You have to go back to 2017 to get insight into how the more general AIs of the future might learn. Once he explained how AlphaGo works, it gave us the context to have a discussion about how RL works in LLMs and how it could work better – naive policy gradient RL has to figure out which of the 100k+ tokens in your trajectory actually got you the right answer, while AlphaGo’s MCTS suggests a strictly better action every single move, giving you a training target that sidesteps the credit assignment problem. The way humans learn is surely closer to the second. Eric also kickstarted an Autoresearch loop on his project. And it was very interesting to discuss which parts of AI research LLMs can already automate pretty well (implementing and running experiments, optimizing hyperparameters) and which they still struggle with (choosing the right question to investigate next, escaping research dead ends). Informative to all the recent discussion about when we should expect an intelligence explosion, and what it would look like from the inside. Timestamps: 0:00:00 – Basics of Go 0:08:06 – Monte Carlo Tree Search 0:31:53 – What the neural network does 1:00:22 – Self-play 1:25:27 – Alternative RL approaches 1:45:36 – Why doesn’t MCTS work for LLMs 2:00:58 – Off-policy training 2:11:51 – RL is even more information inefficient than you thought 2:22:05 – Automated AI researchers
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@L0m3z Ultimately, the future of media production belongs to studios that master the intersection of high-performance infrastructure and classic narrative structure.
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The first big company with big money and an eye for talent who does this, and lets their creatives go wild with AI tools––which will shortcut many existing production bottlenecks, and which I now believe enhances rather than degrades genuine human talent––is going to win.
joshua steinman (🇺🇸,🇺🇸)@JoshuaSteinman
It’s time for X Studios. A full scope entertainment company using cutting edge technology to create full length features for X and elsewhere. I hear @AmandaMilius is available.
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@MariaBartiromo @MorningsMaria @FoxBusiness I see this intersection of nuclear tech and computing as a fundamental shift in how the macroeconomic value of AI is calculated. The winners of the race will not just be those with the best algorithms, but those who successfully secure long-term energy autonomy.
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There's a 'huge momentum' around data centers and AI, Nano Nuclear founder says | video.foxbusiness.com/v/6395686846112 @MorningsMaria @FoxBusiness
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@bindureddy The US ecosystem is less centralized, but it benefits from strong frontier labs, chips, cloud infrastructure, and enterprise distribution. Competition is less about complaints and more about layered capability stacks.
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China is building pragmatic open-source AI models and no one can really stop them....
These models can already handle 50% of every day tasks effectively and are 30x cheaper to run
In a few months, they will be able to handle most professional tasks - you don't need a 10T param model to automate work
The US should stop complaining and catch up
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@JKeynesAlpha The biggest upside driver would be Tempus becoming a default data layer for healthcare AI models. If that happens, its value could shift from “healthcare tech company” to “critical AI infrastructure asset.”
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$TEM The Market Still Misunderstands Tempus AI’s Role in the Frontier AI Era
Tempus AI is becoming one of the clearest examples in the market of a "stock lagging the company."
The share price has declined from highs and chopped around. The business keeps stacking material progress. Revenue growth remains strong. Diagnostics volumes are growing. Data & Applications is accelerating. Major pharma collaborations keep expanding. Tempus is building one of the most important proprietary AI moats in healthcare, and I think the market is still struggling to classify what it owns here.
The stock gets treated like a long-duration software name, healthcare growth name, or generic AI application company at a time when the market prefers semis, power, data centers, and obvious AI capex beneficiaries. That valuation bucket has worked against $TEM.
Tempus looks far more like proprietary healthcare intelligence infrastructure than a conventional SaaS company.
Its value is rooted in a scarce asset: longitudinal, multimodal, clinically integrated healthcare data that is extraordinarily difficult to reproduce. That data is paired with proprietary AI models, diagnostic algorithms, sequencing infrastructure, physician workflow integration, and life sciences tooling. The rise of frontier AI may increase the strategic value of Tempus’ data moat.
Eric Lefkofsky @lefkofsky made the point directly at the recent Morgan Stanley Technology, Media & Telecom Conference on March 3rd:
“But I think there’s pretty good consensus in like ’27, ’28, they’re hitting the ends of that. So more and more of those companies are coming to people like us saying, what data do you have? And I think the next frontier of fun is going to be the big frontier modelers trying to garner access to more and more proprietary data like the kind of data Tempus has to train their models. In our case, the data we have is really hard to replicate. First, you have to go to, in our case, I think, 5,500 of the roughly 8,000 hospitals in the United States and convince them they should give you their data, which is not quick.”
That quote is central to the thesis.
Frontier model companies like Anthropic, OpenAI, and Google Gemini, are increasingly going to need scarce, proprietary, domain-specific datasets as public training data becomes less useful at the frontier. Tempus owns one of the most valuable and hardest-to-replicate healthcare datasets in the world. Lefkofsky goes on to explain the process: legal approvals, hospital IT bottlenecks, systems integration, longitudinal patient data, structured data, unstructured data, physician progress notes, and other forms of clinical information that have to be assembled over time.
This is why the SaaS comparison misses the heart of the business. Tempus looks far more like proprietary healthcare intelligence infrastructure than a conventional software company. It monetizes data, diagnostics, clinical workflows, AI models, and diagnostic algorithms built on top of a healthcare dataset that took years of institutional integration to assemble.
I also would not be surprised if $GOOG Google/Alphabet, which still reported 1.55M $TEM shares worth roughly $70.1M in its latest 13F portfolio, eventually announced some kind of Gemini-Tempus partnership around healthcare AI and proprietary multimodal data. This is purely my speculation. The logic is easy to see from Lefkofsky’s comments. Frontier model companies increasingly need scarce, domain-specific proprietary datasets, and Tempus owns one of the most valuable healthcare data assets in the world. Alphabet’s continued Tempus position makes that possibility especially interesting to watch.
The financials are beginning to show the power of that structure.
Diagnostics generated $261.1M in Q1 2026 revenue, up 34.7% YoY, driven by 28% oncology volume growth and 54% hereditary volume growth. This is the clinical engine. It keeps Tempus embedded in real patient care, physician decision-making, molecular testing, and healthcare workflow.
Data & Applications generated $87.0M, up 40.5% YoY, with Insights growing 44.1% YoY. This is the higher-value AI, data licensing, modeling, and biopharma enablement layer. It shows Tempus evolving into a platform company with multiple compounding engines rather than a single-product diagnostics story.
The flywheel is getting harder to ignore:
• Diagnostics deepen the proprietary data asset
• The data asset improves AI models and diagnostic algorithms
• Those models and datasets support biopharma R&D
• The partnerships generate more revenue, strategic validation, and product pull-through
• More clinical activity strengthens the entire system
The 2026 news flow has been excellent:
• Q1 revenue rose 36.1% YoY to $348.1M, with gross profit up 43.1% to $222.0M
• Tempus raised 2026 revenue guidance to $1.59B-$1.60B and maintained an expectation of approximately $65M in adjusted EBITDA
• Merck expanded a multi-year strategic collaboration with Tempus focused on AI/ML-driven precision medicine, biomarker discovery, cancer resistance mechanisms, and oncology development
• Gilead expanded its multi-year collaboration with Tempus, including enterprise-wide access to its AI-driven Lens platform and broader multimodal datasets to support oncology R&D and real-world evidence generation
• Daiichi Sankyo entered a strategic oncology collaboration using Tempus’ proprietary foundation models, including PRISM2, to support biomarker discovery and patient stratification across an ADC clinical program
• Bristol Myers Squibb expanded its strategic collaboration with Tempus to apply AI, multimodal real-world data, and data science techniques across oncology and neuroscience clinical development programs
• USC and Tempus announced a strategic collaboration aimed at accelerating AI-driven precision medicine across patient care and research, with the USC system representing more than 1.5M annual patient visits
• NYU Langone Health entered a multi-year strategic collaboration centered on precision oncology, serial molecular profiling, and the development of AI-powered diagnostic tools and personalized therapies
• Northwestern Medicine selected Tempus to expand genomic testing access for oncology patients, including DNA and RNA profiling, liquid biopsy, MRD, and broader next-generation sequencing access
• Tempus launched automated Active Follow-Up, an AI-enabled clinical update service designed to provide ongoing therapy monitoring and context-aware notifications through its physician portal
• Tempus reported Total Remaining Contract Value above $1.1B at year-end 2025 and 126% net revenue retention. It also disclosed that it signed data agreements with 70+ customers during 2025, including $AZN AstraZeneca, $GSK GSK, $BMY Bristol Myers Squibb, $PFE Pfizer, $NVS Novartis, $MRK Merck, $ABBV AbbVie, Daiichi Sankyo, $LLY Eli Lilly, and Boehringer Ingelheim
That pharma list matters.
These are some of the largest pharma and drug developers in the world. They are increasingly using Tempus’ multimodal datasets, AI-enabled analytics, and platform capabilities for biomarker discovery, drug development, clinical trial design, patient stratification, and real-world evidence.
I think the market is still underappreciating what $TEM is becoming.
Diagnostics is growing. Data & Applications is growing faster. Insights is compounding at a high rate. Major pharma relationships are broadening. Proprietary models and diagnostic algorithms are expanding. The dataset keeps getting larger and more valuable. Frontier AI companies themselves are beginning to recognize how important proprietary data assets like Tempus’ could become.
The price action has been frustrating. The thesis has kept improving.
At some point the rotation changes. Long-duration AI platforms regain attention. Healthcare AI gets reassessed. Investors begin to understand that Tempus belongs in a different strategic category from SaaS or legacy diagnostic names.
$TEM is building proprietary healthcare intelligence infrastructure for the AI era, and I think the market will eventually pay much more attention to that reality.
I have no idea when the rotation will hit, but I'm positioned and adding to my position until it does.
J Keynes@JKeynesAlpha
$TEM Tempus AI again expands AI collaboration with $BMY and has 80% of big pharma locked up with AI/data contracts and the market is like 💤💤💤💤🥱🥱🥱🥱 “I only care about semis”
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@appleinsider @OliverJHaslam The skepticism is fair because both Apple and Google are now competing in a space where expectations have already been set by faster-moving AI-native companies. Delivery speed will matter more than announcements.
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Gemini Intelligence has been announced just a month before Apple Intelligence's big moment at WWDC, but I have doubts about whether either company can deliver on its AI promises.
#Apple #AI By @oliverjhaslam
appleinsider.com/articles/26/05…
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@Public_Citizen I see this spending pattern as a calculated move by the tech sector to secure a favorable legislative pause against strict regulations. By backing friendly candidates, they are building a political moat that protects their market dominance from genuine oversight.
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Big Tech isn’t just building AI; it’s trying to buy congress.
AI companies have already poured over $185 MILLION to boost candidates who are friendly to the industry.
Corporate interest shouldn't come before public safety.
It's time to end Citizens United.
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@kevincodex This massive throughput via the MiMo architecture demonstrates that open models are becoming highly competitive with closed alternatives. By subsidizing this level of access, Xiaomi is effectively accelerating the adoption of decentralized agent workflows globally.
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@nvidia The ability to analyze images and sound directly from a smartphone transforms personal hardware into a continuous health network. This shift from reactive treatment to proactive prevention can neutralize the immense economic burdens of illness.
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In remote Australia, the nearest doctor can be hundreds of miles away. In the Catalyst series, we meet visionaries tackling the world's biggest challenges. See how Helfieis leveraging Microsoft Azure and NVIDIA to bridge that gap with AI-driven health monitoring, bringing preventive care to distant communities.
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@ArtificialAnlys Comparing the slide outputs from last year to now highlights how quickly the visual and structural quality of AI work is evolving. It shows that today's frontier models are mastering presentation layout and inventory logic rather than just generating basic text.
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AI is making rapid progress in economically valuable tasks: based on their GDPval-AA Elo scores, GPT-5.5 is expected to win ~98% of head-to-head comparisons on realistic work outputs against Claude 4 Sonnet, the leading model in GDPval-AA a year ago
GDPval-AA measures how well models complete tasks across nine industries and 44 occupations. The graphic shows slide outputs for an Inventory Management task from Claude 4 Sonnet (May 2025) against GPT-5.5 (xhigh, May 2026).
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@TrungTPhan The involvement of celebrity figures such as Thompson and Shaq in tech investments brings attention to startups and can influence public perception, potentially attracting further investments and support.
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Tristan Thompson investing early in Anthropic after having dinner with some employees is the AI age version Shaq getting into the Google Series A round in 1999 — 5 years before IPO — after overhearing two Google employees chatting at the Beverly Hills Hotel lobby.
Market Bubble@MarketBubble
Tristan Thompson explains how he invested in Anthropic “It was a buddy of mine in Silicon Valley… just going to a couple of these dinners they were doing with the NBA, sitting there talking to these guys and really picking their brains” “They were telling me about the future of AI and technology… I was like hey man, I like what you’re talking about” “It was a bunch of SF guys and everyone kept telling me, so I was like alright man, here’s a check. I wanna get in the game” “From there they just opened up so much for me. I was able to get in early through SPVs and bring in my other buddies to make a bigger check”
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@gregisenberg The suggestion that agents could watch and learn a user’s job over a week to replicate it raises the potential for high automation levels and efficiency in the future workplace.
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More AI agent observations below (I keep adding to the list):
1. Hermes agents write to their own memory after every task. Which means starting today versus starting in 6 months is an unfair advantage for you.
2. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now
3. The real reason local models matter for founders: you can ship a product where the AI runs entirely on the customer's device and you never touch their data. Zero privacy concerns. Zero server costs. Zero compliance headaches.
That changes which industries you can sell to overnight. Healthcare, legal, finance, all the regulated verticals that won't send data to the cloud just opened up.
4. Every company needs to be rebuilt as a "second brain" before agents can be useful. That means every process, every decision, every piece of institutional knowledge has to exist in a format an agent can read. Most companies have none of this.
5. Agent costs are the new headcount. Won't be crazy for companies to spend 50%+ of their total headcount cost on tokens.
6. Agents are accidentally creating internal competition at companies. The marketing agent and the sales agent are optimizing for different metrics and working against each other without anyone realizing it. It took humans decades to develop cross-functional alignment. Nobody thought about it for agents.
7. The YAML config file is becoming the new org chart. Who reports to who, what permissions they have, what tools they access, all defined in a config file. The company's structure is literally a file you can version control, fork, and deploy. That's new.
8. The first agents that can smell a scam are going to be worth billions. Right now agents will happily wire money to a fake invoice because it matched the format. The trust layer is completely missing.
9. We're about to find out that most "expertise" was actually just memory. Knowing the tax code. Knowing the case law. Knowing which supplier charges what. When an agent holds all of that in context, the expert's value shifts from "I know things" to "I know which things matter." Much smaller group of people.
10. We're all running the same models. The differentiation is in what you feed them. Two founders with the same agent, same model, same tools will get wildly different results based purely on the quality of their knowledge base. Garbage context in, garbage output out. Forever.
11. The most underbuilt category in AI right now: agents for old people. 70 million boomers who need help with medical forms, insurance claims, and appointment scheduling.
12. Agent latency is the new page load speed. If your agent takes 45 seconds to respond, your customer already switched to one that takes
13. Skills files are the new apps. A SKILL.md that tells an agent how to do one thing well is more valuable than a SaaS subscription that does the same thing behind a login screen.
14. AI hardware... how do you create devices that are good businesses that people want? It'll be a $30 dongle you plug into existing dumb devices to give them an agent brain. Smart toaster doesn't need to be built from scratch. It needs a $30 brain attached to a $15 toaster.
15. Your agent can read faster than you can think. The bottleneck in every agent workflow is now the human approval step. We're the slow part. That's a strange thing to sit with.
16. Agents made the 80/20 rule violent. The 20% of work that matters is now the only work humans do. The 80% just disappeared. Entire job descriptions were hiding inside that 80%.
17. The thing I keep coming back to: the best businesses right now are being built by people who are just slightly ahead of their customers. Not 10 years ahead. 6 months ahead. That's the sweet spot. Far enough to lead. Close enough to be understood.
GREG ISENBERG@gregisenberg
My 30+ observations on the greatest opportunities in AI agents right now: And some ideas that are keeping me up at night. 1. The new buyer on the internet is an AI agent. Imagine billions of new customers showing up with money to spend but they only shop via MCP. That's what's happening. No MCP server means you're invisible to the fastest growing buyer on the internet. 2. Every franchise system in America (30,000+) needs an agent layer and none of them have one. One founder per franchise vertical. That's 30,000 businesses waiting. 3. Everyone said "distribution is the only moat" a year ago. Now I'd add that the only moat is distribution plus memory. The company that has your audience AND your agent's accumulated context is impossible to leave. 4. Consumer mobile is more interesting than it's been since 2012. Apps can finally DO things for you instead of showing you things. The next wave of $100M apps are being built right now. 5. The most interesting startup nobody has built is an agent marketplace where you rent access to someone else's trained agent. A recruiter spent 6 months training a sourcing agent on healthcare hiring. That agent is worth renting to every other healthcare recruiter on earth. The agent itself becomes the product. 6. A sorta strange phenomenon that's happening right now is agents are developing preferences. Give the same agent the same task 100 times and it starts developing patterns in how it approaches it. Nobody is studying this yet. But the agents that develop good patterns are worth more than the ones that don't. That's a new kind of asset. 7. Dead internet theory is about to become dead SaaS theory. Half the apps you use will quietly replace their support team, their onboarding team, and their content team with agents. You won't notice for months. Then you'll realize you haven't talked to a human at that company in a year. 8. The most valuable data in the world right now is sitting in the support tickets of small or mid tier SaaS companies. Every ticket is a customer telling you exactly what to build next. Mine this. 9. The most interesting pricing problem nobody has solved is how do you price a product when your costs change every time OpenAI or Anthropic updates their model pricing? Your margins can swing 40% overnight based on a decision made in San Francisco. The company that builds dynamic pricing infrastructure for agent-based businesses solves a problem every AI company has. 10. The best AI products feel like they're reading your mind. The worst ones feel like filling out a form with extra steps. 11. An interesting arbitrage I've noticed lately is hiring a human VA for $20/hour to supervise an AI agent that does $200/hour work. The human just checks the output. 12. The managed AI agent business is becoming the new agency model. $5k/month per client. You build it, run it, maintain it. The client gets a digital employee they never have to think about. This will be a $50 B+ category. 13. The first "shadow agent" scandals are about to drop. Employees running personal agents on company infrastructure without telling anyone. Using company API keys. Agents accessing internal docs. IT departments have little visibility into this right now. Lots of opportunity to build companies here. Definitely a painkiller not a vitamin type of business. 14. Right now there are probably millions of agents running on autopilot that their creators forgot about. Still burning tokens. Still sending emails. Still scraping websites. Still costing money. The "find and kill your zombie agents" tool is a product that writes itself. 15. Companies are starting to hire based on someone's agent portfolio instead of their resume. "Show me 3 agents you built that are running right now." It's REALLY early but it's starting. 16. Your Slack archive is a product. Every company's internal Slack has thousands of messages explaining how they actually do things. The company that lets you point an agent at your Slack history and auto-generate SOPs and agents from it will be enormous. 17. We're watching the cost of intelligence fall faster than the cost of distribution. Which means distribution is now the expensive thing. 18. The most underrated asset a human can have in 2026: the ability to sit in a room with another human, make eye contact, and have a real conversation. As AI handles more of the transactional stuff, the humans who can do the relational stuff become disproportionately valuable. The soft skills people used to dismiss as fluffy are becoming the hard skills. The hard skills people spent decades acquiring are becoming the soft ones. 19. There are MANY huge companies to be built around the fact that most people's agents are running on their personal laptops which they also use to browse the internet, check email, and download random files. The attack surface is enormous. One compromised Chrome extension and your agent's API keys, customer data, and workflows are exposed. 20. There's a new type of burnout forming that doesn't have a name. It's not from working too hard. It's from context switching between human work and agent work 50 times a day. Reviewing agent output, correcting it, approving it, reviewing again. The mental load of supervising agents is different from the mental load of doing the work yourself. Some founders are telling me they were less tired when they did everything manually because at least the cognitive pattern was consistent. 21. The cheapest form of market research: search "[your industry] spreadsheet template" on Google. Whatever people are tracking manually is your product. 22. Half the YC companies pivoted within 8 weeks of demo day. Not because they failed. Because agents let them test 5 ideas in the time it used to take to test one. The concept of "committing to an idea" is dissolving. Serial pivoting is becoming the default because 1) AI lets you move fast 2) the world is moving fast. 23. The loneliest job in tech right now is being the only person at your company who understands what the agents are doing. You can't explain it to your boss. You can't hand it off to a colleague. If you leave, everything breaks. You've become a single point of failure for an entire automated system. That person needs a title, a team, and a backup plan. Most companies haven't figured this out yet. 24. Your browser history is the most valuable training data you own and you're giving it away for free. Every site you visit, every product you research, every competitor you study, every pricing page you screenshot. That behavioral data, structured and fed to an agent, would make it understand your business better than any onboarding call. The company that lets you turn your browser history into agent context builds something nobody can replicate. 25. Everyone is building AI wrappers. Nobody is building AI unwrappers. The tool that takes an AI-generated document and tells you which parts a human wrote and which parts were generated. 26. Stripe just became the most important company in the agent economy and they barely had to do anything. Every agent that sells something needs Stripe. Every agent that buys something needs Stripe. They're the payment rail for the entire agentic internet by default. 27. The most undervalued API in the world right now is the US Postal Service address verification API. It's practically free. Every local business lead gen agent needs it. Every real estate agent needs it. Every direct mail agent needs it. Boring government infrastructure is quietly becoming the backbone of agent-native businesses. 28. The concept of "business hours" is for humans. Your agent closed a deal in Tokyo at 3am, processed the payment, sent the onboarding email, and updated the CRM before your alarm went off. 29. What happens when agents start recommending other agents? Your research agent finds that a competitor's sales agent is better and suggests you switch. Agent referral networks are forming organically. The first agent affiliate program is probably 6 months away. 30. Cal dotcom closed their source code. That's the canary. When open source companies start closing up, it means agents were cloning their product too easily. Every open source company is quietly asking the same question right now. 31. "AI for pet groomers" sounds like a joke and that's exactly why it will work. 150,000 of them in America. Zero tech. All scheduling by phone or IG DMs. The joke ideas always win. 32. The thing that will seem most obvious in hindsight: we spent 2025-2026 arguing about which model is best while the entire value was in the orchestration layer. The model is the CPU. Nobody buys a computer based on the CPU anymore. They buy it based on what they can do with it. Makes so much sense in hindsight. What else will be obvious in hindsight? I'll share more notes soon. I can't sleep with all that's going on. Maybe you too. What an incredible time to be building.
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@ResisttheMS Such exchanges can influence public perception of both the media and political leaders. Claims of "fake news" contribute to a polarized environment where trust in journalistic sources may diminish.
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President Trump absolutely went off at a BBC reporter.
Reporter: “Were you able to confirm yet that it was a US missile [that hit the school in Iran]?"
Trump: "Who are you with?"
Reporter: "BBC…"
Trump: "Fake BBC. You mean the ones that put AI in my mouth. The ones that gave me, that had me saying a statement that they now admit was not true? The ones that put terrible words in my mouth and then had to admit that it was fake? The ones that are now being sued for $5B… They’re another fake outfit."
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@durov The integration of AI tools by participants could lead to innovative solutions, demonstrating the potential of AI as a valuable asset in engineering and blockchain development.
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$100,400 just went to the 15 winners of the TON Consensus Challenge.
The smartest engineers on the planet — some armed with loyal AI sidekicks — helped us maximize the security of the new consensus mechanism.
Telegram contests are the only path to joining our engineering team.
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@TechCrunch This report proves that the ultimate limit on AI scaling might not be algorithm design or silicon availability, but simple electricity. When local markets are disrupted this heavily, the entire macroeconomic framework behind computing power has to be fundamentally reevaluated.
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Silicon Valley’s vacationland needs a new energy provider just as AI is driving prices up techcrunch.com/2026/05/15/sil…
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@Reuters The quiet drain of talent to competitors suggests that the internal damage might last long after the strike ends. If the company cannot resolve these compensation disparities quickly, its long-term goal of leading the chip market will be heavily compromised.
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Exclusive: At Samsung, the global AI boom spurred a looming strike and deep divisions reut.rs/4nxs7WN reut.rs/4nxs7WN
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@lennysan I agree that the six-month reorg cycle highlights a fundamental human limit on adapting to structural change, regardless of how fast the code is shipping. Even in hypergrowth AI companies, organizational architecture simply takes time to break and be rebuilt.
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I'm actually surprised how rarely these big reorgs at top AI companies happen, given their pace. They seem to happen every ~6 months, which is about the same rate as traditional hypergrowth companies.
Maybe this is the limit on how much humans can deal with such change. Or, how long it takes to figure out something is broken, no matter your shipping pace.
Max Zeff@ZeffMax
Scoop: OpenAI announced another major reorg on Friday, as part of its effort to unify ChatGPT and Codex. -Greg Brockman is officially taking over OpenAI's products, after previously being tapped as an interim leader -Head of Codex, Thibault Sottiaux, is now leading core product and platform -Head of ChatGPT, Nick Turley, is now also going to work on revamping enterprise products
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@MarioNawfal It is fascinating to see how the concept of Sovereign AI is transitioning into a tool for complete economic autonomy. This systemic injection of AI across all sectors proves that Beijing views the technology as core infrastructure.
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🚨🇺🇸🇨🇳 China is no longer interested in American silicon, they are building a "Sovereign AI" future that makes Western sanctions irrelevant.
Defense analyst Pravin Sawhney explained how Beijing is injecting artificial intelligence into every facet of its administration and economy to achieve total self-reliance by 2030.
@PravinSawhney
Mario Nawfal@MarioNawfal
🚨🇺🇸🇮🇷 The deadlock at the Strait of Hormuz won't be broken by military threats, but by the diplomacy that Washington currently lacks. Defense analyst Pravin Sawhney suggested that the U.S. walking out of talks in Islamabad was a massive setback for securing peace in the foreseeable future. Pravin said that until there is a face-to-face dialogue regarding the blockade and shipping fees, the risk of a global catastrophe remains at an all-time high. @PravinSawhney
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@EpochAIResearch This data clearly confirms that model optimization involves serious trade-offs between logic domains. A higher score in software engineering often seems to come at the expense of pure mathematical processing capabilities.
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Claude is typically better at software engineering and worse at math than frontier competitors.
Aggregating benchmarks to create our domain-specific ECI, we find the Claude family has an average SWE-ECI 2.7 points higher than their general ECI, and a Math-ECI 1.8 points lower.
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@Alfred_Lin I agree that open source is now a corporate weapon used to neutralize incumbents. The shift from a development philosophy to a strategic moat is a reality we see today.
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Very thoughtful post on the role and success of open source, including potential applications for AVs and AI. Long, but worth the read.
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"Open source is no longer just how good software gets built. It is how dominant incumbents get neutralized, how trillion-dollar industries shift their power structure, and how the next generation of strategic moats gets dug — by the companies smart enough to dig them in the open. The world’s most sophisticated technology companies have spent fifteen years quietly mastering this. Most of the world is still treating open source as a development philosophy when it has long since become a corporate weapon. That gap in understanding is itself a form of structural disadvantage.
[...] A new world order in technology is being constructed in real time, and the role open source plays in that order is being decided right now — partly in foundation board rooms, partly in earnings calls, partly in congressional hearings, partly in policy white papers being written by lobbyists for the largest closed AI companies in the world. The companies that understand this will compound their advantages over the next decade. The countries that understand this will lead the global technology landscape. The individuals who understand this will be impossible to outmaneuver."
Bill Gurley@bgurley
A new @bgurley blog post! I have been thinking about how sophisticated executives are using open source in super creative ways. Started writing this three years ago. Excited to finish it up and publish it! And with the new @p3institute brand. substack.com/home/post/p-19…
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