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Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new... task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."show more

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92,419 次观看 • 7 个月前 •via X (Twitter)

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Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Wide Awake Media

54,156 次观看 • 10 个月前

Kevin Hassett: "The data can't be propaganda. The data has to be something you can trust, because decision-makers throughout the economy trust that these are the data that they can build a factory because they believe, or cut interest rates because they believe. And if the data aren't that good, then it's a real problem for the US."

Kevin Hassett: "The data can't be propaganda. The data has to be something you can trust, because decision-makers throughout the economy trust that these are the data that they can build a factory because they believe, or cut interest rates because they believe. And if the data aren't that good, then it's a real problem for the US."

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It's easy for robots to nail a demo. It's much harder to get them to work reliably in the real world. The bottleneck is data. At Axis Robotics we're building a compounding data engine that combines simulation, ego-centric data and DAgger correction data at scale. Watch our CEO ChrisF(✱,✱) break down the data bottleneck, why messy data can still train good models, and what it takes to get robots out of the lab with blocmates. ⬇️

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Axis Robotics

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$SNOW is becoming data layer for the new agentic AI economy because agents need secure access to proprietary enterprise data and business context before they can do anything useful. That makes Snowflake even more valuable as models get cheaper and more interchangeable because it can bring the best model to the data while AI products like CoCo and CoWork drive a ton more storage, compute and core platform consumption underneath.

$SNOW is becoming data layer for the new agentic AI economy because agents need secure access to proprietary enterprise data and business context before they can do anything useful. That makes Snowflake even more valuable as models get cheaper and more interchangeable because it can bring the best model to the data while AI products like CoCo and CoWork drive a ton more storage, compute and core platform consumption underneath.

Shay Boloor

62,547 次观看 • 7 天前

Larry Ellison—owner of Oracle, CBS, and now TikTok—tells Tony Blair about his plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" his AI models. "We have to take all of this data... and move it into a single, if you will, unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Larry Ellison—owner of Oracle, CBS, and now TikTok—tells Tony Blair about his plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" his AI models. "We have to take all of this data... and move it into a single, if you will, unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

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The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."

The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."

TBPN

85,358 次观看 • 7 个月前

ChatGPT connecting to external data sources offers a glimpse into what AI interoperability looks like. Ask any question in ChatGPT and it can now pull data from Box. The future of AI is AI Agents that can access your systems and data to complete any task for you.

ChatGPT connecting to external data sources offers a glimpse into what AI interoperability looks like. Ask any question in ChatGPT and it can now pull data from Box. The future of AI is AI Agents that can access your systems and data to complete any task for you.

Aaron Levie

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Cowboy CEO Baiju Bhatt on why orbital data centers won't just be a SpaceX monopoly. "We're working on it. I think that if there's enough commercial demand for this, and there are enough dollars flowing to this, I think that capitalism is efficient, right?" "I think that as we look at, at least in the US, data centers are not very popular. They're actually quite unpopular, right? "And the intersection between these massive build outs, as we look out to the amount of capacity that people want—we're still kind of the early innings of this stuff." "The frustration from the US population, I see this probably getting worse before it gets better, which means that if AI wants to eat a ton of energy, it's gonna need some other places to get it." "So I think there is a degree of inevitability, not just to data centers in space, but also the other ways of powering AI." "And I think if there's enough commercial activity, you're gonna see a lot of people try to do it. And like I said, we're also doing it." With Mo Islam at the NYSE 🏛

Cowboy CEO Baiju Bhatt on why orbital data centers won't just be a SpaceX monopoly. "We're working on it. I think that if there's enough commercial demand for this, and there are enough dollars flowing to this, I think that capitalism is efficient, right?" "I think that as we look at, at least in the US, data centers are not very popular. They're actually quite unpopular, right? "And the intersection between these massive build outs, as we look out to the amount of capacity that people want—we're still kind of the early innings of this stuff." "The frustration from the US population, I see this probably getting worse before it gets better, which means that if AI wants to eat a ton of energy, it's gonna need some other places to get it." "So I think there is a degree of inevitability, not just to data centers in space, but also the other ways of powering AI." "And I think if there's enough commercial activity, you're gonna see a lot of people try to do it. And like I said, we're also doing it." With Mo Islam at the NYSE 🏛

Jack Kuhr

125,101 次观看 • 14 天前

Why data is a trillion-dollar market "The bigger models scale, the more data you need. Data is a very durable need. If you believe in the scaling of models, then absolutely you should believe in the data market. I believe it's going to be at least a $100BN by 2030, if not $1TRN." Anastasios Nikolas Angelopoulos Do you have to scale into enterprise and mid-market to get this revenue scale or could you do it on frontier lab spend alone? Brendan (can/do) Garrett Lord Jonathan Siddharth Ali Ansari

Why data is a trillion-dollar market "The bigger models scale, the more data you need. Data is a very durable need. If you believe in the scaling of models, then absolutely you should believe in the data market. I believe it's going to be at least a $100BN by 2030, if not $1TRN." Anastasios Nikolas Angelopoulos Do you have to scale into enterprise and mid-market to get this revenue scale or could you do it on frontier lab spend alone? Brendan (can/do) Garrett Lord Jonathan Siddharth Ali Ansari

Harry Stebbings

20,976 次观看 • 1 个月前

President Trump FULLY BACKS American AI, saying working class Americans will benefit HUGELY "I think it's going to create a LOT of jobs, it's going to create success, it's going to create TREMENDOUS innovation." "If I were the mayor of a town or the governor of a state and I had a chance to get a big plant, an AI plant or a data center, I would absolutely want it because the jobs are ENORMOUS and the money paid, the taxes paid are just enormous." "I think it's really creating jobs. And I can tell you one thing. If you look at our construction jobs right now, because we're building more plants, factories, things that really have something to do with the AI, but also car factories, car plants, they're moving into our country."

President Trump FULLY BACKS American AI, saying working class Americans will benefit HUGELY "I think it's going to create a LOT of jobs, it's going to create success, it's going to create TREMENDOUS innovation." "If I were the mayor of a town or the governor of a state and I had a chance to get a big plant, an AI plant or a data center, I would absolutely want it because the jobs are ENORMOUS and the money paid, the taxes paid are just enormous." "I think it's really creating jobs. And I can tell you one thing. If you look at our construction jobs right now, because we're building more plants, factories, things that really have something to do with the AI, but also car factories, car plants, they're moving into our country."

Nick Sortor

53,027 次观看 • 23 天前

MoistCr1TiKaL comes out strongly against data centers in recent video and ponders if they're making people sicker due to hoarding clean water "AI data centers are actually the boogeyman..a scourge on this planet and a stain on civilized society..no normal ppl with functioning frontal lobes want data centers in their community, it is bad for them" "I've been getting conspiratorial..I was reading something that someone was sharing about like when a AI data center came through, and the impact it had on their water, cause that's a very common thing" "I'm not fully bought into it..but it's an interesting theory..what if the reason there's been seemingly so much more sickness, like it feels like ppl are getting sick more frequently now and a wide variety of illnesses just seem more common..what if AI data centers are hoarding all of the clean water..what if all the clean water is being diverted to the AI data centers and the normal civilians are just getting the sludge runoff..I don't think it's out of the realm of possibility..they are evil enough to do that" (Via penguinz0 on YT)

MoistCr1TiKaL comes out strongly against data centers in recent video and ponders if they're making people sicker due to hoarding clean water "AI data centers are actually the boogeyman..a scourge on this planet and a stain on civilized society..no normal ppl with functioning frontal lobes want data centers in their community, it is bad for them" "I've been getting conspiratorial..I was reading something that someone was sharing about like when a AI data center came through, and the impact it had on their water, cause that's a very common thing" "I'm not fully bought into it..but it's an interesting theory..what if the reason there's been seemingly so much more sickness, like it feels like ppl are getting sick more frequently now and a wide variety of illnesses just seem more common..what if AI data centers are hoarding all of the clean water..what if all the clean water is being diverted to the AI data centers and the normal civilians are just getting the sludge runoff..I don't think it's out of the realm of possibility..they are evil enough to do that" (Via penguinz0 on YT)

yeet

3,040,241 次观看 • 1 个月前

Cognition founder Scott Wu says most software never gets built because the economics don't work: "The math only really works out to create software if it's going to be used at least a million times or something. Maybe it's 10,000 times or whatever." "You need that software to be used enough times or to create enough value for that to be worth it." "There are so many things out there which only need to be used a few times or even only need to be used once." "I'm going to go look through these 15 LinkedIn profiles. Or fill out these forms. Or do this data analysis and put this Excel sheet together—all of these things are things that could be done with software." "It obviously doesn't make sense to hire a whole team of people to go make you that piece of software which you're going to use one time and never again—versus just having the human go and do that themselves."

Cognition founder Scott Wu says most software never gets built because the economics don't work: "The math only really works out to create software if it's going to be used at least a million times or something. Maybe it's 10,000 times or whatever." "You need that software to be used enough times or to create enough value for that to be worth it." "There are so many things out there which only need to be used a few times or even only need to be used once." "I'm going to go look through these 15 LinkedIn profiles. Or fill out these forms. Or do this data analysis and put this Excel sheet together—all of these things are things that could be done with software." "It obviously doesn't make sense to hire a whole team of people to go make you that piece of software which you're going to use one time and never again—versus just having the human go and do that themselves."

David Senra

37,919 次观看 • 2 个月前

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."

Patrick OShaughnessy

394,097 次观看 • 7 个月前

Hunt: So, who’s right on data centers? President Trump or Governor Shapiro? Fetterman: Oh, the president is actually right overall. I mean, you know, uh, we we used to be moving forward on data centers and now, I mean, he's actually scared by the polls. So I mean, what really what's really motivated by more than just the polls overall, you know, I set out I set up my message on it on data centers just a couple days on X where I'm saying it's, you know, the president happens to be right in this. You know, I think it's it's very necessary that we win the war on AI. Uh, we, I think it's necessary because AI is going to transform our world. And, you know, it can be either us be the leaders or it can be the Chinese. And we could we could live under those rules and they're going to build unlimited data centers. And they're not really, uh, really stopped by with the kind of like democracy and the kinds of rules that, that we have overall, I think it's important. And I would remind people, like a lot of the opposition is, is been driven by the ccp and a lot of a lot of influence. So I think now withwith data centers, uh, also there's the facts now that it, it consumes a very, very, maybe one tenth of 1%, you know, compared to almonds and compared to other other things, part of our economy and also our land usage as well, too. Pennsylvania, you know, we are energy rich, we are water rich. And we have we're land rich now. So I think it's an important part of our economy, not just for Pennsylvania's but also the country right now.

Hunt: So, who’s right on data centers? President Trump or Governor Shapiro? Fetterman: Oh, the president is actually right overall. I mean, you know, uh, we we used to be moving forward on data centers and now, I mean, he's actually scared by the polls. So I mean, what really what's really motivated by more than just the polls overall, you know, I set out I set up my message on it on data centers just a couple days on X where I'm saying it's, you know, the president happens to be right in this. You know, I think it's it's very necessary that we win the war on AI. Uh, we, I think it's necessary because AI is going to transform our world. And, you know, it can be either us be the leaders or it can be the Chinese. And we could we could live under those rules and they're going to build unlimited data centers. And they're not really, uh, really stopped by with the kind of like democracy and the kinds of rules that, that we have overall, I think it's important. And I would remind people, like a lot of the opposition is, is been driven by the ccp and a lot of a lot of influence. So I think now withwith data centers, uh, also there's the facts now that it, it consumes a very, very, maybe one tenth of 1%, you know, compared to almonds and compared to other other things, part of our economy and also our land usage as well, too. Pennsylvania, you know, we are energy rich, we are water rich. And we have we're land rich now. So I think it's an important part of our economy, not just for Pennsylvania's but also the country right now.

Acyn

204,278 次观看 • 9 天前

Elon Musk On What It Takes To Build A Competitive AI Model Elon Musk breaks down the three factors that decide whether a foundation model can compete, and why the next frontier isn't human data at all. Speaking with Garry Tan, President and CEO of Y Combinator, Elon lays out what's actually required to build a large foundation model that's competitive: "You've got to get a lot of GPUs and have them train coherently and stably. Then it's like, what unique access to data do you have? I guess distribution matters to some degree as well like, how do people get exposed to your AI? Those are critical factors." But there's a problem with the data part of that equation. Echoing what a friend in the field has said, Elon explains that the industry has essentially run out of human-generated pre-training data: "You run out of tokens pretty fast, certainly of high-quality tokens. And then you need to essentially create synthetic data, and be able to accurately judge the synthetic data that you're creating, to verify: is this real synthetic data, or is it a hallucination that doesn't actually match reality?" That verification step is the hard part: "Achieving grounding in reality is tricky. But we are at the stage where there's more effort put into synthetic data. Right now we're training Grok 3.5, which is a heavy focus on reasoning." On reasoning, Garry Tan adds an interesting detail from researchers he's spoken to: hard science, particularly physics textbooks is very useful for training reasoning, whereas social science is "totally useless" for it. Elon's response: "Yes, that's probably true." He then points to where all of this is heading: "Something that's going to be very important in the future is combining deep AI in the data center or supercluster with robotics. So, things like the Optimus humanoid robot."

Elon Musk On What It Takes To Build A Competitive AI Model Elon Musk breaks down the three factors that decide whether a foundation model can compete, and why the next frontier isn't human data at all. Speaking with Garry Tan, President and CEO of Y Combinator, Elon lays out what's actually required to build a large foundation model that's competitive: "You've got to get a lot of GPUs and have them train coherently and stably. Then it's like, what unique access to data do you have? I guess distribution matters to some degree as well like, how do people get exposed to your AI? Those are critical factors." But there's a problem with the data part of that equation. Echoing what a friend in the field has said, Elon explains that the industry has essentially run out of human-generated pre-training data: "You run out of tokens pretty fast, certainly of high-quality tokens. And then you need to essentially create synthetic data, and be able to accurately judge the synthetic data that you're creating, to verify: is this real synthetic data, or is it a hallucination that doesn't actually match reality?" That verification step is the hard part: "Achieving grounding in reality is tricky. But we are at the stage where there's more effort put into synthetic data. Right now we're training Grok 3.5, which is a heavy focus on reasoning." On reasoning, Garry Tan adds an interesting detail from researchers he's spoken to: hard science, particularly physics textbooks is very useful for training reasoning, whereas social science is "totally useless" for it. Elon's response: "Yes, that's probably true." He then points to where all of this is heading: "Something that's going to be very important in the future is combining deep AI in the data center or supercluster with robotics. So, things like the Optimus humanoid robot."

High Signal AI

21,733 次观看 • 1 个月前

AI that had human-level intelligence would actually be way above human level in capability. Obviously, if we trained a human-level AI, we could just run way more instances of it in parallel. This is a huge advantage. But it's not the only one. Right now, LLMs are much less sample-efficient than humans, but they can process each individual piece of data much more quickly. A model that was a human-level learner would be able to gain the equivalent of thousands of years of education. AIs can also be way more task-oriented than humans. Even a really well-motivated human worker can only operate with ‘human-level’ effort for relatively short spans of time. AGI could do it continuously.

AI that had human-level intelligence would actually be way above human level in capability. Obviously, if we trained a human-level AI, we could just run way more instances of it in parallel. This is a huge advantage. But it's not the only one. Right now, LLMs are much less sample-efficient than humans, but they can process each individual piece of data much more quickly. A model that was a human-level learner would be able to gain the equivalent of thousands of years of education. AIs can also be way more task-oriented than humans. Even a really well-motivated human worker can only operate with ‘human-level’ effort for relatively short spans of time. AGI could do it continuously.

Dwarkesh Patel

31,861 次观看 • 4 个月前

James Fishback says Byron Donalds is LYING about data centers! Says this has nothing to do with China and it’s all about GREED. “There are 4,200 data centers in the United States. There are 470 in China. We've already won the AI war. My grandfather was a computer science major at Florida Atlantic University about 30 minutes south of here in Boca. And he would tell you that in the 1970s and 80s, computers were the size of Costco warehouses and now those same computers fit in our pocket. Why are we building an AI data center that in five years is going to be obsolete, is going to be one one hundredth of the size. And so I fear it's because a lot of the big land developers want to buy up cheap land, flip it for the next AI data center. So it's a greedy thing. It's a greed thing. And I don't want that to happen in our state because you know what? If you buy something and you screw up, you can return it. You could sell it. If you pave over citrus groves or the Everglades or a wetlands to build the data center, you cannot easily undo that. And so I don't want our farmers, I don't want our rural towns to be run roughshod over. My opponent, Byron Donalds, will say, well, what about the AI data center? Don't they have property rights? And I would say to him, what about the property rights of the people who live a hundred feet from the data center? Where there is EMF, where there is audible noise pollution, where there is the potential even for small radioactive waves? What happens in that case? Do they not have property rights? They don't have to live next to something like that.”

James Fishback says Byron Donalds is LYING about data centers! Says this has nothing to do with China and it’s all about GREED. “There are 4,200 data centers in the United States. There are 470 in China. We've already won the AI war. My grandfather was a computer science major at Florida Atlantic University about 30 minutes south of here in Boca. And he would tell you that in the 1970s and 80s, computers were the size of Costco warehouses and now those same computers fit in our pocket. Why are we building an AI data center that in five years is going to be obsolete, is going to be one one hundredth of the size. And so I fear it's because a lot of the big land developers want to buy up cheap land, flip it for the next AI data center. So it's a greedy thing. It's a greed thing. And I don't want that to happen in our state because you know what? If you buy something and you screw up, you can return it. You could sell it. If you pave over citrus groves or the Everglades or a wetlands to build the data center, you cannot easily undo that. And so I don't want our farmers, I don't want our rural towns to be run roughshod over. My opponent, Byron Donalds, will say, well, what about the AI data center? Don't they have property rights? And I would say to him, what about the property rights of the people who live a hundred feet from the data center? Where there is EMF, where there is audible noise pollution, where there is the potential even for small radioactive waves? What happens in that case? Do they not have property rights? They don't have to live next to something like that.”

Chris Nelson 🏝️🇺🇸

27,295 次观看 • 1 个月前

#WATCH | Delhi: On the #CasteCensus, author and Advaita teacher Acharya Prashant says, "Any action depends on the intention. Caste census means collecting more data, authentic data and data in itself are neither forward nor backwards, good nor bad. It depends on what you want to what you intend to do with the data. If you want to identify the marginalised groups in a better way, so that more accurate and more efficient policy making can happen, then it's a good step, a forward step... On the other hand, if you use this data to cement cost identification further, then obviously it's a step backwards. If you use this data for electoral gains by targeting particular caste groups concentrated in particular electorates or geographies, then obviously it's a backwards thing..."

#WATCH | Delhi: On the #CasteCensus, author and Advaita teacher Acharya Prashant says, "Any action depends on the intention. Caste census means collecting more data, authentic data and data in itself are neither forward nor backwards, good nor bad. It depends on what you want to what you intend to do with the data. If you want to identify the marginalised groups in a better way, so that more accurate and more efficient policy making can happen, then it's a good step, a forward step... On the other hand, if you use this data to cement cost identification further, then obviously it's a step backwards. If you use this data for electoral gains by targeting particular caste groups concentrated in particular electorates or geographies, then obviously it's a backwards thing..."

ANI

82,457 次观看 • 1 年前

🚨 JUST IN: JD Vance helps to clarify President Trump's stance on data centers, highlighting his desire for data centers to put power BACK into the grid "I think probably 99% of the backlash to data centers has come in areas where building a data center means higher utility and higher electricity for the people on the ground." "If you build a data center, you should be putting power back into the grid, not taking it out, and if that is happening, I don't think the data centers are that controversial."

🚨 JUST IN: JD Vance helps to clarify President Trump's stance on data centers, highlighting his desire for data centers to put power BACK into the grid "I think probably 99% of the backlash to data centers has come in areas where building a data center means higher utility and higher electricity for the people on the ground." "If you build a data center, you should be putting power back into the grid, not taking it out, and if that is happening, I don't think the data centers are that controversial."

Nick Sortor

217,808 次观看 • 11 天前

Dara (CEO of Uber) on their AI spend: "We blew through our AI budget in a quarter, for the whole year. It is forcing us to adjust. We are going to meter headcount increases because to the extent that my engineers are getting much more efficient, their throughput is increasing. There's a cost to that, and it's a significant cost. AI adoption has been occurring in all parts of the business –– whether it's engineers and how they scope projects, how they build, debugging, platform migrations. I'm pushing the teams to fundamentally use the power of AI to rebuild systems and processes from the bottoms up. I do think it's a combination for us right now of encouraging adoption, but then driving efficiency. We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are open source."

Dara (CEO of Uber) on their AI spend: "We blew through our AI budget in a quarter, for the whole year. It is forcing us to adjust. We are going to meter headcount increases because to the extent that my engineers are getting much more efficient, their throughput is increasing. There's a cost to that, and it's a significant cost. AI adoption has been occurring in all parts of the business –– whether it's engineers and how they scope projects, how they build, debugging, platform migrations. I'm pushing the teams to fundamentally use the power of AI to rebuild systems and processes from the bottoms up. I do think it's a combination for us right now of encouraging adoption, but then driving efficiency. We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are open source."

Patrick OShaughnessy

616,953 次观看 • 3 个月前