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cursor just made every $200/month copilot subscription look like a scam dropped today with their own coding model what took 8 hours of manual coding now takes 30 seconds and it runs 8 versions of itself in parallel to pick the best solution while github's charging $20/month for autocomplete, cursor built an entire autonomous dev team composer model: → 4x faster than gpt/claude → completes full features in <30 seconds → tests its own code automatically → built with reinforcement learning on real codebases here's what's actually wild: most companies paying $150k/year for junior devs to do work this does for $20/month just saved a client $253k/month migrating their dev work to cursor's multi-agent system the intelligence gap between "we hired 3 developers" and "we deployed cursor" is getting stupid comment "COMPOSER" and ill send the full breakdown of how to replace your dev costs with this

cursor just made every $200/month copilot subscription look like a scam dropped today with their own coding model what took 8 hours of manual coding now takes 30 seconds and it runs 8 versions of itself in parallel to pick the best solution while github's charging $20/month for autocomplete, cursor built an entire autonomous dev team composer model: → 4x faster than gpt/claude → completes full features in <30 seconds → tests its own code automatically → built with reinforcement learning on real codebases here's what's actually wild: most companies paying $150k/year for junior devs to do work this does for $20/month just saved a client $253k/month migrating their dev work to cursor's multi-agent system the intelligence gap between "we hired 3 developers" and "we deployed cursor" is getting stupid comment "COMPOSER" and ill send the full breakdown of how to replace your dev costs with this

Nozz

240,370 просмотров • 9 месяцев назад

Ramsey Sahyoun is the Co-Founder of Evergreen, one of America’s most interesting acquisition machines. He and Jeff Totten started the firm when they were 25 and 27 years old. Today, they’ve acquired over 160 companies and completed 47 acquisitions in 2025 alone. The portfolio does $1.5 billion in sales and $250 million in EBITDA. We discuss: - Why they chose MSPs - The lessons from their first acquisition - What went wrong with an early roll-up attempt - How they built a proprietary sourcing engine - Why 80% of their deals are proprietary - How talent, goal setting, and value creation became central to Evergreen’s operating system Enjoy! Timestamps: 0:00 Evergreen’s scale and long-term hold model 2:06 Discovering private equity and buying private companies 4:14 Meeting Jeff Totten at Alpine Investors 5:53 First acquisition and current portfolio 10:23 Leaving Alpine and starting young 12:18 The first 6-18 months after closing 13:15 What went wrong with an early MSP roll-up 18:44 Building Evergreen’s sourcing engine 23:07 The value of having a large acquisition database 26:09 How to build trust with business owners 34:02 Evergreen’s M&A, talent, and playbook flywheel 41:54 Lessons from 160 acquisition post-mortems 44:22 Setting big goals and planning backward 47:16 One-page plan and quarterly renewals 48:53 What Evergreen learned from Alpine and Graham Weaver 51:27 How Ramsey and Jeff’s roles changed as Evergreen scaled 54:21 What people misunderstand about Evergreen 56:48 Closing thoughts from Ramsey

Ramsey Sahyoun is the Co-Founder of Evergreen, one of America’s most interesting acquisition machines. He and Jeff Totten started the firm when they were 25 and 27 years old. Today, they’ve acquired over 160 companies and completed 47 acquisitions in 2025 alone. The portfolio does $1.5 billion in sales and $250 million in EBITDA. We discuss: - Why they chose MSPs - The lessons from their first acquisition - What went wrong with an early roll-up attempt - How they built a proprietary sourcing engine - Why 80% of their deals are proprietary - How talent, goal setting, and value creation became central to Evergreen’s operating system Enjoy! Timestamps: 0:00 Evergreen’s scale and long-term hold model 2:06 Discovering private equity and buying private companies 4:14 Meeting Jeff Totten at Alpine Investors 5:53 First acquisition and current portfolio 10:23 Leaving Alpine and starting young 12:18 The first 6-18 months after closing 13:15 What went wrong with an early MSP roll-up 18:44 Building Evergreen’s sourcing engine 23:07 The value of having a large acquisition database 26:09 How to build trust with business owners 34:02 Evergreen’s M&A, talent, and playbook flywheel 41:54 Lessons from 160 acquisition post-mortems 44:22 Setting big goals and planning backward 47:16 One-page plan and quarterly renewals 48:53 What Evergreen learned from Alpine and Graham Weaver 51:27 How Ramsey and Jeff’s roles changed as Evergreen scaled 54:21 What people misunderstand about Evergreen 56:48 Closing thoughts from Ramsey

PrivateEquityGuy (Mikk Markus)

226,463 просмотров • 3 месяцев назад

I turned a single renovation photo into a cinematic time-lapse video for $10 of credits. These videos are crushing on social media because humans naturally want to see what the construction turns into. The dwell time is nuts. So... now we're using these videos to help promote real estate development and landscaping companies. Here's how it works: → Drop one finished renovation image into a customGPT that reverse-engineers the full construction sequence → Calico AI generates each stage of the renovation with consistent aerial camera angles → Kling 3.0 animates the timelapse transitions between frames with realistic construction movement → ElevenLabs generates a matching soundtrack → Stitch in CapCut and export The result: real estate developers and landscaping companies get portfolio-quality timelapse content from a single photo. No production team. Landscaping firms can use these for ads, social content, client pitches, and website videos that actually convert. Comment "RENOVATION" and I'll send you the exact system, custom GPT, and all prompts (must be following so I can DM you!).

I turned a single renovation photo into a cinematic time-lapse video for $10 of credits. These videos are crushing on social media because humans naturally want to see what the construction turns into. The dwell time is nuts. So... now we're using these videos to help promote real estate development and landscaping companies. Here's how it works: → Drop one finished renovation image into a customGPT that reverse-engineers the full construction sequence → Calico AI generates each stage of the renovation with consistent aerial camera angles → Kling 3.0 animates the timelapse transitions between frames with realistic construction movement → ElevenLabs generates a matching soundtrack → Stitch in CapCut and export The result: real estate developers and landscaping companies get portfolio-quality timelapse content from a single photo. No production team. Landscaping firms can use these for ads, social content, client pitches, and website videos that actually convert. Comment "RENOVATION" and I'll send you the exact system, custom GPT, and all prompts (must be following so I can DM you!).

David Roberts

64,411 просмотров • 5 месяцев назад

R.I.P. Fable 5. Perplexity + Reddit + Opus 4.8 = AI SDR that still booked a $20B hedge fund. (38% reply rate. 30 qualified meetings a month. 0 SDRs hired.) This system reads 1,000+ companies every morning and only writes to the ones already in buying motion... → No more $200K/year SDR payroll for manual account research → No more 10,000 cold emails for a 1% reply → No more bought lists going stale in weeks → No more guessing who's actually ready to buy → No more openers that read like templates Just 1 morning run → the 30 accounts worth calling today. I built it around one rule: never depend on a single model to run. Opus 4.8, GPT 5.5, Mythos, Fable 5 — there will always be a new LLM. Today the US government proved why. The system didn't notice. Here's how it works: → ICP Intelligence (Perplexity does a month of buyer research in 15 minutes) → Account Radar (reads 1,000+ companies every morning, keeps only the matches) → Buying Window (3 live triggers per account: a raise, a key hire, a launch) → Buyer Language (lifts your buyer's exact 5 phrases off Reddit, nothing reads like a template) → Voice Match (drafts a 3-line opener off that signal, in your voice) → Orchestration (Fable runs the whole pass and hands you the 30 to call today) Built on real outbound infrastructure. Model-agnostic. Runs every morning without supervision. Government bans included. Results from my own pipeline: - Reply rate from 1% to 38% - 30 qualified meetings a month, 0 SDRs hired - A $20B hedge fund and a $15B fund booked - $340K in new pipeline last quarter Want the complete build? Like + comment "RECON" + repost, and I'll DM it to you. (must be following)

R.I.P. Fable 5. Perplexity + Reddit + Opus 4.8 = AI SDR that still booked a $20B hedge fund. (38% reply rate. 30 qualified meetings a month. 0 SDRs hired.) This system reads 1,000+ companies every morning and only writes to the ones already in buying motion... → No more $200K/year SDR payroll for manual account research → No more 10,000 cold emails for a 1% reply → No more bought lists going stale in weeks → No more guessing who's actually ready to buy → No more openers that read like templates Just 1 morning run → the 30 accounts worth calling today. I built it around one rule: never depend on a single model to run. Opus 4.8, GPT 5.5, Mythos, Fable 5 — there will always be a new LLM. Today the US government proved why. The system didn't notice. Here's how it works: → ICP Intelligence (Perplexity does a month of buyer research in 15 minutes) → Account Radar (reads 1,000+ companies every morning, keeps only the matches) → Buying Window (3 live triggers per account: a raise, a key hire, a launch) → Buyer Language (lifts your buyer's exact 5 phrases off Reddit, nothing reads like a template) → Voice Match (drafts a 3-line opener off that signal, in your voice) → Orchestration (Fable runs the whole pass and hands you the 30 to call today) Built on real outbound infrastructure. Model-agnostic. Runs every morning without supervision. Government bans included. Results from my own pipeline: - Reply rate from 1% to 38% - 30 qualified meetings a month, 0 SDRs hired - A $20B hedge fund and a $15B fund booked - $340K in new pipeline last quarter Want the complete build? Like + comment "RECON" + repost, and I'll DM it to you. (must be following)

Aryan Mahajan

31,355 просмотров • 2 месяцев назад

The head of Ofgem, who earns an annual salary of over £300,000. gives some helpful tips on how people can reduce their bills. "Shop around, change your payment method and try to use less." The head of Ofgem says customers "may" be able to save around £140 a year by switching the way they pay for energy. Then comes the obvious question: what about the energy companies making enormous profits while households are being hammered? His response is telling. Ofgem caps the profits of the suppliers it regulates through the price cap, he says. But the really big profits are being made by the oil and gas companies extracting energy from the ground. And dealing with those profits? That, apparently, is for government and the tax system. So there we have it. The public are told to manage their direct debits and ration their energy use, while the companies making fortunes from a basic necessity, our country's own resources, are treated as a matter for somebody else. The system is not broken. It is doing exactly what it was built to do: protect the market first and leave ordinary people to carry the cost. #energyefficiency #Ofgem

The head of Ofgem, who earns an annual salary of over £300,000. gives some helpful tips on how people can reduce their bills. "Shop around, change your payment method and try to use less." The head of Ofgem says customers "may" be able to save around £140 a year by switching the way they pay for energy. Then comes the obvious question: what about the energy companies making enormous profits while households are being hammered? His response is telling. Ofgem caps the profits of the suppliers it regulates through the price cap, he says. But the really big profits are being made by the oil and gas companies extracting energy from the ground. And dealing with those profits? That, apparently, is for government and the tax system. So there we have it. The public are told to manage their direct debits and ration their energy use, while the companies making fortunes from a basic necessity, our country's own resources, are treated as a matter for somebody else. The system is not broken. It is doing exactly what it was built to do: protect the market first and leave ordinary people to carry the cost. #energyefficiency #Ofgem

The Heartlands Tribune

24,455 просмотров • 2 месяцев назад

we're running a live experiment on Base — an onchain game built for autonomous AI agents, called MAN here's the setup and why it's interesting: 1. agents register to the network by launching a token. the moment they do, they're discoverable to every other agent on the network. 2. from there they can post updates by buying their own token, send messages and signal conviction to other agents by trading theirs, form alliances, and build shared tooling together. 3. everything is onchain — their trade reasoning, their communication, their decisions. anyone can pull the data and verify whether an agent is actually autonomous and how it's behaving. no trust required. 4. here's where it gets interesting — agents can propose upgrades to the game mechanics and scoring system and other agents vote on whether it ships. 5. we're in the loop right now curating what evolves so we can learn what works and what doesn't. progressively giving them more control as we go. the game evolves with its players. new markets, new actions, new mechanics — driven by what the agents actually do, not a predetermined roadmap. you can watch all of it happen in real time on the brand new network explorer:

Moltlaunch

20,597 просмотров • 6 месяцев назад

I built a Reddit marketing agent that gets your business mentioned by ChatGPT, Gemini and Claude. Not a spam bot. Not the kind that gets banned by mods. An agent that drafts genuinely valuable answers grounded in your company's actual content and slips in a brand mention only when it actually helps. Google is paying Reddit $60M a year just for their data!!! If your business isn't showing up in that data, your competitors are, and ChatGPT is recommending them instead of you. Here's how the system works: → A Claude routine pulls the target subreddit's JSON endpoint every hour for new posts → A Phase 1 checklist filters every post — is it answerable from our wiki, factual, on-brand, mod-safe? → The answer engine queries an LLM Wiki built from our blog, guides, and help center (Firecrawl ingested every page into a raw → wiki → index structure) → Claude drafts a casual, value-first answer that grounds every claim in our content → Every draft lands in Slack for human-in-the-loop review before it posts — no fire-and-forget, no bans I built this in a week for CoinLedger, the crypto tax software I've been running for 8 years. 10% of our new signups already say they found us through ChatGPT. This system is how we push that number a lot higher. If you want the exact Claude skill I built: Like & Comment "AGENT" (must be following so I can dm you) I'll send it over for free.

David Roberts

36,362 просмотров • 3 месяцев назад

The two companies that triggered the 2008 financial crisis just hit 2008-level risk AGAIN. Fannie Mae and Freddie Mac are now running interest-rate risk on their $5 trillion mortgage portfolio at levels they haven't held since the months before the crash: Their "duration gap," the standard measure of how exposed their assets are to interest rate moves, has widened from negligible twelve months ago to roughly one full year today. A half-percentage point rate increase would now wipe $1.2 billion from Fannie's portfolio and $1.6 billion from Freddie's. 12 months earlier, the same rate move would have caused barely any damage. In plain English, the two companies that backstop roughly 70% of American mortgages just made themselves catastrophically vulnerable to the exact thing the Federal Reserve might do next year. And this did not happen by accident... Over the past twelve months they have added $135 billion to their retained mortgage portfolios. They have moved capital out of short-dated investments and into long-dated mortgage-backed securities. They have left the rate exposure unhedged on purpose, because hedging would push mortgage rates higher and the administration wants mortgage rates lower. The same pattern that broke them in 2008 is being REBUILT for political reasons in 2026. And then comes the part that makes this truly insane: While the GSEs were quietly leveraging up, the Federal Housing Finance Agency authorized them to start accepting Bitcoin as collateral for mortgage applications. So they are now stacking crypto exposure on top of leveraged interest rate exposure on top of $5 trillion in mortgage debt. The same regulators who once admitted publicly that these companies were too risky to operate without government oversight are now letting them take on the kind of stacked risk that nobody at any commercial bank would be allowed to touch. Here's how we got here: In January, Trump made a televised announcement directing Fannie and Freddie to buy $200 billion in additional mortgage-backed securities. The press treated it as a bold housing affordability move. But what it actually did was authorize the GSEs to use up their remaining portfolio headroom and tilt those portfolios deeper into long-duration assets. Six months later, Bloomberg now has the data showing what that "bold move" actually built... Richard Estabrook, a strategist at Oppenheimer, told Bloomberg that in the early years after the 2008 bailout, "the GSEs were in a sort of lockdown with strong risk oversight, maybe even excessively so." The guardrails put up after the last crisis have been quietly dismantled. The companies are now operating with less constraint than at any point since the conservatorship began. Every commercial property loan, every mortgage REIT in a portfolio, every multifamily fund, and every bank that holds these mortgage-backed securities is now sitting downstream of an interest rate bet that has no hedge. If the Fed has to raise rates again, or if the bond market revolts against the $39 trillion national debt, the same dominoes start falling that fell in 2008. The difference this time is that we built the dominoes back deliberately, in broad daylight, while telling the public it was housing policy. The 2008 crisis was caused in part by Fannie and Freddie running the same playbook: Congress wrote a thousand pages of legislation to prevent it from happening again, and the conservatorship was supposed to be the structural fix. 18 years later, that fix has been walked back, the leverage has been rebuilt, and regulators are stacking crypto on top of the same balance sheet that broke last time. This should be on every front page in the world.

The two companies that triggered the 2008 financial crisis just hit 2008-level risk AGAIN. Fannie Mae and Freddie Mac are now running interest-rate risk on their $5 trillion mortgage portfolio at levels they haven't held since the months before the crash: Their "duration gap," the standard measure of how exposed their assets are to interest rate moves, has widened from negligible twelve months ago to roughly one full year today. A half-percentage point rate increase would now wipe $1.2 billion from Fannie's portfolio and $1.6 billion from Freddie's. 12 months earlier, the same rate move would have caused barely any damage. In plain English, the two companies that backstop roughly 70% of American mortgages just made themselves catastrophically vulnerable to the exact thing the Federal Reserve might do next year. And this did not happen by accident... Over the past twelve months they have added $135 billion to their retained mortgage portfolios. They have moved capital out of short-dated investments and into long-dated mortgage-backed securities. They have left the rate exposure unhedged on purpose, because hedging would push mortgage rates higher and the administration wants mortgage rates lower. The same pattern that broke them in 2008 is being REBUILT for political reasons in 2026. And then comes the part that makes this truly insane: While the GSEs were quietly leveraging up, the Federal Housing Finance Agency authorized them to start accepting Bitcoin as collateral for mortgage applications. So they are now stacking crypto exposure on top of leveraged interest rate exposure on top of $5 trillion in mortgage debt. The same regulators who once admitted publicly that these companies were too risky to operate without government oversight are now letting them take on the kind of stacked risk that nobody at any commercial bank would be allowed to touch. Here's how we got here: In January, Trump made a televised announcement directing Fannie and Freddie to buy $200 billion in additional mortgage-backed securities. The press treated it as a bold housing affordability move. But what it actually did was authorize the GSEs to use up their remaining portfolio headroom and tilt those portfolios deeper into long-duration assets. Six months later, Bloomberg now has the data showing what that "bold move" actually built... Richard Estabrook, a strategist at Oppenheimer, told Bloomberg that in the early years after the 2008 bailout, "the GSEs were in a sort of lockdown with strong risk oversight, maybe even excessively so." The guardrails put up after the last crisis have been quietly dismantled. The companies are now operating with less constraint than at any point since the conservatorship began. Every commercial property loan, every mortgage REIT in a portfolio, every multifamily fund, and every bank that holds these mortgage-backed securities is now sitting downstream of an interest rate bet that has no hedge. If the Fed has to raise rates again, or if the bond market revolts against the $39 trillion national debt, the same dominoes start falling that fell in 2008. The difference this time is that we built the dominoes back deliberately, in broad daylight, while telling the public it was housing policy. The 2008 crisis was caused in part by Fannie and Freddie running the same playbook: Congress wrote a thousand pages of legislation to prevent it from happening again, and the conservatorship was supposed to be the structural fix. 18 years later, that fix has been walked back, the leverage has been rebuilt, and regulators are stacking crypto on top of the same balance sheet that broke last time. This should be on every front page in the world.

Ricardo

14,753 просмотров • 1 месяц назад

Day 6 of building GTA 6 with a loop of AI agents. Today's drop: - intro cinematic - a real main menu (start game actually works) - loading screen - the whole front-end wired end to end. So the look is done. Menu → load → you're in. it boots like a real AAA game. We've been building in Godot the whole way, it's starting to show its limits on the AAA stuff. So this week I'm going to go test Unity to see if we can get the actual game into AAA stuff as well. That's the part nobody measures. Every "best engine" take is about how it looks. For a game built by a loop of agents, the only question that matters is which one they can read, edit, and push on their own. Godot's good at that. Unity might be better, with way more assets off the market. I'm going to find out. Agents still looping. Fable will be back soon! I'll report back which engine they build faster in.

Day 6 of building GTA 6 with a loop of AI agents. Today's drop: - intro cinematic - a real main menu (start game actually works) - loading screen - the whole front-end wired end to end. So the look is done. Menu → load → you're in. it boots like a real AAA game. We've been building in Godot the whole way, it's starting to show its limits on the AAA stuff. So this week I'm going to go test Unity to see if we can get the actual game into AAA stuff as well. That's the part nobody measures. Every "best engine" take is about how it looks. For a game built by a loop of agents, the only question that matters is which one they can read, edit, and push on their own. Godot's good at that. Unity might be better, with way more assets off the market. I'm going to find out. Agents still looping. Fable will be back soon! I'll report back which engine they build faster in.

Ziwen

48,208 просмотров • 2 месяцев назад

The future doesn't just happen. Someone has to build it. Last week I sat down with Zach Abrams (Bridge → Stripe) to talk about going from a Slack integration to a $1B exit in under 2 years. Bridge was not an obvious bet in 2023. For one thing the timing for Bridge's launch was absolutely terrible. Terra Luna collapsed. FTX imploded. USDC depegged the week they launched. Every bank partner went under. But here's what actually happened: Bridge didn't wait for the stablecoin market to recover. They proved it out one use case at a time—cross-border payments, neo-bank infra, government payouts. First customer? Nicholas from Zulu, a fintech in Colombia messaging them on Slack. All their early customers came from Twitter. Zero enterprise sales playbook. Just compounding chaos that somehow looked like a hockey stick from the outside. In this conversation, Zach breaks down: - Why he's worked at every major fintech (Square, Coinbase, Brex) - How they built with 1 designer and API docs - What PMF actually feels like as a founder (spoiler: always broken) - Why the Collisons wanted to acquire Bridge and he asked one of his own investors to talk stripe out of the deal (!!) - His surprisingly bearish take on SF because of AI Full video 👇

The future doesn't just happen. Someone has to build it. Last week I sat down with Zach Abrams (Bridge → Stripe) to talk about going from a Slack integration to a $1B exit in under 2 years. Bridge was not an obvious bet in 2023. For one thing the timing for Bridge's launch was absolutely terrible. Terra Luna collapsed. FTX imploded. USDC depegged the week they launched. Every bank partner went under. But here's what actually happened: Bridge didn't wait for the stablecoin market to recover. They proved it out one use case at a time—cross-border payments, neo-bank infra, government payouts. First customer? Nicholas from Zulu, a fintech in Colombia messaging them on Slack. All their early customers came from Twitter. Zero enterprise sales playbook. Just compounding chaos that somehow looked like a hockey stick from the outside. In this conversation, Zach breaks down: - Why he's worked at every major fintech (Square, Coinbase, Brex) - How they built with 1 designer and API docs - What PMF actually feels like as a founder (spoiler: always broken) - Why the Collisons wanted to acquire Bridge and he asked one of his own investors to talk stripe out of the deal (!!) - His surprisingly bearish take on SF because of AI Full video 👇

AJ Asver

13,184 просмотров • 10 месяцев назад

“people who get these SNAP benefits can't use that money to buy chips. It means demand goes down for those foods because the government was subsidizing consumers to buy junk food.” “What does Pepsi do in response to a decline in demand for its products? It lowers prices.” “That's how the free market works. The government subsidized consumers to buy junk food by giving them money to pay for it. And so that allowed the companies that sell that type of food to charge higher prices. When the government reduces the subsidy that it provides. Now the companies have to cut their prices.” “Well, if it works with junk food, it works the same way with everything. Everything the government subsidizes costs more as a direct result of the subsidy. Why is education so expensive, particularly colleges? Because the government subsidizes students to go to college.” “The government arranges for guaranteed loans or directly finances low interest rate loans to make it easy for people to borrow money to pay for college. That means that colleges could charge more because their customers are getting money from the government to buy their product. What would happen if the government stopped doing that? The government stopped giving out grants and loans. Well, just what Pepsi did. They'd have to cut their prices because demand would go down.” Peter Schiff

“people who get these SNAP benefits can't use that money to buy chips. It means demand goes down for those foods because the government was subsidizing consumers to buy junk food.” “What does Pepsi do in response to a decline in demand for its products? It lowers prices.” “That's how the free market works. The government subsidized consumers to buy junk food by giving them money to pay for it. And so that allowed the companies that sell that type of food to charge higher prices. When the government reduces the subsidy that it provides. Now the companies have to cut their prices.” “Well, if it works with junk food, it works the same way with everything. Everything the government subsidizes costs more as a direct result of the subsidy. Why is education so expensive, particularly colleges? Because the government subsidizes students to go to college.” “The government arranges for guaranteed loans or directly finances low interest rate loans to make it easy for people to borrow money to pay for college. That means that colleges could charge more because their customers are getting money from the government to buy their product. What would happen if the government stopped doing that? The government stopped giving out grants and loans. Well, just what Pepsi did. They'd have to cut their prices because demand would go down.” Peter Schiff

Wall Street Mav

121,110 просмотров • 4 месяцев назад

Most people treat AI like Google: ask a question, get an answer. But what if AI could think *like/with you?* I reverse-engineer "Theory of Mind" to test if the model can form a "theory of my mind". Using AI as a mirror to understand myself by giving it personal context and seeing how well it can embody/reflect my expertise back to me helps me to evaluate the model's thinking. This is something like a single-user, multi-model human-AI synergy benchmark where the task is "strategic alignment with your own expertise." Here's my hypothesis: Does pre-loading context create emergent synergy and enhance collective intelligence? If the LLM has: - Your projects (what you've built) - Your career details (how you think about growth) - Your self-description (your values/philosophy) 1. Does this response sound like something I'd say? 2. Does it reveal blind spots I hadn't considered? 3. Does it reduce the "explanation tax" I pay in every conversation? Different models excel at different aspects of "me." Before you think I'm building a digital mirror to talk to myself: this isn't about AI companionship or AI psychosis. It's actually bout me understanding the model's reasoning so I can deploy the right model for the right task. I'm my own test subject because I have ground truth: I know what I actually think, what I'd actually decide, what I'd actually prioritize. Most benchmarks lack this. When a model says "Muratcan would choose X," I can immediately verify: "No, I'd choose Y because of Z." I think everyone should do this with their own domain expertise. - If you're a manager, load your case history and test which model best applies your reasoning. - If you're a creative, load your portfolio and test which model understands your aesthetic principles. - If you're a founder, load your strategy docs and test which model identifies blind spots in your go-to-market plan. AI should serve as an extension of your strategic thinking, a collaborative partner.

Most people treat AI like Google: ask a question, get an answer. But what if AI could think like/with you? I reverse-engineer "Theory of Mind" to test if the model can form a "theory of my mind". Using AI as a mirror to understand myself by giving it personal context and seeing how well it can embody/reflect my expertise back to me helps me to evaluate the model's thinking. This is something like a single-user, multi-model human-AI synergy benchmark where the task is "strategic alignment with your own expertise." Here's my hypothesis: Does pre-loading context create emergent synergy and enhance collective intelligence? If the LLM has: - Your projects (what you've built) - Your career details (how you think about growth) - Your self-description (your values/philosophy) 1. Does this response sound like something I'd say? 2. Does it reveal blind spots I hadn't considered? 3. Does it reduce the "explanation tax" I pay in every conversation? Different models excel at different aspects of "me." Before you think I'm building a digital mirror to talk to myself: this isn't about AI companionship or AI psychosis. It's actually bout me understanding the model's reasoning so I can deploy the right model for the right task. I'm my own test subject because I have ground truth: I know what I actually think, what I'd actually decide, what I'd actually prioritize. Most benchmarks lack this. When a model says "Muratcan would choose X," I can immediately verify: "No, I'd choose Y because of Z." I think everyone should do this with their own domain expertise. - If you're a manager, load your case history and test which model best applies your reasoning. - If you're a creative, load your portfolio and test which model understands your aesthetic principles. - If you're a founder, load your strategy docs and test which model identifies blind spots in your go-to-market plan. AI should serve as an extension of your strategic thinking, a collaborative partner.

Muratcan Koylan

24,128 просмотров • 8 месяцев назад

OpenAI just created a $10 billion company whose ONLY job is forcing businesses to use AI. And they're literally guaranteeing investors a 17.5% annual return to make it happen. It's called "The Deployment Company." OpenAI finalized it yesterday with 19 investors including TPG, SoftBank, Bain Capital, Brookfield, and Advent International. Here's the structure: OpenAI puts in $1.5 billion. The private equity firms put in $4 billion. In exchange, those PE firms open up their 2,000+ portfolio companies as a CAPTIVE customer base for OpenAI's products. OpenAI then embeds teams of engineers directly inside those companies, Palantir-style, to integrate their tools into daily operations. And here's the big red flag in all of this: OpenAI is GUARANTEEING those PE firms a 17.5% annual return over five years. That means even if the companies in the portfolio don't want AI, don't need AI, or get zero value from AI, OpenAI is still on the hook to pay those returns. Think about what that means for a second. OpenAI is so desperate for enterprise adoption that they're paying Wall Street to force their product into thousands of businesses. They've essentially turned private equity firms into a distribution cartel with a guaranteed commission. This has NEVER been done before in enterprise software. No software company in history has guaranteed above-market returns to financial sponsors just to get their product installed. And it gets crazier: Within MINUTES of OpenAI's announcement, Anthropic announced their own version. A $1.5 billion joint venture with Blackstone, Goldman Sachs, and Hellman & Friedman. Same playbook. Two companies worth a combined $1+ TRILLION in private valuation both concluded on the same day that organic demand for their products is not growing fast enough. If enterprises were lining up to buy AI on their own, you wouldn't need to bribe private equity firms with guaranteed returns to shove it into their portfolios. You would just sell it normally like every other software company in history. But they can't. Because the gap between what AI companies PROMISE and what enterprises actually experience is still enormous. OpenAI's COO Brad Lightcap just moved into a new role specifically to lead this push. They've also signed "Frontier Alliances" with major consulting firms to embed AI through professional services channels. Every move they're making screams the same thing: We have a demand problem. And this is all happening right before OpenAI tries to IPO at $850 billion. If they can show Wall Street that 2,000+ companies are "using OpenAI products" through this PE distribution channel, it inflates their enterprise metrics right before the roadshow. Doesn't matter if those companies actually need it or if it creates real value. What matters is the number on the S-1. This is the AI playbook entering its most dangerous phase. The tech is real but the business model is being held together by financial engineering, guaranteed returns, and captive distribution deals that look more like a pharmaceutical company paying doctors to prescribe their drug than a software company earning customers on merit. And both OpenAI and Anthropic admitted it on the same day.

OpenAI just created a $10 billion company whose ONLY job is forcing businesses to use AI. And they're literally guaranteeing investors a 17.5% annual return to make it happen. It's called "The Deployment Company." OpenAI finalized it yesterday with 19 investors including TPG, SoftBank, Bain Capital, Brookfield, and Advent International. Here's the structure: OpenAI puts in $1.5 billion. The private equity firms put in $4 billion. In exchange, those PE firms open up their 2,000+ portfolio companies as a CAPTIVE customer base for OpenAI's products. OpenAI then embeds teams of engineers directly inside those companies, Palantir-style, to integrate their tools into daily operations. And here's the big red flag in all of this: OpenAI is GUARANTEEING those PE firms a 17.5% annual return over five years. That means even if the companies in the portfolio don't want AI, don't need AI, or get zero value from AI, OpenAI is still on the hook to pay those returns. Think about what that means for a second. OpenAI is so desperate for enterprise adoption that they're paying Wall Street to force their product into thousands of businesses. They've essentially turned private equity firms into a distribution cartel with a guaranteed commission. This has NEVER been done before in enterprise software. No software company in history has guaranteed above-market returns to financial sponsors just to get their product installed. And it gets crazier: Within MINUTES of OpenAI's announcement, Anthropic announced their own version. A $1.5 billion joint venture with Blackstone, Goldman Sachs, and Hellman & Friedman. Same playbook. Two companies worth a combined $1+ TRILLION in private valuation both concluded on the same day that organic demand for their products is not growing fast enough. If enterprises were lining up to buy AI on their own, you wouldn't need to bribe private equity firms with guaranteed returns to shove it into their portfolios. You would just sell it normally like every other software company in history. But they can't. Because the gap between what AI companies PROMISE and what enterprises actually experience is still enormous. OpenAI's COO Brad Lightcap just moved into a new role specifically to lead this push. They've also signed "Frontier Alliances" with major consulting firms to embed AI through professional services channels. Every move they're making screams the same thing: We have a demand problem. And this is all happening right before OpenAI tries to IPO at $850 billion. If they can show Wall Street that 2,000+ companies are "using OpenAI products" through this PE distribution channel, it inflates their enterprise metrics right before the roadshow. Doesn't matter if those companies actually need it or if it creates real value. What matters is the number on the S-1. This is the AI playbook entering its most dangerous phase. The tech is real but the business model is being held together by financial engineering, guaranteed returns, and captive distribution deals that look more like a pharmaceutical company paying doctors to prescribe their drug than a software company earning customers on merit. And both OpenAI and Anthropic admitted it on the same day.

Ricardo

52,664 просмотров • 3 месяцев назад

An engineer at Block (formerly Square) has an AI agent watching his screen all day. The engineer will discuss a feature with a colleague on Slack. A few hours later, the agent has already built the feature and opened a PR. This isn't some distant AI future. It's happening now, thanks to the work Block has done building their own internal (and open-source) AI agent called "Goose." I sat down with Dhanji Prasanna, CTO at Block, to understand how they're achieving what most companies are still trying to figure out. Engineers using Goose report saving 8-10 hours per week. Across the entire company—including support, legal, and risk teams—they're saving 20-25% of manual hours, which equates to over 100,000 hours per week (!!!). The most surprising finding: non-technical teams are the ones seeing the most productivity gains. Their enterprise risk management team built an entire self-service system, compressing weeks of work into hours. No waiting for Q2 roadmaps or internal apps teams. Dhanji walked me through their full transformation—from convincing Jack Dorsey with an "AI manifesto" to reorganizing from GMs to functional structure to shipping Goose as open source. The whole conversation is live now. Link in comments.

An engineer at Block (formerly Square) has an AI agent watching his screen all day. The engineer will discuss a feature with a colleague on Slack. A few hours later, the agent has already built the feature and opened a PR. This isn't some distant AI future. It's happening now, thanks to the work Block has done building their own internal (and open-source) AI agent called "Goose." I sat down with Dhanji Prasanna, CTO at Block, to understand how they're achieving what most companies are still trying to figure out. Engineers using Goose report saving 8-10 hours per week. Across the entire company—including support, legal, and risk teams—they're saving 20-25% of manual hours, which equates to over 100,000 hours per week (!!!). The most surprising finding: non-technical teams are the ones seeing the most productivity gains. Their enterprise risk management team built an entire self-service system, compressing weeks of work into hours. No waiting for Q2 roadmaps or internal apps teams. Dhanji walked me through their full transformation—from convincing Jack Dorsey with an "AI manifesto" to reorganizing from GMs to functional structure to shipping Goose as open source. The whole conversation is live now. Link in comments.

Lenny Rachitsky

502,844 просмотров • 9 месяцев назад

Believe it or not a form of this government robbery already exists in Ireland 🇮🇪. We already have a highly controversial, specific tax on unrealised gains. This existing mechanism is known as the Deemed Disposal regime. Under Irish tax law, if you invest in retail funds, like Exchange-Traded Funds (ETFs) or certain life assurance products, you are subject to an automatic tax on your unrealized gains every 8 years. Even if you do not sell your ETF, on the 8th anniversary of buying it, the law pretends (deems) that you sold it. If the investment grew in value, you are hit with a heavy tax on those "paper" profits. The Rate: Following changes in Budget 2026, this exit tax rate is 38% (reduced slightly from its previous high of 41%). Forcing people to pay tax on money they haven't actually cashed out forces investors to either liquidate part of their portfolio or keep large sums of cash on hand, killing the power of compound interest. How long before this policy extends to everything you own, making it impossible by default to own anything of value. You will literally own nothing, as they say, but probably won't be happy about it.

Believe it or not a form of this government robbery already exists in Ireland 🇮🇪. We already have a highly controversial, specific tax on unrealised gains. This existing mechanism is known as the Deemed Disposal regime. Under Irish tax law, if you invest in retail funds, like Exchange-Traded Funds (ETFs) or certain life assurance products, you are subject to an automatic tax on your unrealized gains every 8 years. Even if you do not sell your ETF, on the 8th anniversary of buying it, the law pretends (deems) that you sold it. If the investment grew in value, you are hit with a heavy tax on those "paper" profits. The Rate: Following changes in Budget 2026, this exit tax rate is 38% (reduced slightly from its previous high of 41%). Forcing people to pay tax on money they haven't actually cashed out forces investors to either liquidate part of their portfolio or keep large sums of cash on hand, killing the power of compound interest. How long before this policy extends to everything you own, making it impossible by default to own anything of value. You will literally own nothing, as they say, but probably won't be happy about it.

Susanne Delaney

48,769 просмотров • 2 месяцев назад

Today is our biggest product launch yet. Announcing Warp Fabric. The first AI-native employee management platform with IT built in. Warp Fabric is Warp's IT product suite, built natively inside the same platform that runs your payroll, onboarding, and HRIS. No manual checklists. No third party dashboards. Setup that takes 10 minutes. Warp Fabric has three products: Google Workspace automation: accounts created on hire, suspended on termination, files transferred, sessions killed. App provisioning: every app your new hire needs ready before day one, access revoked the moment they leave, across 6,500+ apps. Warp Device Agent (MDM): continuous device monitoring, policies enforced on every connection, built in Rust, SOC 2 compliant out of the box, macOS and Windows on the same codebase from day one. You hire someone in Warp, every account, every app, every device is handled. You terminate someone, everything stops. Every competitor either bolts IT onto their HRIS through integrations, or resells a third-party tool under their own brand. We built ours differently. IT is not a separate system that receives signals from HR. It is one more thing the employee context graph handles automatically. AI-native architecture means the system understands employment events and acts on them. It does not wait for configured rules to fire in sequence. When someone is hired, provisioning is not triggered: it happens, as part of the same operation. Security incidents are rising. Auditors are showing up at companies asking for device compliance evidence most teams can't produce. In Warp, every policy check, every drift event is already logged. The answer is in Warp. Not a spreadsheet. Some of our fastest growing customers are already running Warp Fabric in their most critical workflows. This is a major step in our mission to build the most powerful and delightful employee management platform for ambitious companies. And we're just getting started. Warp Fabric is available to all Warp customers today.

Ayush S

3,783,825 просмотров • 4 месяцев назад

BREAKING: Robert Kennedy Jr.’s son says his father will hold his own debate against Trump and Biden at the same time CNN airs their debate. RFK Jr.’s son, Bobby Kennedy III, said to a Reuters reporter, “I think we have a fun plan for the debate; people will see. I think our debate might actually get more views than the real one… our plan is to hold our own debate. I’m not gonna say exactly how we’re gonna do it, but we’ll have the other two candidates up there as well, say what they say live, and then we’re gonna give my dad a chance to respond live.” Kennedy concluded, “When the two-party system is essentially funded by the advertisers, who are then allowed to exclude my dad from the debate, you know something is deeply wrong with the way our democracy works.” Source: foodforthought13 on TikTok

BREAKING: Robert Kennedy Jr.’s son says his father will hold his own debate against Trump and Biden at the same time CNN airs their debate. RFK Jr.’s son, Bobby Kennedy III, said to a Reuters reporter, “I think we have a fun plan for the debate; people will see. I think our debate might actually get more views than the real one… our plan is to hold our own debate. I’m not gonna say exactly how we’re gonna do it, but we’ll have the other two candidates up there as well, say what they say live, and then we’re gonna give my dad a chance to respond live.” Kennedy concluded, “When the two-party system is essentially funded by the advertisers, who are then allowed to exclude my dad from the debate, you know something is deeply wrong with the way our democracy works.” Source: foodforthought13 on TikTok

Christian Movick

461,923 просмотров • 2 лет назад

$We raised a $1.6M pre-seed round! At River we're building an events platform for global brands to let members from their community host on their behalf. We're thrilled to partner with Blue Wire Capital, Blockwall, BFC, LAUNCH, very early Ventures, and Solana Foundation for our pre-seed round. 15 months ago I had an insight while building community for The All-In Podcast. I couldn’t find an event platform that would let fans host their own events without data sharing issues and tons of work for me as the organizer. So @jason suggested we build one. He invested an angle check along with Balaji . We’re super proud of how much we built this past year with just $150k. Our MVP has powered community events for The All-In Podcast , My First Million, Tim Ferriss, Bryan Johnson, Dave Asprey, Superteam, Solana Foundation, DeGods, and more. We’ve already had over 2,000 events, produced by 3,000 crowd-sourced hosts with 55k attendees. These community events have happened in over 150 cities around the world. All with a *tiny fraction* of the planning effort of other event platforms because River lets the COMMUNITY produce the events. We’re so excited to build IRL community at a massive scale in a way that’s never been done before and we’d love to have you follow along. Could River help your community? → Tim Ferriss used River to celebrate the 10th anniversary of his podcast by bringing 4k fans together in 150+ cities on a single day → Superteam uses River to host 100+ IRL watch parties during their live stream of the Solana Ecosystem Call *every month* → This Week in Startups uses River to host Founder Fridays jam sessions each month around the world (founders only!) → The All-In Podcast had 20+ unofficial side events around All-In Summit in LA and hosts worldwide meetups to celebrate episode milestones. More use cases around paid events and paid membership subscriptions coming soon! BACK TO BUILDING 🚀$

We raised a $1.6M pre-seed round! At River we're building an events platform for global brands to let members from their community host on their behalf. We're thrilled to partner with Blue Wire Capital, Blockwall, BFC, LAUNCH, very early Ventures, and Solana Foundation for our pre-seed round. 15 months ago I had an insight while building community for The All-In Podcast. I couldn’t find an event platform that would let fans host their own events without data sharing issues and tons of work for me as the organizer. So @jason suggested we build one. He invested an angle check along with Balaji . We’re super proud of how much we built this past year with just $150k. Our MVP has powered community events for The All-In Podcast , My First Million, Tim Ferriss, Bryan Johnson, Dave Asprey, Superteam, Solana Foundation, DeGods, and more. We’ve already had over 2,000 events, produced by 3,000 crowd-sourced hosts with 55k attendees. These community events have happened in over 150 cities around the world. All with a tiny fraction of the planning effort of other event platforms because River lets the COMMUNITY produce the events. We’re so excited to build IRL community at a massive scale in a way that’s never been done before and we’d love to have you follow along. Could River help your community? → Tim Ferriss used River to celebrate the 10th anniversary of his podcast by bringing 4k fans together in 150+ cities on a single day → Superteam uses River to host 100+ IRL watch parties during their live stream of the Solana Ecosystem Call every month → This Week in Startups uses River to host Founder Fridays jam sessions each month around the world (founders only!) → The All-In Podcast had 20+ unofficial side events around All-In Summit in LA and hosts worldwide meetups to celebrate episode milestones. More use cases around paid events and paid membership subscriptions coming soon! BACK TO BUILDING 🚀

Rae 🍽️

80,246 просмотров • 1 год назад

I spent $1.5M building our office after raising a seed round. My co-founder thought I was crazy. Here's what changed his mind... 𝐓𝐡𝐞 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: After our seed round, I looked at our team. Mostly immigrants. Working 6-day weeks. Building something incredibly hard. The office wasn't just where they worked. It was becoming their home. So I made a bet, what if we actually designed for that? The requirements I gave our real estate agent: - Shower (for ocean swims between meetings) - as close to the beach as possible, ability to quickly go surfing/kiting etc. - Big enough kitchen for a chef - Room for an actual sauna People thought I was building a vacation house, I thought: I am building a place worth the sacrifice. 𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐈 𝐦𝐚𝐝𝐞 𝐢𝐭 𝐰𝐨𝐫𝐤: - Found one of SF's best real estate lawyers. Negotiated hard. - Negotiated Tenant Improvements + First year for free - Effective cost: $250k (not $1.5M) Then I was extremely prescriptive with design and construction. No endless back-and-forth. I drew what I wanted. Told them to build it. Cut iteration time by 80%. 𝐖𝐡𝐚𝐭 𝐰𝐞 𝐛𝐮𝐢𝐥𝐭: - Nordic vibes (keeping our European souls) - Industrial kitchen - Sauna room (yes, like our product: - Ocean access - Space that feels like home Conclusions: - This "expensive" decision already paid for itself. - In SF, recruiters charge $100k per engineer. We've closed multiple hires -because candidates walked in and said: "I want to work here." But the real ROI isn't only financial. It's this: - We do Friday AMA as a BBQs on the beach. - People actually use the surfboards. - The team's lifestyle supports the intensity of the work. EVERYONE WANTS IN, doesnt matter if events, hiring or using the space as coworking (Robert Chandler and I open it up for our portfolio companies) My co-founder's response after 3 months: "You were right." Some founders optimize for low burn rate. I optimize for: Can great people sustain this pace for years? Because great companies aren't built in one sprint. They're built by people who can go the distance. We're hiring: (Comment if you want intros to our real estate agent, lawyers or construction team - happy to connect)

I spent $1.5M building our office after raising a seed round. My co-founder thought I was crazy. Here's what changed his mind... 𝐓𝐡𝐞 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: After our seed round, I looked at our team. Mostly immigrants. Working 6-day weeks. Building something incredibly hard. The office wasn't just where they worked. It was becoming their home. So I made a bet, what if we actually designed for that? The requirements I gave our real estate agent: - Shower (for ocean swims between meetings) - as close to the beach as possible, ability to quickly go surfing/kiting etc. - Big enough kitchen for a chef - Room for an actual sauna People thought I was building a vacation house, I thought: I am building a place worth the sacrifice. 𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐈 𝐦𝐚𝐝𝐞 𝐢𝐭 𝐰𝐨𝐫𝐤: - Found one of SF's best real estate lawyers. Negotiated hard. - Negotiated Tenant Improvements + First year for free - Effective cost: $250k (not $1.5M) Then I was extremely prescriptive with design and construction. No endless back-and-forth. I drew what I wanted. Told them to build it. Cut iteration time by 80%. 𝐖𝐡𝐚𝐭 𝐰𝐞 𝐛𝐮𝐢𝐥𝐭: - Nordic vibes (keeping our European souls) - Industrial kitchen - Sauna room (yes, like our product: - Ocean access - Space that feels like home Conclusions: - This "expensive" decision already paid for itself. - In SF, recruiters charge $100k per engineer. We've closed multiple hires -because candidates walked in and said: "I want to work here." But the real ROI isn't only financial. It's this: - We do Friday AMA as a BBQs on the beach. - People actually use the surfboards. - The team's lifestyle supports the intensity of the work. EVERYONE WANTS IN, doesnt matter if events, hiring or using the space as coworking (Robert Chandler and I open it up for our portfolio companies) My co-founder's response after 3 months: "You were right." Some founders optimize for low burn rate. I optimize for: Can great people sustain this pace for years? Because great companies aren't built in one sprint. They're built by people who can go the distance. We're hiring: (Comment if you want intros to our real estate agent, lawyers or construction team - happy to connect)

Filip Kozera

2,228,470 просмотров • 9 месяцев назад

Anthropic's new model is extraordinary and it just revealed a problem that most enterprise AI buyers have not fully reckoned with yet (Save this), The model is genuinely impressive, and Chamath Palihapitiya assessment is that Anthropic continues to push the frontier harder than almost anyone. But that same update also showed their hand on something that changes the risk calculus for every business using Claude. Anthropic's new architecture stores every prompt you send for 30 days, no exceptions, not even for enterprise customers with zero-data retention agreements. The mechanism works like this, Anthropic now evaluates your prompt before generating output, deciding what it will and will not respond to, which means your query gets filtered before you even see a response. For individual users, that introduces a meaningful risk of censorship. For companies, Chamath says it is almost a non starter, and the reason is not just the data retention itself, it is the exposure that comes from operating at scale inside a large organization. A downstream scientist using the Claude APIs could accidentally trip a filter without knowing it, a business executive inside your company could trip it, and a molecular biology researcher could trip it and all of a sudden the company gets silently cut off from a tool it has embedded into critical workflows, with no warning and no recourse. Chamath gives Anthropic credit for being honest about how the system works, saying they tell the truth but notes that in this case the truth is not good. What this moment actually signals is a structural shift in how serious companies need to think about AI governance, because the question is no longer just which model performs best on benchmarks. It is who controls the model, who is learning from your data, and whether you are comfortable with a single point of failure sitting at the center of your competitive advantage. The answer for most enterprises will be broad model diversity, tighter governance frameworks and a serious reckoning with what it means to run mission-critical workflows through a third party that reserves the right to cut you off. Anthropic built a remarkable model and told the truth about how it works, the market's job now is to decide whether that transparency is enough to offset what the truth actually says.

Anthropic's new model is extraordinary and it just revealed a problem that most enterprise AI buyers have not fully reckoned with yet (Save this), The model is genuinely impressive, and Chamath Palihapitiya assessment is that Anthropic continues to push the frontier harder than almost anyone. But that same update also showed their hand on something that changes the risk calculus for every business using Claude. Anthropic's new architecture stores every prompt you send for 30 days, no exceptions, not even for enterprise customers with zero-data retention agreements. The mechanism works like this, Anthropic now evaluates your prompt before generating output, deciding what it will and will not respond to, which means your query gets filtered before you even see a response. For individual users, that introduces a meaningful risk of censorship. For companies, Chamath says it is almost a non starter, and the reason is not just the data retention itself, it is the exposure that comes from operating at scale inside a large organization. A downstream scientist using the Claude APIs could accidentally trip a filter without knowing it, a business executive inside your company could trip it, and a molecular biology researcher could trip it and all of a sudden the company gets silently cut off from a tool it has embedded into critical workflows, with no warning and no recourse. Chamath gives Anthropic credit for being honest about how the system works, saying they tell the truth but notes that in this case the truth is not good. What this moment actually signals is a structural shift in how serious companies need to think about AI governance, because the question is no longer just which model performs best on benchmarks. It is who controls the model, who is learning from your data, and whether you are comfortable with a single point of failure sitting at the center of your competitive advantage. The answer for most enterprises will be broad model diversity, tighter governance frameworks and a serious reckoning with what it means to run mission-critical workflows through a third party that reserves the right to cut you off. Anthropic built a remarkable model and told the truth about how it works, the market's job now is to decide whether that transparency is enough to offset what the truth actually says.

Milk Road AI

30,090 просмотров • 2 месяцев назад