The best products are created by people solving a problem for themselves. I wanted a podcast that discussed the biggest news in tech, every week, with amazing analysis and left the politics and ego aside. This is the result and the only show you have to listen to every week. - Instinct Raises $1B at $10B Valuation - AMD Buys Fei-Fei Li's World Labs for $8.2B - Meta Poaches MongoDB's CEO - Oura Pulls IPO - Nubank Eyes $8–12B Monzo Takeover My notes below with @jasonlk, @rodriscoll, and @jaltma 1. It Is BS to Think That Open Source Runs Away With It by Being Cheaper Open-source models will not win on price alone because frontier labs hold structural advantages in compute scale, distribution, and revenue. Closed providers can subsidize lower-tier models aggressively enough to match open-source pricing. Open source will still capture a meaningful share of developer workloads, but frontier labs will compete hard on price to defend their position. 2. Why the Personal Assistant Space Is Very Reminiscent of the AI Coding Space Consumer AI assistants like Instinct represent an "aggregator of aggregators" shift similar to Cursor's impact on coding. Instead of acting as passive chat boxes, these autonomous agents can execute complex, multi-step actions across the live internet. By fundamentally reshaping how consumers interact with software and services, this new paradigm creates room for both startups and incumbents to build enormous value. 3. Tyler Cowen's Prediction for the Future of Venture AI is driving greater variance across venture capital, concentrating returns around an even smaller number of breakout winners. As Tyler Cowen put it, "Variance is gonna go up with AI, and many of you will fail." Traditional compounding strategies are becoming less reliable as capital and value creation cluster around outliers. 4. Why Will We See Many More Neo Lab Acquisitions? Foundation model labs and semiconductor giants like AMD are actively acquiring Neo Labs to expand technical capabilities and defend strategic positions. Few of the roughly 100 existing research labs are likely to survive as independent businesses. Those with differentiated technical assets and specialized domain models could become highly valuable acquisition targets. 5. Seed Round Should Still Be $2M to $3M Despite the hype around $50 million seed rounds, $2 million to $3 million remains the atomic unit of early-stage investing. AI development tools now allow lean founding teams to achieve as much operational progress on $2 million to $3 million as startups required far more capital to achieve a decade ago. Outside capital-intensive research labs, disciplined initial rounds help limit dilution and preserve healthy fund mechanics. 6. Should We Invest in Jev at $10 Billion? Running top-tier frontier models for 10 to 12 hours a day is economically unsustainable for many enterprise workflows. Lower-cost alternatives like Jev, operating at a fraction of the price and up to 100x the speed, are already capturing significant token volume. As compute budgets become more constrained, specialized efficiency models could represent a major venture opportunity. (links in comments)

The best products are created by people solving a problem for themselves. I wanted a podcast that discussed the biggest news in tech, every week, with amazing analysis and left the politics and ego aside. This is the result and the only show you have to listen to every week. - Instinct Raises $1B at $10B Valuation - AMD Buys Fei-Fei Li's World Labs for $8.2B - Meta Poaches MongoDB's CEO - Oura Pulls IPO - Nubank Eyes $8–12B Monzo Takeover My notes below with @jasonlk, @rodriscoll, and @jaltma 1. It Is BS to Think That Open Source Runs Away With It by Being Cheaper Open-source models will not win on price alone because frontier labs hold structural advantages in compute scale, distribution, and revenue. Closed providers can subsidize lower-tier models aggressively enough to match open-source pricing. Open source will still capture a meaningful share of developer workloads, but frontier labs will compete hard on price to defend their position. 2. Why the Personal Assistant Space Is Very Reminiscent of the AI Coding Space Consumer AI assistants like Instinct represent an "aggregator of aggregators" shift similar to Cursor's impact on coding. Instead of acting as passive chat boxes, these autonomous agents can execute complex, multi-step actions across the live internet. By fundamentally reshaping how consumers interact with software and services, this new paradigm creates room for both startups and incumbents to build enormous value. 3. Tyler Cowen's Prediction for the Future of Venture AI is driving greater variance across venture capital, concentrating returns around an even smaller number of breakout winners. As Tyler Cowen put it, "Variance is gonna go up with AI, and many of you will fail." Traditional compounding strategies are becoming less reliable as capital and value creation cluster around outliers. 4. Why Will We See Many More Neo Lab Acquisitions? Foundation model labs and semiconductor giants like AMD are actively acquiring Neo Labs to expand technical capabilities and defend strategic positions. Few of the roughly 100 existing research labs are likely to survive as independent businesses. Those with differentiated technical assets and specialized domain models could become highly valuable acquisition targets. 5. Seed Round Should Still Be $2M to $3M Despite the hype around $50 million seed rounds, $2 million to $3 million remains the atomic unit of early-stage investing. AI development tools now allow lean founding teams to achieve as much operational progress on $2 million to $3 million as startups required far more capital to achieve a decade ago. Outside capital-intensive research labs, disciplined initial rounds help limit dilution and preserve healthy fund mechanics. 6. Should We Invest in Jev at $10 Billion? Running top-tier frontier models for 10 to 12 hours a day is economically unsustainable for many enterprise workflows. Lower-cost alternatives like Jev, operating at a fraction of the price and up to 100x the speed, are already capturing significant token volume. As compute budgets become more constrained, specialized efficiency models could represent a major venture opportunity. (links in comments)

The best products are created by people solving a problem for themselves. I wanted a podcast that discussed the biggest news in tech, every week, with amazing analysis and left the politics and ego aside. This is the result and the only show you have to listen to every week. - Instinct Raises $1B at $10B Valuation - AMD Buys Fei-Fei Li's World Labs for $8.2B - Meta Poaches MongoDB's CEO - Oura Pulls IPO - Nubank Eyes $8–12B Monzo Takeover My notes below with @jasonlk, @rodriscoll, and @jaltma 1. It Is BS to Think That Open Source Runs Away With It by Being Cheaper Open-source models will not win on price alone because frontier labs hold structural advantages in compute scale, distribution, and revenue. Closed providers can subsidize lower-tier models aggressively enough to match open-source pricing. Open source will still capture a meaningful share of developer workloads, but frontier labs will compete hard on price to defend their position. 2. Why the Personal Assistant Space Is Very Reminiscent of the AI Coding Space Consumer AI assistants like Instinct represent an "aggregator of aggregators" shift similar to Cursor's impact on coding. Instead of acting as passive chat boxes, these autonomous agents can execute complex, multi-step actions across the live internet. By fundamentally reshaping how consumers interact with software and services, this new paradigm creates room for both startups and incumbents to build enormous value. 3. Tyler Cowen's Prediction for the Future of Venture AI is driving greater variance across venture capital, concentrating returns around an even smaller number of breakout winners. As Tyler Cowen put it, "Variance is gonna go up with AI, and many of you will fail." Traditional compounding strategies are becoming less reliable as capital and value creation cluster around outliers. 4. Why Will We See Many More Neo Lab Acquisitions? Foundation model labs and semiconductor giants like AMD are actively acquiring Neo Labs to expand technical capabilities and defend strategic positions. Few of the roughly 100 existing research labs are likely to survive as independent businesses. Those with differentiated technical assets and specialized domain models could become highly valuable acquisition targets. 5. Seed Round Should Still Be $2M to $3M Despite the hype around $50 million seed rounds, $2 million to $3 million remains the atomic unit of early-stage investing. AI development tools now allow lean founding teams to achieve as much operational progress on $2 million to $3 million as startups required far more capital to achieve a decade ago. Outside capital-intensive research labs, disciplined initial rounds help limit dilution and preserve healthy fund mechanics. 6. Should We Invest in Jev at $10 Billion? Running top-tier frontier models for 10 to 12 hours a day is economically unsustainable for many enterprise workflows. Lower-cost alternatives like Jev, operating at a fraction of the price and up to 100x the speed, are already capturing significant token volume. As compute budgets become more constrained, specialized efficiency models could represent a major venture opportunity. (links in comments)

The best products are created by people solving a problem for themselves. I wanted a podcast that discussed the biggest news in tech, every week, with amazing analysis and left the politics and ego aside. This is the result and the only show you have to listen to every week. - Instinct Raises $1B at $10B Valuation - AMD Buys Fei-Fei Li's World Labs for $8.2B - Meta Poaches MongoDB's CEO - Oura Pulls IPO - Nubank Eyes $8–12B Monzo Takeover My notes below with @jasonlk, @rodriscoll, and @jaltma 1. It Is BS to Think That Open Source Runs Away With It by Being Cheaper Open-source models will not win on price alone because frontier labs hold structural advantages in compute scale, distribution, and revenue. Closed providers can subsidize lower-tier models aggressively enough to match open-source pricing. Open source will still capture a meaningful share of developer workloads, but frontier labs will compete hard on price to defend their position. 2. Why the Personal Assistant Space Is Very Reminiscent of the AI Coding Space Consumer AI assistants like Instinct represent an "aggregator of aggregators" shift similar to Cursor's impact on coding. Instead of acting as passive chat boxes, these autonomous agents can execute complex, multi-step actions across the live internet. By fundamentally reshaping how consumers interact with software and services, this new paradigm creates room for both startups and incumbents to build enormous value. 3. Tyler Cowen's Prediction for the Future of Venture AI is driving greater variance across venture capital, concentrating returns around an even smaller number of breakout winners. As Tyler Cowen put it, "Variance is gonna go up with AI, and many of you will fail." Traditional compounding strategies are becoming less reliable as capital and value creation cluster around outliers. 4. Why Will We See Many More Neo Lab Acquisitions? Foundation model labs and semiconductor giants like AMD are actively acquiring Neo Labs to expand technical capabilities and defend strategic positions. Few of the roughly 100 existing research labs are likely to survive as independent businesses. Those with differentiated technical assets and specialized domain models could become highly valuable acquisition targets. 5. Seed Round Should Still Be $2M to $3M Despite the hype around $50 million seed rounds, $2 million to $3 million remains the atomic unit of early-stage investing. AI development tools now allow lean founding teams to achieve as much operational progress on $2 million to $3 million as startups required far more capital to achieve a decade ago. Outside capital-intensive research labs, disciplined initial rounds help limit dilution and preserve healthy fund mechanics. 6. Should We Invest in Jev at $10 Billion? Running top-tier frontier models for 10 to 12 hours a day is economically unsustainable for many enterprise workflows. Lower-cost alternatives like Jev, operating at a fraction of the price and up to 100x the speed, are already capturing significant token volume. As compute budgets become more constrained, specialized efficiency models could represent a major venture opportunity. (links in comments)

The best products are created by people solving a problem for themselves. I wanted a podcast that discussed the biggest news in tech, every week, with amazing analysis and left the politics and ego aside. This is the result and the only show you have to listen to every week. - Instinct Raises $1B at $10B Valuation - AMD Buys Fei-Fei Li's World Labs for $8.2B - Meta Poaches MongoDB's CEO - Oura Pulls IPO - Nubank Eyes $8–12B Monzo Takeover My notes below with @jasonlk, @rodriscoll, and @jaltma 1. It Is BS to Think That Open Source Runs Away With It by Being Cheaper Open-source models will not win on price alone because frontier labs hold structural advantages in compute scale, distribution, and revenue. Closed providers can subsidize lower-tier models aggressively enough to match open-source pricing. Open source will still capture a meaningful share of developer workloads, but frontier labs will compete hard on price to defend their position. 2. Why the Personal Assistant Space Is Very Reminiscent of the AI Coding Space Consumer AI assistants like Instinct represent an "aggregator of aggregators" shift similar to Cursor's impact on coding. Instead of acting as passive chat boxes, these autonomous agents can execute complex, multi-step actions across the live internet. By fundamentally reshaping how consumers interact with software and services, this new paradigm creates room for both startups and incumbents to build enormous value. 3. Tyler Cowen's Prediction for the Future of Venture AI is driving greater variance across venture capital, concentrating returns around an even smaller number of breakout winners. As Tyler Cowen put it, "Variance is gonna go up with AI, and many of you will fail." Traditional compounding strategies are becoming less reliable as capital and value creation cluster around outliers. 4. Why Will We See Many More Neo Lab Acquisitions? Foundation model labs and semiconductor giants like AMD are actively acquiring Neo Labs to expand technical capabilities and defend strategic positions. Few of the roughly 100 existing research labs are likely to survive as independent businesses. Those with differentiated technical assets and specialized domain models could become highly valuable acquisition targets. 5. Seed Round Should Still Be $2M to $3M Despite the hype around $50 million seed rounds, $2 million to $3 million remains the atomic unit of early-stage investing. AI development tools now allow lean founding teams to achieve as much operational progress on $2 million to $3 million as startups required far more capital to achieve a decade ago. Outside capital-intensive research labs, disciplined initial rounds help limit dilution and preserve healthy fund mechanics. 6. Should We Invest in Jev at $10 Billion? Running top-tier frontier models for 10 to 12 hours a day is economically unsustainable for many enterprise workflows. Lower-cost alternatives like Jev, operating at a fraction of the price and up to 100x the speed, are already capturing significant token volume. As compute budgets become more constrained, specialized efficiency models could represent a major venture opportunity. (links in comments)

Higgsfield is the most untold story in tech. $1BN in ARR in 18 months. Faster than everyone other than OpenAI and Anthropic. They spend $4M a month on models. They expect this to be $100K per person per month. They have 150 people working in a content machine. They will breed more millionaires than any other company in Kazakh history. For the first time, @alexmashrabov on the journey to $1BN in ARR. (below) 1. The Power of the Immigrant Founder Coming from Uzbekistan, Alex was pushed into competitive programming at age eight as his single path to reach the United States. For international founders, placing top in global competitions serves as the ultimate social elevator, instilling the relentless work ethic required to build breakout companies. 2. My Biggest Lessons in the Journey to Finding Product-Market Fit @higgsfield burned over $10 million of its $16 million seed round chasing hype and narrative rather than product quality. With under $5 million left, the team pivoted to product-led growth, solving camera control for creative directors, which immediately triggered organic hypergrowth without paid ads. 3. The 150-Person Content Team Powering Higgsfield's Billion in ARR Nearly half of Higgsfield's workforce consists of 150 in-house creative professionals producing tutorials, ads, and cinematic projects. Generating 90 minutes of TV-quality AI video requires 100 hours of raw output, proving human taste and curation remain the primary drivers of distribution. 4. We Spend $4 Million per Month on Models Higgsfield spends $4 million monthly on internal model usage, averaging $10,000 per employee so teams can freely vibe code and test workflows. Uncapped inference compute acts as a force multiplier, allowing top talent to discover breakthroughs at maximum velocity. 5. Why Chasing Benchmarks Is Bullshit and the Corporate Misalignment Occurring Public benchmarks have devolved into corporate psyops where lab researchers overfit test data to secure bonuses before job-hopping. Text-to-video benchmarks ignore real production workflows requiring 3,000-word prompts, proving direct customer iteration beats artificial leaderboards. 6. Why Team Sizes Won't Be Impacted as Much as People Think While AI handles over 60% of basic support requests, complex B2B environments cannot eliminate human teams. High product velocity constantly shifts rules and context, requiring smart, coordinated operators across legal and customer success. 7. Americans Are Way More Promiscuous When It Comes to Leaving Companies Silicon Valley workers routinely jump jobs every two years, prioritizing short-term trends over deep commitment. This transactional market gives international hubs an advantage, where cultural loyalty and team stability build compounding technical moats. (links in comments)

🚨 WATCH: Andy Burnham says he will create a "global code" for AI so Britain can "capture the benefits" "We led the world's first industrial revolution from the Midlands and the North of England. So why can't we lead the next one? We can, and we will"

Higgsfield is the most untold story in tech. $1BN in ARR in 18 months. Faster than everyone other than OpenAI and Anthropic. They spend $4M a month on models. They expect this to be $100K per person per month. They have 150 people working in a content machine. They will breed more millionaires than any other company in Kazakh history. For the first time, @alexmashrabov on the journey to $1BN in ARR. (below) 1. The Power of the Immigrant Founder Coming from Uzbekistan, Alex was pushed into competitive programming at age eight as his single path to reach the United States. For international founders, placing top in global competitions serves as the ultimate social elevator, instilling the relentless work ethic required to build breakout companies. 2. My Biggest Lessons in the Journey to Finding Product-Market Fit @higgsfield burned over $10 million of its $16 million seed round chasing hype and narrative rather than product quality. With under $5 million left, the team pivoted to product-led growth, solving camera control for creative directors, which immediately triggered organic hypergrowth without paid ads. 3. The 150-Person Content Team Powering Higgsfield's Billion in ARR Nearly half of Higgsfield's workforce consists of 150 in-house creative professionals producing tutorials, ads, and cinematic projects. Generating 90 minutes of TV-quality AI video requires 100 hours of raw output, proving human taste and curation remain the primary drivers of distribution. 4. We Spend $4 Million per Month on Models Higgsfield spends $4 million monthly on internal model usage, averaging $10,000 per employee so teams can freely vibe code and test workflows. Uncapped inference compute acts as a force multiplier, allowing top talent to discover breakthroughs at maximum velocity. 5. Why Chasing Benchmarks Is Bullshit and the Corporate Misalignment Occurring Public benchmarks have devolved into corporate psyops where lab researchers overfit test data to secure bonuses before job-hopping. Text-to-video benchmarks ignore real production workflows requiring 3,000-word prompts, proving direct customer iteration beats artificial leaderboards. 6. Why Team Sizes Won't Be Impacted as Much as People Think While AI handles over 60% of basic support requests, complex B2B environments cannot eliminate human teams. High product velocity constantly shifts rules and context, requiring smart, coordinated operators across legal and customer success. 7. Americans Are Way More Promiscuous When It Comes to Leaving Companies Silicon Valley workers routinely jump jobs every two years, prioritizing short-term trends over deep commitment. This transactional market gives international hubs an advantage, where cultural loyalty and team stability build compounding technical moats. (links in comments)

Higgsfield is the most untold story in tech. $1BN in ARR in 18 months. Faster than everyone other than OpenAI and Anthropic. They spend $4M a month on models. They expect this to be $100K per person per month. They have 150 people working in a content machine. They will breed more millionaires than any other company in Kazakh history. For the first time, @alexmashrabov on the journey to $1BN in ARR. (below) 1. The Power of the Immigrant Founder Coming from Uzbekistan, Alex was pushed into competitive programming at age eight as his single path to reach the United States. For international founders, placing top in global competitions serves as the ultimate social elevator, instilling the relentless work ethic required to build breakout companies. 2. My Biggest Lessons in the Journey to Finding Product-Market Fit @higgsfield burned over $10 million of its $16 million seed round chasing hype and narrative rather than product quality. With under $5 million left, the team pivoted to product-led growth, solving camera control for creative directors, which immediately triggered organic hypergrowth without paid ads. 3. The 150-Person Content Team Powering Higgsfield's Billion in ARR Nearly half of Higgsfield's workforce consists of 150 in-house creative professionals producing tutorials, ads, and cinematic projects. Generating 90 minutes of TV-quality AI video requires 100 hours of raw output, proving human taste and curation remain the primary drivers of distribution. 4. We Spend $4 Million per Month on Models Higgsfield spends $4 million monthly on internal model usage, averaging $10,000 per employee so teams can freely vibe code and test workflows. Uncapped inference compute acts as a force multiplier, allowing top talent to discover breakthroughs at maximum velocity. 5. Why Chasing Benchmarks Is Bullshit and the Corporate Misalignment Occurring Public benchmarks have devolved into corporate psyops where lab researchers overfit test data to secure bonuses before job-hopping. Text-to-video benchmarks ignore real production workflows requiring 3,000-word prompts, proving direct customer iteration beats artificial leaderboards. 6. Why Team Sizes Won't Be Impacted as Much as People Think While AI handles over 60% of basic support requests, complex B2B environments cannot eliminate human teams. High product velocity constantly shifts rules and context, requiring smart, coordinated operators across legal and customer success. 7. Americans Are Way More Promiscuous When It Comes to Leaving Companies Silicon Valley workers routinely jump jobs every two years, prioritizing short-term trends over deep commitment. This transactional market gives international hubs an advantage, where cultural loyalty and team stability build compounding technical moats. (links in comments)

AMD acquires World Labs for ~$8.2b. But who gets the 💰? My usual breakdown below 👇 From founding to an $8.2b exit in ~2.5 years. A huge value creation event. This is a fantastic exit, especially for the co-founders and the team. Investors will still share ~$2.3b of profits on $1.2b invested, a ~2.9x blended. Low-ish because most of the capital came in last. But it hides a lot of disparity between the various rounds! So let's dive in: 1) The real home run: founders and team 🏆🥳 This is one of the best founder outcomes I've modelled. @drfeifei, Justin Johnson, Christoph Lassner and Ben Mildenhall started World Labs in Apr-24. Less than 2.5 years later, they and the team still own ~56% according to my estimates. At an $8.2b valuation, that's a total prize of ~$4.6b to share between them. Taking a 15% attributed option pool, that's ~$1.2b for the team and ~$3.4b for the four co-founders. If split equally, that's ~$850m each, in just two and a half years. 💸 And, at close, Fei-Fei Li joins AMD as EVP and Chief Scientist, reporting to Lisa Su! Legendary. 2) The buyer was already on the cap table (again) 🔁 AMD Ventures invested in the Series B and the Series C here. Now they are buying the whole company. It's the same playbook as Nvidia with Hugging Face: strategic corporates are using their venture arms as a free option on future acquisitions. 3) The standout VC winners: a16z and Radical Ventures 👑 Both backed Fei-Fei at inception, in Apr-24 at a ~$300m valuation. That Series A will return ~19x the money, or ~$1.2b of proceeds, in under 2.5 years. Congrats to @martin_casado and team. Another mega win after Cursor and OpenRouter. The team at @a16z truly is on a generational run. 4) The star angels did well 🌟 The Sep-24 Series B had arguably the strongest AI angel list ever assembled: Geoffrey Hinton, Andrej Karpathy, Jeff Dean, Eric Schmidt, Reid Hoffman, Marc Benioff, Ram Shriram and more. Priced at $1.3b, it was never going to be a 1,000x. But ~5x in two years is a ~125% IRR 📈 And the best part is: Hinton's AlexNet won the 2012 ImageNet competition, and ImageNet was the dataset Fei-Fei built. Twelve years later, he backed her company. Full circle. 🔄 5) The late money: a great IRR, but a poor multiple of money 🤏 The $1b Series C in Feb-26 at $5.4b returns ~1.5x in 7 months. That's a great IRR, but in terms of venture returns, this is a poor multiple of money. Although I am sure no one will complain about seeing DPI this quickly! Congrats to all involved!

Higgsfield is the most untold story in tech. $1BN in ARR in 18 months. Faster than everyone other than OpenAI and Anthropic. They spend $4M a month on models. They expect this to be $100K per person per month. They have 150 people working in a content machine. They will breed more millionaires than any other company in Kazakh history. For the first time, @alexmashrabov on the journey to $1BN in ARR. (below) 1. The Power of the Immigrant Founder Coming from Uzbekistan, Alex was pushed into competitive programming at age eight as his single path to reach the United States. For international founders, placing top in global competitions serves as the ultimate social elevator, instilling the relentless work ethic required to build breakout companies. 2. My Biggest Lessons in the Journey to Finding Product-Market Fit @higgsfield burned over $10 million of its $16 million seed round chasing hype and narrative rather than product quality. With under $5 million left, the team pivoted to product-led growth, solving camera control for creative directors, which immediately triggered organic hypergrowth without paid ads. 3. The 150-Person Content Team Powering Higgsfield's Billion in ARR Nearly half of Higgsfield's workforce consists of 150 in-house creative professionals producing tutorials, ads, and cinematic projects. Generating 90 minutes of TV-quality AI video requires 100 hours of raw output, proving human taste and curation remain the primary drivers of distribution. 4. We Spend $4 Million per Month on Models Higgsfield spends $4 million monthly on internal model usage, averaging $10,000 per employee so teams can freely vibe code and test workflows. Uncapped inference compute acts as a force multiplier, allowing top talent to discover breakthroughs at maximum velocity. 5. Why Chasing Benchmarks Is Bullshit and the Corporate Misalignment Occurring Public benchmarks have devolved into corporate psyops where lab researchers overfit test data to secure bonuses before job-hopping. Text-to-video benchmarks ignore real production workflows requiring 3,000-word prompts, proving direct customer iteration beats artificial leaderboards. 6. Why Team Sizes Won't Be Impacted as Much as People Think While AI handles over 60% of basic support requests, complex B2B environments cannot eliminate human teams. High product velocity constantly shifts rules and context, requiring smart, coordinated operators across legal and customer success. 7. Americans Are Way More Promiscuous When It Comes to Leaving Companies Silicon Valley workers routinely jump jobs every two years, prioritizing short-term trends over deep commitment. This transactional market gives international hubs an advantage, where cultural loyalty and team stability build compounding technical moats. (links in comments)

The biggest AI launch since ChatGPT is not happening on your laptop. It is happening on WhatsApp. What it means for Meta, Booking, your insurer, and everyone who has never opened ChatGPT: