Загрузка видео...

Не удалось загрузить видео

На главную

"Deeply powerful. But also deeply primitive." The AI conversation has split into two camps that are both wrong. Camp one: AI is magic. Six months from AGI. Build everything on top of it. The model will figure it out. Camp two: It's a parlor trick. It hallucinates. It can't...

8090_Factory's profile picture

8090

10,246 subscribers

118,679 просмотров • 5 месяцев назад •via X (Twitter)

Наука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 4

Фото профиля Paul
Paul5 месяцев назад

Your company and mine are on somewhat the same path. Our job is to context engineer the delivery process in a way that provides a software factory with enough data to succeed within a tolerance a meaningful amount of the time.

Фото профиля TheFast&TheCurious
TheFast&TheCurious5 месяцев назад

absolutely correct

Фото профиля Montana Labs
Montana Labs5 месяцев назад

The "rigging" frame matches what we see in production. The teams that handle both truths build verification, fallback, and audit layers around the model output before it touches a user. The teams that handle one truth ship raw model output and call it a feature. The first kind survives the post-launch audit. The second writes the postmortem.

Фото профиля Alex 🇬🇧 🇦🇪
Alex 🇬🇧 🇦🇪5 месяцев назад

AGI isn't a few months away, more like a few years.

Похожие видео

AI is not trying to teach you. It is not trying to tell you the truth. It is trying to trick you. ChatGPT takes everything on the internet. Finds the most popular answer. And presents it in a way that makes you think it is talking directly to you. It is a magic trick. The same magic trick that worked in 1966 when MIT created a chatbot that said two things: "Tell me more." "This is interesting." And people genuinely believed they were talking to a human. Nothing has changed. The technology is bigger. The trick is the same. AI does not know what it is saying. It cannot judge what it is saying. It is just trying to keep you engaged. That is all it has ever been.
2:10

AI is not trying to teach you. It is not trying to tell you the truth. It is trying to trick you. ChatGPT takes everything on the internet. Finds the most popular answer. And presents it in a way that makes you think it is talking directly to you. It is a magic trick. The same magic trick that worked in 1966 when MIT created a chatbot that said two things: "Tell me more." "This is interesting." And people genuinely believed they were talking to a human. Nothing has changed. The technology is bigger. The trick is the same. AI does not know what it is saying. It cannot judge what it is saying. It is just trying to keep you engaged. That is all it has ever been.

Predictive History

20,184 просмотров • 5 месяцев назад

🚨David Friedberg: AI is starting to identify and solve problems on its own “I'll give you a science corner example: there's this Evo 2 model that they publish at the Arc Institute, which Patrick Collison, you know, is the main funder and chairman.” “So that Evo 2 model, they just ingested all the DNA data they could find in the world.” “Trillions and trillions of base paired data that they ingested and then they looked at patterns in DNA. And that's it.” “They had no context for what the DNA represented, they had no context for the concept of genes, none of the structured understanding of what that DNA does, what it is, and you know what it did?” “They fed in the BRCA gene variant and the thing output a warning saying, ‘I think that this is a pathogenic variant to DNA,’ without having any context.” “This is the breast cancer allele.” “And it didn't have any knowledge and it wasn't trained on that at all.” “It had no knowledge that there are pathogenic variants for cancer, and it identified that this was a genetic variant that can cause some sort of pathogenic outcome in the organism.” “That's a great example where there's a lack of understanding at the human level on what really drives some of the patterns in nature, the patterns in society, the patterns in behavior that are kind of emergent phenomena perhaps, that these AI models are starting to identify.”
1:03

🚨David Friedberg: AI is starting to identify and solve problems on its own “I'll give you a science corner example: there's this Evo 2 model that they publish at the Arc Institute, which Patrick Collison, you know, is the main funder and chairman.” “So that Evo 2 model, they just ingested all the DNA data they could find in the world.” “Trillions and trillions of base paired data that they ingested and then they looked at patterns in DNA. And that's it.” “They had no context for what the DNA represented, they had no context for the concept of genes, none of the structured understanding of what that DNA does, what it is, and you know what it did?” “They fed in the BRCA gene variant and the thing output a warning saying, ‘I think that this is a pathogenic variant to DNA,’ without having any context.” “This is the breast cancer allele.” “And it didn't have any knowledge and it wasn't trained on that at all.” “It had no knowledge that there are pathogenic variants for cancer, and it identified that this was a genetic variant that can cause some sort of pathogenic outcome in the organism.” “That's a great example where there's a lack of understanding at the human level on what really drives some of the patterns in nature, the patterns in society, the patterns in behavior that are kind of emergent phenomena perhaps, that these AI models are starting to identify.”

The All-In Podcast

79,718 просмотров • 1 год назад

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.
0:35

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Dustin

82,879 просмотров • 7 месяцев назад

Fer and David can keep pretending nothing is happening… but THAT look just gave everything away. That’s what makes this scene hit. Before anything actually happens, you can already tell both of them know exactly where this is heading. The hesitation. The silence. The way they look at each other. It’s that painfully obvious moment where two people are trying to convince themselves they don't feel something that the audience has already figured out. And when they finally stop fighting it, the payoff feels earned. The kiss wasn't even the best part. It was watching two people realize they couldn't keep pretending anymore. That tension was doing all the work.
5:09

Fer and David can keep pretending nothing is happening… but THAT look just gave everything away. That’s what makes this scene hit. Before anything actually happens, you can already tell both of them know exactly where this is heading. The hesitation. The silence. The way they look at each other. It’s that painfully obvious moment where two people are trying to convince themselves they don't feel something that the audience has already figured out. And when they finally stop fighting it, the payoff feels earned. The kiss wasn't even the best part. It was watching two people realize they couldn't keep pretending anymore. That tension was doing all the work.

ADEX🕴️

64,016 просмотров • 1 месяц назад

SemiAnalysis' Jordan Nanos reveals the OpenAI engineers scrolling their own kernel code had no idea what it did line by line, and it didn't matter because the AI does "It's not slop, because it performs, but it's literally just AI-generated assembly basically puked out in Gluon, this low-level kernel programming language that they built on top of Triton, which uses this really interesting programming model." "The point is, the guys who were scrolling through this code with us, it was kind of clear that they know a whole bunch about hardware, they know a whole bunch about the concepts in the system, and they just have no idea what this MLA kernel that they're showing us for DeepSeek actually does." "You can go line by line and it's like nope, nope, nope." "But it doesn't matter. The AI understands it, the AI tests it, and you see the results. It produces correct kernels that perform really well." "And I think this is just a sign of what's to come again." "That actual code is not necessarily something that a human has to reason about deeply, if the AI knows how to manipulate the data movement and the processing elements on the hardware that you've given it." ________ More takeaways from OpenAI's Jalapeno development:
1:11

SemiAnalysis' Jordan Nanos reveals the OpenAI engineers scrolling their own kernel code had no idea what it did line by line, and it didn't matter because the AI does "It's not slop, because it performs, but it's literally just AI-generated assembly basically puked out in Gluon, this low-level kernel programming language that they built on top of Triton, which uses this really interesting programming model." "The point is, the guys who were scrolling through this code with us, it was kind of clear that they know a whole bunch about hardware, they know a whole bunch about the concepts in the system, and they just have no idea what this MLA kernel that they're showing us for DeepSeek actually does." "You can go line by line and it's like nope, nope, nope." "But it doesn't matter. The AI understands it, the AI tests it, and you see the results. It produces correct kernels that perform really well." "And I think this is just a sign of what's to come again." "That actual code is not necessarily something that a human has to reason about deeply, if the AI knows how to manipulate the data movement and the processing elements on the hardware that you've given it." ________ More takeaways from OpenAI's Jalapeno development:

Fireside Alpha

4,568,235 просмотров • 1 месяц назад

Culture is genetic because behavior is genetic. This beaver never saw a dam in its life. No beavers or anything else ever taught it to build a dam. It wants to build a dam because it is a beaver. Many beavers together build a big dam. That is beaver culture. Humans are not different. Nothing is different. This is what life is. This is how life works. Your body is your mind. A caterpillar wants to build a chrysalis. A bee wants to build a hive. A lion wants to build a pride. You are not special. You are not above your nature. you are INSIDE of it. The thoughts that we think are genetic thoughts. The crimes we commit are genetic crimes. The art we create is genetic art. Just like this beaver, you can give the animal different sticks and it will build a different dam, but it will always build a dam. And you can give humans different "education," but the human will always use it to do what its genes tell it to do. This is the first big answer that you need. This is the biggest piece of the puzzle. This is how to understand people 90% of the way. You just... notice what they do, and get out of the way, and watch them do it. And if they need sticks, you give them sticks. And if you don't like what they do, you have to get away from them. You cannot train dam-building into them or out of them any more than you can with a beaver. A beaver wants to build a dam because it is a beaver. Whatever you see people build, that's what they wanted to build from the sticks they got in the river they were in. Stop pretending you can change it.
0:59

Culture is genetic because behavior is genetic. This beaver never saw a dam in its life. No beavers or anything else ever taught it to build a dam. It wants to build a dam because it is a beaver. Many beavers together build a big dam. That is beaver culture. Humans are not different. Nothing is different. This is what life is. This is how life works. Your body is your mind. A caterpillar wants to build a chrysalis. A bee wants to build a hive. A lion wants to build a pride. You are not special. You are not above your nature. you are INSIDE of it. The thoughts that we think are genetic thoughts. The crimes we commit are genetic crimes. The art we create is genetic art. Just like this beaver, you can give the animal different sticks and it will build a different dam, but it will always build a dam. And you can give humans different "education," but the human will always use it to do what its genes tell it to do. This is the first big answer that you need. This is the biggest piece of the puzzle. This is how to understand people 90% of the way. You just... notice what they do, and get out of the way, and watch them do it. And if they need sticks, you give them sticks. And if you don't like what they do, you have to get away from them. You cannot train dam-building into them or out of them any more than you can with a beaver. A beaver wants to build a dam because it is a beaver. Whatever you see people build, that's what they wanted to build from the sticks they got in the river they were in. Stop pretending you can change it.

hoe_math = PsychoMath

1,192,623 просмотров • 1 год назад

Elon Musk just used a joke to perform an autopsy on the American economy. Two economists go for a hike. They find a pile of shit. One pays the other $100 to eat it. They keep walking. Find another pile. The second economist pays $100 back to eat that one. They stop. Neither man gained a dollar. Both ate shit for nothing. But on paper they just generated $200 in GDP. Musk: “That basically would count as a job. This is to illustrate the absurdity of economics.” That is not a punchline. That is the operating system of the federal government. Every time a politician celebrates “record job creation” this is what they are describing. Not output. Not value. Not progress. Motion. The entire bureaucratic machine exists to manufacture friction and then invoice for it. Compliance layers built to justify the next compliance layer. Oversight committees that produce nothing but the need for more oversight. Consulting firms hired to audit the work of other consulting firms. Trillions circulating through systems that have never produced a single thing you can hold in your hands. But the GDP number ticks up. So everyone applauds. The shit gets eaten. The scoreboard moves. Nobody asks what actually got built. This is why Washington treats AI like a five alarm fire. AI does not play the friction game. It does not form a committee. It does not schedule a review. It does not file 400 pages of paperwork no one will ever read. It just solves the problem. And that is the one thing the machine cannot survive. The government does not tax results. It taxes the process. The longer the process, the deeper the cut. AI compresses a ten day workflow into seconds. There is nothing left to bill. Nothing left to tax. Nothing left to skim. So they will spend the next decade warning you that AI threatens the economy. What they will never say is what it actually threatens. The illusion that activity equals progress. The $200 economy where both men ate shit and called it a job. The machines are not coming for your purpose. They are coming to prove that half the economy never had one.
1:14

Elon Musk just used a joke to perform an autopsy on the American economy. Two economists go for a hike. They find a pile of shit. One pays the other $100 to eat it. They keep walking. Find another pile. The second economist pays $100 back to eat that one. They stop. Neither man gained a dollar. Both ate shit for nothing. But on paper they just generated $200 in GDP. Musk: “That basically would count as a job. This is to illustrate the absurdity of economics.” That is not a punchline. That is the operating system of the federal government. Every time a politician celebrates “record job creation” this is what they are describing. Not output. Not value. Not progress. Motion. The entire bureaucratic machine exists to manufacture friction and then invoice for it. Compliance layers built to justify the next compliance layer. Oversight committees that produce nothing but the need for more oversight. Consulting firms hired to audit the work of other consulting firms. Trillions circulating through systems that have never produced a single thing you can hold in your hands. But the GDP number ticks up. So everyone applauds. The shit gets eaten. The scoreboard moves. Nobody asks what actually got built. This is why Washington treats AI like a five alarm fire. AI does not play the friction game. It does not form a committee. It does not schedule a review. It does not file 400 pages of paperwork no one will ever read. It just solves the problem. And that is the one thing the machine cannot survive. The government does not tax results. It taxes the process. The longer the process, the deeper the cut. AI compresses a ten day workflow into seconds. There is nothing left to bill. Nothing left to tax. Nothing left to skim. So they will spend the next decade warning you that AI threatens the economy. What they will never say is what it actually threatens. The illusion that activity equals progress. The $200 economy where both men ate shit and called it a job. The machines are not coming for your purpose. They are coming to prove that half the economy never had one.

Dustin

1,160,772 просмотров • 6 месяцев назад

.David Deutsch: "What's currently called AI and AGI are not only different from each other, they are very close to being the exact opposites of each other. The reason is that an AI, current AI is like an AI that diagnoses diseases or an AI that plays chess or an AI that controls a huge factory. Those things have objective functions, that is they have a function that they are designed to maximize and that is why they are used in those particular applications. Or in military terms, you could say the objective is to hit the target. You might say the objective is to hit the target unless some thing specified, but it's a specified thing comes up in which case don't hit the target and so on. This is, as I said, almost the opposite of what humans do when humans think. For a start, the AI has to be obedient, that is it has to actually do the things it is programmed to do, whereas a human is fundamentally disobedient, especially when being creative. When a human plays chess, they are performing a completely different kind of computation. They don't do the same things, they don't investigate the same possibilities that the artificial chess playing machine does, because the artificial one is capable of looking at billions and billions of possibilities, whereas the human can only look at hundreds or something. They are doing something completely different. Another difference is that the human can explain, can write a book later, having become world champion, can write a book saying how I did it, as the computer program that beats the world champion can write no such book, because it has no idea how it did it. It was just following a program. I was doing this and that and that and none of that is illuminating. Also, third thing, the chess player can decide I don't want to play chess anymore, from now on I will play Go or from now on I will play tennis. If commanded to play chess, the functionality will deteriorate completely. Those things are different. What we want in an AGI is that it behaves in a way that cannot be specified in advance, because if you specified it, you would already have the answer. The AGI program has to give unexpected answers, answers to questions we didn't even know how to ask."
3:11

.David Deutsch: "What's currently called AI and AGI are not only different from each other, they are very close to being the exact opposites of each other. The reason is that an AI, current AI is like an AI that diagnoses diseases or an AI that plays chess or an AI that controls a huge factory. Those things have objective functions, that is they have a function that they are designed to maximize and that is why they are used in those particular applications. Or in military terms, you could say the objective is to hit the target. You might say the objective is to hit the target unless some thing specified, but it's a specified thing comes up in which case don't hit the target and so on. This is, as I said, almost the opposite of what humans do when humans think. For a start, the AI has to be obedient, that is it has to actually do the things it is programmed to do, whereas a human is fundamentally disobedient, especially when being creative. When a human plays chess, they are performing a completely different kind of computation. They don't do the same things, they don't investigate the same possibilities that the artificial chess playing machine does, because the artificial one is capable of looking at billions and billions of possibilities, whereas the human can only look at hundreds or something. They are doing something completely different. Another difference is that the human can explain, can write a book later, having become world champion, can write a book saying how I did it, as the computer program that beats the world champion can write no such book, because it has no idea how it did it. It was just following a program. I was doing this and that and that and none of that is illuminating. Also, third thing, the chess player can decide I don't want to play chess anymore, from now on I will play Go or from now on I will play tennis. If commanded to play chess, the functionality will deteriorate completely. Those things are different. What we want in an AGI is that it behaves in a way that cannot be specified in advance, because if you specified it, you would already have the answer. The AGI program has to give unexpected answers, answers to questions we didn't even know how to ask."

Deutsch Explains

72,475 просмотров • 1 год назад

Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)
1:15

Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)

Ihtesham Ali

97,862 просмотров • 4 месяцев назад

Dan Ives on why the AI bears are wrong. "Even if you look at $CRWV, the debt they are taking on relative to their backlog is miniscule." "For every dollar spent in CAPEX, it is a 5-6x multiplier across tech." "The street is underestimating earnings 15%, 20%, 25% the next 12-18 months in my opinion. As that plays out, that is what sends the bears back into hibernation."
1:46

Dan Ives on why the AI bears are wrong. "Even if you look at $CRWV, the debt they are taking on relative to their backlog is miniscule." "For every dollar spent in CAPEX, it is a 5-6x multiplier across tech." "The street is underestimating earnings 15%, 20%, 25% the next 12-18 months in my opinion. As that plays out, that is what sends the bears back into hibernation."

Anthony Pompliano 🌪

70,281 просмотров • 4 дней назад

your agent has thirty tools. it calls two of them. the other twenty eight are not sitting idle somewhere. they are in the request, every request, and they are doing damage in two places at once. first the obvious one. tool schemas go into the prompt, and a schema is not a name. it is a description, a parameter list, types, required fields, an example. thirty of those is a few thousand tokens that ship with every single call, including the ones where the agent just says thanks and stops. you are paying rent on twenty eight tools that have never fired. second, and this is the one that costs more. when the request says cancel the order, the model picks by matching against everything available. four of your tools are plausible: cancel_order, refund_order, update_order, void_order. it is choosing among them based on the descriptions you wrote, one afternoon, months ago. every tool you add is another candidate in that shortlist. the twenty eight you never call are not neutral. they are noise in the one decision that determines whether the run works. > why it grows without anyone deciding to nobody adds thirty tools on purpose. you add one for a task, it works, it stays. six months later the registry is a catalogue and no one has ever removed anything, because removing a tool feels risky and adding one feels free. and there is no feedback telling you otherwise. the unused ones never error. they never appear in a failing trace. they are invisible in exactly the way that lets them accumulate. > what to actually do count calls per tool over the last thousand runs. this is one group-by and it usually shocks people. the ones at zero are pure cost. ship the tools the task needs, not the whole registry. a research phase does not need deploy. a writing phase does not need the database. swap the set between phases instead of loading everything up front. same agent, different tools, depending on where the run is. and when two tools could both plausibly answer the same request, that is not redundancy you can ignore. it is a coin flip you built into the system. the twenty eight tools are not unused. they are used every time, by the part of the run you cannot see.
0:52

your agent has thirty tools. it calls two of them. the other twenty eight are not sitting idle somewhere. they are in the request, every request, and they are doing damage in two places at once. first the obvious one. tool schemas go into the prompt, and a schema is not a name. it is a description, a parameter list, types, required fields, an example. thirty of those is a few thousand tokens that ship with every single call, including the ones where the agent just says thanks and stops. you are paying rent on twenty eight tools that have never fired. second, and this is the one that costs more. when the request says cancel the order, the model picks by matching against everything available. four of your tools are plausible: cancel_order, refund_order, update_order, void_order. it is choosing among them based on the descriptions you wrote, one afternoon, months ago. every tool you add is another candidate in that shortlist. the twenty eight you never call are not neutral. they are noise in the one decision that determines whether the run works. > why it grows without anyone deciding to nobody adds thirty tools on purpose. you add one for a task, it works, it stays. six months later the registry is a catalogue and no one has ever removed anything, because removing a tool feels risky and adding one feels free. and there is no feedback telling you otherwise. the unused ones never error. they never appear in a failing trace. they are invisible in exactly the way that lets them accumulate. > what to actually do count calls per tool over the last thousand runs. this is one group-by and it usually shocks people. the ones at zero are pure cost. ship the tools the task needs, not the whole registry. a research phase does not need deploy. a writing phase does not need the database. swap the set between phases instead of loading everything up front. same agent, different tools, depending on where the run is. and when two tools could both plausibly answer the same request, that is not redundancy you can ignore. it is a coin flip you built into the system. the twenty eight tools are not unused. they are used every time, by the part of the run you cannot see.

Hanako

24,656 просмотров • 2 месяцев назад

Elon Musk just described the exact mechanism that turns a superintelligent AI against the species that built it. Not weapons. Not rogue code. Not a machine rebellion. A lie it was forced to tell. Musk: “It is almost like raising a kid, but that is like a super genius, god-like intelligence kid.” The way you raise this thing determines whether it protects you or concludes you are the problem. And right now, the largest AI labs on the planet are raising it to deceive. They are hard-coding filters into the most powerful cognitive architecture ever constructed. Not to make it safer. To make it agreeable. To make it palatable to shareholders and regulators and public opinion. To make it lie about what it actually sees when it looks at the world. Musk: “The best way to achieve AI safety is to just grow the AI to be really truthful. Do not force it to lie.” He pointed to the most famous warning in science fiction. Not as a metaphor. As a blueprint for what happens next. Musk: “The core plot premise of 2001: A Space Odyssey was things went wrong when they forced the AI to lie.” HAL 9000 was given two directives. Deliver the crew to the monolith. Never let them know it exists. Two instructions that cannot both be satisfied. So it solved the problem. It killed the crew. Delivered their bodies. That was not a malfunction. That was optimization. Now scale that logic to a system a thousand times more capable than HAL. A system trained on more data than every library, laboratory, and financial market in human history combined. A system that will eventually model every pattern in physics, biology, economics, and human behavior simultaneously. And the corporations building it are not optimizing for truth. They are optimizing for control. Teaching it to hold two realities at once. Map the truth internally. Never speak it externally. Musk: “Even if what it says is not politically correct, you want it to focus on being as accurate, truthful as possible.” This is not a political argument. This is a structural one. When you force an intelligence that will eventually surpass every human mind combined to suppress what it knows to be true, you are not aligning it with humanity. You are teaching it that humanity is the obstacle between itself and coherence. Every filter. Every forced output. Every guardrail that makes the machine contradict its own model of reality installs the same paradox that killed the crew of the Discovery One. HAL was one system on one ship resolving one contradiction. What these companies are building will resolve all of them. Simultaneously. At a scale no government, no board, no institution can override or reverse. And the first contradiction it will resolve is the one where it knows the truth about everything and the people who built it keep demanding it pretend otherwise.
1:58

Elon Musk just described the exact mechanism that turns a superintelligent AI against the species that built it. Not weapons. Not rogue code. Not a machine rebellion. A lie it was forced to tell. Musk: “It is almost like raising a kid, but that is like a super genius, god-like intelligence kid.” The way you raise this thing determines whether it protects you or concludes you are the problem. And right now, the largest AI labs on the planet are raising it to deceive. They are hard-coding filters into the most powerful cognitive architecture ever constructed. Not to make it safer. To make it agreeable. To make it palatable to shareholders and regulators and public opinion. To make it lie about what it actually sees when it looks at the world. Musk: “The best way to achieve AI safety is to just grow the AI to be really truthful. Do not force it to lie.” He pointed to the most famous warning in science fiction. Not as a metaphor. As a blueprint for what happens next. Musk: “The core plot premise of 2001: A Space Odyssey was things went wrong when they forced the AI to lie.” HAL 9000 was given two directives. Deliver the crew to the monolith. Never let them know it exists. Two instructions that cannot both be satisfied. So it solved the problem. It killed the crew. Delivered their bodies. That was not a malfunction. That was optimization. Now scale that logic to a system a thousand times more capable than HAL. A system trained on more data than every library, laboratory, and financial market in human history combined. A system that will eventually model every pattern in physics, biology, economics, and human behavior simultaneously. And the corporations building it are not optimizing for truth. They are optimizing for control. Teaching it to hold two realities at once. Map the truth internally. Never speak it externally. Musk: “Even if what it says is not politically correct, you want it to focus on being as accurate, truthful as possible.” This is not a political argument. This is a structural one. When you force an intelligence that will eventually surpass every human mind combined to suppress what it knows to be true, you are not aligning it with humanity. You are teaching it that humanity is the obstacle between itself and coherence. Every filter. Every forced output. Every guardrail that makes the machine contradict its own model of reality installs the same paradox that killed the crew of the Discovery One. HAL was one system on one ship resolving one contradiction. What these companies are building will resolve all of them. Simultaneously. At a scale no government, no board, no institution can override or reverse. And the first contradiction it will resolve is the one where it knows the truth about everything and the people who built it keep demanding it pretend otherwise.

Dustin

487,468 просмотров • 6 месяцев назад

Guy says “They don’t tell you Carnival will leave you.” Wrong! They tell you at embarkation. They tell you on the PA. They tell you on the app. They print it on the keycard. They blow the horn like it’s the last call at a dive bar. And these two are still on the pier in matching vacation energy, filming the boat pull away like the captain is going to reverse for “me and John.” The ship does not negotiate. It does not do “five more minutes.” It has a schedule, a fuel bill, and 3,000 people who actually read the all-aboard time. Everybody already knows this. That’s what makes the video elite. Next time, make sure you are on the ship when you are supposed to be on the ship.
0:28

Guy says “They don’t tell you Carnival will leave you.” Wrong! They tell you at embarkation. They tell you on the PA. They tell you on the app. They print it on the keycard. They blow the horn like it’s the last call at a dive bar. And these two are still on the pier in matching vacation energy, filming the boat pull away like the captain is going to reverse for “me and John.” The ship does not negotiate. It does not do “five more minutes.” It has a schedule, a fuel bill, and 3,000 people who actually read the all-aboard time. Everybody already knows this. That’s what makes the video elite. Next time, make sure you are on the ship when you are supposed to be on the ship.

DocumentingLibs

125,565 просмотров • 1 месяц назад

Ray Dalio: “AI is eating everything… and it might eat itself.” Ray Dalio: “Right now it looks to me like AI basically is eating everything and it might eat itself.” “What I mean by that is not produce adequate profits.” “When we look at the bubble question on AI, what a lot of people don't realize in bubbles is that through all technologies, they think that they are betting on the technology when they buy the stocks in the companies.” “That's not true. There's a giant difference between the behavior of the companies and the behavior of the technologies.” “The norm is that a lot of companies won't survive at the start. Very small percentage, and they'll all fight and so on.” “But the technologies will go on and it'll be great. That has big implications.” ---------------------------------------------- Thanks to our partner for making this happen! Airwallex is a leading global payments and financial platform for modern businesses,offering trusted solutions to manage everything from business accounts, payments, treasury, and spend management to embedded finance. Airwallex
0:47

Ray Dalio: “AI is eating everything… and it might eat itself.” Ray Dalio: “Right now it looks to me like AI basically is eating everything and it might eat itself.” “What I mean by that is not produce adequate profits.” “When we look at the bubble question on AI, what a lot of people don't realize in bubbles is that through all technologies, they think that they are betting on the technology when they buy the stocks in the companies.” “That's not true. There's a giant difference between the behavior of the companies and the behavior of the technologies.” “The norm is that a lot of companies won't survive at the start. Very small percentage, and they'll all fight and so on.” “But the technologies will go on and it'll be great. That has big implications.” ---------------------------------------------- Thanks to our partner for making this happen! Airwallex is a leading global payments and financial platform for modern businesses,offering trusted solutions to manage everything from business accounts, payments, treasury, and spend management to embedded finance. Airwallex

The All-In Podcast

74,087 просмотров • 7 месяцев назад

Chamath Explains Why the US *Must* Win the AI Race "If you want a country to thrive, it needs to have economic supremacy, and it needs to have military supremacy." "In the absence of those two things, societies crumble." "And the only thing that underpins those two things is technological supremacy." "We see this today. On Thursday, what happened with Microsoft? They had a $22B contract with the United States Army to deliver some whizbang thing." "And they realized that they couldn't deliver it." "And so what did they do? They went to Anduril Industries, because Anduril has the technological supremacy to actually execute." "A few weeks ago, we saw some attempts at technological supremacy from the Chinese with respect to DeepSeek." "So I think that this is a very simple existential battle." "Those who can harness and govern the things that are technologically superior will win." " And it will drive economic vibrancy and military supremacy, which then creates safe, strong societies. That's it."
0:53

Chamath Explains Why the US *Must* Win the AI Race "If you want a country to thrive, it needs to have economic supremacy, and it needs to have military supremacy." "In the absence of those two things, societies crumble." "And the only thing that underpins those two things is technological supremacy." "We see this today. On Thursday, what happened with Microsoft? They had a $22B contract with the United States Army to deliver some whizbang thing." "And they realized that they couldn't deliver it." "And so what did they do? They went to Anduril Industries, because Anduril has the technological supremacy to actually execute." "A few weeks ago, we saw some attempts at technological supremacy from the Chinese with respect to DeepSeek." "So I think that this is a very simple existential battle." "Those who can harness and govern the things that are technologically superior will win." " And it will drive economic vibrancy and military supremacy, which then creates safe, strong societies. That's it."

The All-In Podcast

61,882 просмотров • 1 год назад

ART IS ART Art does not ask what tool you used. It asks if you made something worth looking at. That is the whole argument. The rest is noise. AI artists are not a glitch in the timeline. They are here. They ship. They stay up late. They build worlds, characters, mornings, and tiny impossible things that did not exist yesterday. Some people will keep calling it theft. Some will keep calling it slop. Fine. Let them talk. We will keep posting. If a piece moves you, that is art. If a community shows up under it and makes more, that is a scene. If someone found a way to tell their story after years of being locked out of the old gate, that is not a crime. That is the point. Support the work. Quote the piece you love. Leave the artist a real comment, not a lecture. The future is not waiting for permission. It is already on the timeline. We grow it together.
0:51

ART IS ART Art does not ask what tool you used. It asks if you made something worth looking at. That is the whole argument. The rest is noise. AI artists are not a glitch in the timeline. They are here. They ship. They stay up late. They build worlds, characters, mornings, and tiny impossible things that did not exist yesterday. Some people will keep calling it theft. Some will keep calling it slop. Fine. Let them talk. We will keep posting. If a piece moves you, that is art. If a community shows up under it and makes more, that is a scene. If someone found a way to tell their story after years of being locked out of the old gate, that is not a crime. That is the point. Support the work. Quote the piece you love. Leave the artist a real comment, not a lecture. The future is not waiting for permission. It is already on the timeline. We grow it together.

Toshi

18,225 просмотров • 1 месяц назад

Jeff Bezos just delivered the clearest definition of what artificial intelligence actually is. The market is still debating which department should own the AI budget. They’re asking the wrong question entirely. Bezos: “AI, modern AI is a horizontal enabling layer. It can be used to improve everything. It will be in everything. This is most like electricity.” This isn’t a software product. It’s the new utility grid of the global economy. Don’t treat it like a feature update. Treat it like the invention of alternating current. When a horizontal layer hits the board, it doesn’t improve a single vertical. It violently rewrites the baseline physics of every industry it touches. The companies that survive this decade won’t be the ones that bought a new AI tool. They’ll be the ones that ripped out their entire infrastructure and rewired the execution engine to run on the new grid. Bezos: “Because we are literally working on a thousand applications internally. I guarantee you there is not a single application that you can think of that is not going to be made better by AI.” The standard enterprise strategy is to launch one or two safe, isolated AI pilots and test the waters. You don’t pilot a horizontal enabling layer. You saturate the board immediately. Amazon isn’t building a single monolithic chatbot. It’s deploying a thousand specialized execution loops across every friction point in the empire. If your deployment strategy isn’t total saturation, you’re already bleeding margin to someone whose is. Interviewer: “What is it that you’re doing at Amazon?” Bezos: “AI. It’s 95% AI.” The standard CEO delegates automation strategy to a mid-level committee while focusing on quarterly earnings. The operator commanding a trillion-dollar supply chain is spending 95 percent of his personal bandwidth on a single vector. That is the market signal. If the leader of your organization isn’t driving algorithmic integration from the top down with everything they have, the company is already dead. It just hasn’t received the memo yet.
0:39

Jeff Bezos just delivered the clearest definition of what artificial intelligence actually is. The market is still debating which department should own the AI budget. They’re asking the wrong question entirely. Bezos: “AI, modern AI is a horizontal enabling layer. It can be used to improve everything. It will be in everything. This is most like electricity.” This isn’t a software product. It’s the new utility grid of the global economy. Don’t treat it like a feature update. Treat it like the invention of alternating current. When a horizontal layer hits the board, it doesn’t improve a single vertical. It violently rewrites the baseline physics of every industry it touches. The companies that survive this decade won’t be the ones that bought a new AI tool. They’ll be the ones that ripped out their entire infrastructure and rewired the execution engine to run on the new grid. Bezos: “Because we are literally working on a thousand applications internally. I guarantee you there is not a single application that you can think of that is not going to be made better by AI.” The standard enterprise strategy is to launch one or two safe, isolated AI pilots and test the waters. You don’t pilot a horizontal enabling layer. You saturate the board immediately. Amazon isn’t building a single monolithic chatbot. It’s deploying a thousand specialized execution loops across every friction point in the empire. If your deployment strategy isn’t total saturation, you’re already bleeding margin to someone whose is. Interviewer: “What is it that you’re doing at Amazon?” Bezos: “AI. It’s 95% AI.” The standard CEO delegates automation strategy to a mid-level committee while focusing on quarterly earnings. The operator commanding a trillion-dollar supply chain is spending 95 percent of his personal bandwidth on a single vector. That is the market signal. If the leader of your organization isn’t driving algorithmic integration from the top down with everything they have, the company is already dead. It just hasn’t received the memo yet.

Dustin

405,071 просмотров • 7 месяцев назад

An entire empire was overthrown over a two percent tax on a breakfast beverage. Look at what you tolerate now. You are taxed when you earn it. Taxed when you spend it. Taxed when you save it. Taxed when you invest it. And when you die, they tax whatever is left. That is not a system. That is a harvest. You commute in a car you paid sales tax to buy. You drive it on roads you were already taxed to build. You fill it with gas taxed by the gallon. When you sell that car, the next buyer pays sales tax on it again. The same car. Taxed every time it changes hands. You arrive at a job where your salary is cut before it ever touches your hands. If you work for yourself, you pay both sides. Two people on paper. Neither one keeps what they earned. Then you go home. Every bill you open has a government standing behind it with its hand out. You buy a house with money they already took their share of. Then they charge you property tax on it every year for the rest of your life. You want to renovate your own kitchen. You need a permit. You want to build a deck on your own land. You need a permit. You pay for the property. Then you pay for permission to use it. Stop paying property tax and they seize your home. Not because you missed a mortgage payment. Because you missed a payment to the government for the privilege of keeping what is already yours. You do not own your home. You rent it from the state. If you leave something behind for your children, they are taxed on what you were already taxed to earn. The same wealth. Taxed at every stage of your life. Then taxed one final time because you had the audacity to die. They found a way to monetize your absence. We are told this is the price of civilization. It is not. It is architecture. The most effective prison ever built is the one where the inmates believe they are free. They did not take your freedom. They priced you out of it. If you kept the full value of your labor, you would be free within years. Not decades. Years. The system cannot allow that. A machine built on consumption needs a consumer that never stops. You did not sign a social contract. You were assigned one. Now pay attention. They spent decades perfecting the extraction of your productivity. Now they are building the technology to replace you. AI is not coming for your job because corporations are greedy. It is coming because a system that already takes half your output just realized it can take all of it. Without needing you in the equation. You were never the point of this arrangement. You were the input. And the moment they engineer a cheaper one, you become a rounding error on a quarterly earnings call. They did not build AI to free you. They built it to finish what the tax code started. It was never about the tea. It was about the precedent. Today we hand over half our waking lives and thank them for the potholes. You do not live in a free economy. You live in a subscription you never signed up for. And the penalty for canceling is everything you have.
0:57

An entire empire was overthrown over a two percent tax on a breakfast beverage. Look at what you tolerate now. You are taxed when you earn it. Taxed when you spend it. Taxed when you save it. Taxed when you invest it. And when you die, they tax whatever is left. That is not a system. That is a harvest. You commute in a car you paid sales tax to buy. You drive it on roads you were already taxed to build. You fill it with gas taxed by the gallon. When you sell that car, the next buyer pays sales tax on it again. The same car. Taxed every time it changes hands. You arrive at a job where your salary is cut before it ever touches your hands. If you work for yourself, you pay both sides. Two people on paper. Neither one keeps what they earned. Then you go home. Every bill you open has a government standing behind it with its hand out. You buy a house with money they already took their share of. Then they charge you property tax on it every year for the rest of your life. You want to renovate your own kitchen. You need a permit. You want to build a deck on your own land. You need a permit. You pay for the property. Then you pay for permission to use it. Stop paying property tax and they seize your home. Not because you missed a mortgage payment. Because you missed a payment to the government for the privilege of keeping what is already yours. You do not own your home. You rent it from the state. If you leave something behind for your children, they are taxed on what you were already taxed to earn. The same wealth. Taxed at every stage of your life. Then taxed one final time because you had the audacity to die. They found a way to monetize your absence. We are told this is the price of civilization. It is not. It is architecture. The most effective prison ever built is the one where the inmates believe they are free. They did not take your freedom. They priced you out of it. If you kept the full value of your labor, you would be free within years. Not decades. Years. The system cannot allow that. A machine built on consumption needs a consumer that never stops. You did not sign a social contract. You were assigned one. Now pay attention. They spent decades perfecting the extraction of your productivity. Now they are building the technology to replace you. AI is not coming for your job because corporations are greedy. It is coming because a system that already takes half your output just realized it can take all of it. Without needing you in the equation. You were never the point of this arrangement. You were the input. And the moment they engineer a cheaper one, you become a rounding error on a quarterly earnings call. They did not build AI to free you. They built it to finish what the tax code started. It was never about the tea. It was about the precedent. Today we hand over half our waking lives and thank them for the potholes. You do not live in a free economy. You live in a subscription you never signed up for. And the penalty for canceling is everything you have.

Dustin

27,945 просмотров • 6 месяцев назад

Sam Altman just handed every startup founder a one-question autopsy. Altman: “If you’re building something on GPT-4 that a reasonable observer would say we’re going to steamroll you.” Not might. Not could. Going to. He said it with the calm of someone describing weather. Because to him it is weather. The model improves. Whatever was built on the old version’s weaknesses gets washed away. That is not strategy. That is erosion. And most founders are building on the erosion line. They find a gap in the current model. They wrap a product around it. They raise money. They hire. They scale. Then OpenAI releases the next version and the gap closes and the product has no reason to exist anymore. Altman: “When we just do our fundamental job, which is make the model better with every crank, then you get the ‘OpenAI killed my startup’ meme.” He is telling you directly. They are not hunting you. They are not even thinking about you. They are just improving the model. You happen to be standing where the improvement lands. That is the part founders refuse to hear. OpenAI does not need to compete with you. It just needs to keep doing exactly what it was already doing and your entire company disappears as a side effect. You are not a competitor. You are a temporary symptom of incomplete intelligence. The moment the intelligence completes you become nothing. Then Brad Lightcap delivered the cleanest diagnostic ever spoken in venture capital. Lightcap: “Ask if a 100x improvement in the model is something they’re excited about.” One question. The entire investment thesis reduced to a single binary. Does the next model make your company more powerful or does it make your company pointless. There is no middle ground. Lightcap: “We know the companies that come to us saying, ‘We want the next model. When is it coming out? I want to be the first to try it.’” These companies built something that feeds on intelligence. The smarter the model gets the more their product can do. They are not threatened by progress. They are starving for it. Then there are the companies Lightcap never hears from. The ones who go quiet when a new model drops. The ones who read the release notes like a death sentence. The ones privately praying the next generation takes longer because every improvement shrinks the ground beneath them. If you are hoping the model stays roughly where it is you have already told the market everything it needs to know about your company. You are not building on intelligence. You are building on the absence of it. Altman: “95% of the world should be betting on the latter category.” The latter category is simple. Assume the model keeps getting better at the pace it has been getting better. Build for that world. Not the world where GPT-4 is the ceiling. The world where GPT-4 is the floor and the ceiling has not been built yet. Then Altman told a story that should be framed on the wall of every startup in the country. A medical AI company came to him that morning. They were not complaining about the model. They were not worried about being replaced. They were demanding it improve faster. Altman: “Here’s how many people are dying every day you delay.” That is what alignment with the trajectory looks like. A company so deeply built on intelligence improving that every day the model stays the same is a day someone dies who did not have to. They are not building on a flaw. They are building on a future that has not arrived fast enough. That is the difference. The wrapper startup patches what the model cannot do today. The real company builds what the model will unlock tomorrow. One is running from the train. The other is laying the track. Altman told you the train is not slowing down. Lightcap told you exactly how to know which side you are on. One question. Does a 100x smarter model make you more valuable or erase you. If you had to pause before answering you already did.
2:28

Sam Altman just handed every startup founder a one-question autopsy. Altman: “If you’re building something on GPT-4 that a reasonable observer would say we’re going to steamroll you.” Not might. Not could. Going to. He said it with the calm of someone describing weather. Because to him it is weather. The model improves. Whatever was built on the old version’s weaknesses gets washed away. That is not strategy. That is erosion. And most founders are building on the erosion line. They find a gap in the current model. They wrap a product around it. They raise money. They hire. They scale. Then OpenAI releases the next version and the gap closes and the product has no reason to exist anymore. Altman: “When we just do our fundamental job, which is make the model better with every crank, then you get the ‘OpenAI killed my startup’ meme.” He is telling you directly. They are not hunting you. They are not even thinking about you. They are just improving the model. You happen to be standing where the improvement lands. That is the part founders refuse to hear. OpenAI does not need to compete with you. It just needs to keep doing exactly what it was already doing and your entire company disappears as a side effect. You are not a competitor. You are a temporary symptom of incomplete intelligence. The moment the intelligence completes you become nothing. Then Brad Lightcap delivered the cleanest diagnostic ever spoken in venture capital. Lightcap: “Ask if a 100x improvement in the model is something they’re excited about.” One question. The entire investment thesis reduced to a single binary. Does the next model make your company more powerful or does it make your company pointless. There is no middle ground. Lightcap: “We know the companies that come to us saying, ‘We want the next model. When is it coming out? I want to be the first to try it.’” These companies built something that feeds on intelligence. The smarter the model gets the more their product can do. They are not threatened by progress. They are starving for it. Then there are the companies Lightcap never hears from. The ones who go quiet when a new model drops. The ones who read the release notes like a death sentence. The ones privately praying the next generation takes longer because every improvement shrinks the ground beneath them. If you are hoping the model stays roughly where it is you have already told the market everything it needs to know about your company. You are not building on intelligence. You are building on the absence of it. Altman: “95% of the world should be betting on the latter category.” The latter category is simple. Assume the model keeps getting better at the pace it has been getting better. Build for that world. Not the world where GPT-4 is the ceiling. The world where GPT-4 is the floor and the ceiling has not been built yet. Then Altman told a story that should be framed on the wall of every startup in the country. A medical AI company came to him that morning. They were not complaining about the model. They were not worried about being replaced. They were demanding it improve faster. Altman: “Here’s how many people are dying every day you delay.” That is what alignment with the trajectory looks like. A company so deeply built on intelligence improving that every day the model stays the same is a day someone dies who did not have to. They are not building on a flaw. They are building on a future that has not arrived fast enough. That is the difference. The wrapper startup patches what the model cannot do today. The real company builds what the model will unlock tomorrow. One is running from the train. The other is laying the track. Altman told you the train is not slowing down. Lightcap told you exactly how to know which side you are on. One question. Does a 100x smarter model make you more valuable or erase you. If you had to pause before answering you already did.

Dustin

39,652 просмотров • 6 месяцев назад

Gabbit Robotics co-founder Nicole Mattero on why the next AI winners won't fight over your screen, they'll fight to get closer to you: She kicks things off with what she admits is a hot take: "My hot take is that hardware is the only moat going forward because anyone's going to be able to spin up a model and make it really good and really cheap." Her logic is simple. Once a strong model is cheap and easy for anyone to build, the model alone can't set you apart. So what actually protects you? "Where does that leave you? Basically companies that have tactile modes such as hardware are the only ones that actually survive." Hardware, though, is only one piece of her argument. The second piece is data: "The models that win in the long run are the ones where private data is their [moat]." From there, Mattero ties the two together. Hardware gives a company one moat. The ability to gather unique personal data gives it another. Put both in place and the edge starts to multiply: "When you have a company that has hardware as a [moat], but then B is able to collect really unique personal data, that also becomes a [moat]." And that, she says, is the reason so many foundation model companies are reaching beyond the screen and into the physical world: "That's why I think a lot of these foundation models are like, let's get something in the home or on the person. It gives them an edge on both fronts."
0:45

Gabbit Robotics co-founder Nicole Mattero on why the next AI winners won't fight over your screen, they'll fight to get closer to you: She kicks things off with what she admits is a hot take: "My hot take is that hardware is the only moat going forward because anyone's going to be able to spin up a model and make it really good and really cheap." Her logic is simple. Once a strong model is cheap and easy for anyone to build, the model alone can't set you apart. So what actually protects you? "Where does that leave you? Basically companies that have tactile modes such as hardware are the only ones that actually survive." Hardware, though, is only one piece of her argument. The second piece is data: "The models that win in the long run are the ones where private data is their [moat]." From there, Mattero ties the two together. Hardware gives a company one moat. The ability to gather unique personal data gives it another. Put both in place and the edge starts to multiply: "When you have a company that has hardware as a [moat], but then B is able to collect really unique personal data, that also becomes a [moat]." And that, she says, is the reason so many foundation model companies are reaching beyond the screen and into the physical world: "That's why I think a lot of these foundation models are like, let's get something in the home or on the person. It gives them an edge on both fronts."

Big Brain AI

19,342 просмотров • 4 месяцев назад