Harry Stebbings

How many podcasts do you listen to when you hear leaders posturing without giving an opinion. Substance-less. So so many. This episode with my friend @mcannonbrookes is anything but that. - It is BS that agents will remove interfaces. Design becomes more important. - Companies with their own models is BS. They are post-trained on Chinese models. - The biggest enterprises in the world are fearful of Chinese models. - Inference will get so much cheaper over time. It will be wild. Mike does not hold back here and it was a joy. In a world of meh opinions and nuance, Mike was real and honest. My notes below. 1. Why Headless Is Brainless and Why You Have to Be Involved in the User Experience and Interface Mike Cannon-Brookes stands by his line that "headless is brainless." Giving up on evolving the user interface and customer experience is a brainless bet, even as Atlassian invests heavily in MCP and CLIs so agents can read and write to its Teamwork graph. 2. Agents Will Not Remove All Interfaces. This Is What They Will Do Horizontal agents, deep vertical agents and existing interfaces can all coexist. Agents in Claude, Codex, Cursor, Teams and Slack already use the Atlassian platform, and new Figma and Databricks features bring their tools inside it. Mike is a bear on the "magic button" thesis of one uber agent that does everything. 3. Are the Biggest Enterprises in the World Fearful of Using Chinese Models? Some customers push back on Chinese models but not on open models, so Atlassian uses Nemotron, Mistral and other open models and no Chinese models in production. Mike notes many US models increasingly train on Chinese models without saying so. 4. The Companies Saying They've Built Their Own Models Are Bullsh*tting You When a large company claims to have built its own model, it is usually a Chinese model, or occasionally Nemotron, with fine-tuning on top. Mike calls that BS, because tweaking a model is not training one from scratch. 5. Why Model Inference Will Get So Much Cheaper Over Time The capital, startups and talent working on the price per watt performance of inference is enormous. New chips, better data center design and distillation will take years to come through, but Mike expects the price of LLM performance to fall at a precipitous rate. 6. What Seems Crazy but Will Be Incredibly Common in Five Years' Time? People may stop knowing or caring which content AI has touched, much as moviegoers do not ask whether a film used CGI. Mike expects everyone to get used to possibly synthetic content, with artisanal work and human judgment standing out more. (links in comments)

How many podcasts do you listen to when you hear leaders posturing without giving an opinion. Substance-less. So so many. This episode with my friend @mcannonbrookes is anything but that. - It is BS that agents will remove interfaces. Design becomes more important. - Companies with their own models is BS. They are post-trained on Chinese models. - The biggest enterprises in the world are fearful of Chinese models. - Inference will get so much cheaper over time. It will be wild. Mike does not hold back here and it was a joy. In a world of meh opinions and nuance, Mike was real and honest. My notes below. 1. Why Headless Is Brainless and Why You Have to Be Involved in the User Experience and Interface Mike Cannon-Brookes stands by his line that "headless is brainless." Giving up on evolving the user interface and customer experience is a brainless bet, even as Atlassian invests heavily in MCP and CLIs so agents can read and write to its Teamwork graph. 2. Agents Will Not Remove All Interfaces. This Is What They Will Do Horizontal agents, deep vertical agents and existing interfaces can all coexist. Agents in Claude, Codex, Cursor, Teams and Slack already use the Atlassian platform, and new Figma and Databricks features bring their tools inside it. Mike is a bear on the "magic button" thesis of one uber agent that does everything. 3. Are the Biggest Enterprises in the World Fearful of Using Chinese Models? Some customers push back on Chinese models but not on open models, so Atlassian uses Nemotron, Mistral and other open models and no Chinese models in production. Mike notes many US models increasingly train on Chinese models without saying so. 4. The Companies Saying They've Built Their Own Models Are Bullsh*tting You When a large company claims to have built its own model, it is usually a Chinese model, or occasionally Nemotron, with fine-tuning on top. Mike calls that BS, because tweaking a model is not training one from scratch. 5. Why Model Inference Will Get So Much Cheaper Over Time The capital, startups and talent working on the price per watt performance of inference is enormous. New chips, better data center design and distillation will take years to come through, but Mike expects the price of LLM performance to fall at a precipitous rate. 6. What Seems Crazy but Will Be Incredibly Common in Five Years' Time? People may stop knowing or caring which content AI has touched, much as moviegoers do not ask whether a film used CGI. Mike expects everyone to get used to possibly synthetic content, with artisanal work and human judgment standing out more. (links in comments)

Podcasts frustrate me because they are fluffy and high-level. This is the most in-depth discussion on the framework that @tryramp have used to hire, monitor, and scale their sales team. - Why you should overpay reps - Why you should cancel forecasting calls - Why bankers make the best reps - Why comp plans are BS and you should not have one to start @max11free, SVP of Sales is truly epic here and my notes from the discussion below. -- 1. Why You Should Overpay Your Sales Reps The early bet was to pay SDRs well above market rate and expect output well above market in return. Reps earn 2X a standard SDR but book 40, 50 or 60 meetings, four to six times the usual target, so the unit economics still work. 2. You Have to Treat Hiring a Sales Team Like Moneyball: How to Find Hidden Talent Go-to-market recruiting should follow Billy Beane, targeting top sellers at companies that grew revenue past nine figures despite a low NPS score. Those reps had to create their own demand, work complex cycles with weak third-party reviews and sell at a premium to cheaper rivals. The number one or two seller there can thrive in a system like Ramp. Single biggest advice on finding hidden gem talent @rabois? 3. Why Bankers Make Great Sales Reps Bankers are programmed to work, and a nine-to-five is foreign to them. Before the company had brand recognition, a cold call to a CFO from someone who had just left Goldman TMT IB carried weight. Early sales teams lack time to teach cold call and email basics, so bankers kept the team in pure execution mode. Find this also @PeetsChad 4. Why I Advise Not to Have a Comp Plan Founders are advised to pay 100% OTE for at least the first two quarters instead of setting a comp plan. A quota set too low can be blown out in six months, leaving the company owing a seller multiple six figures. A target picked arbitrarily can also prove unattainable and stall momentum. Haha, interested to hear your thoughts on this one @Carles_Reina 5. Never Hire Sales Reps One at a Time Hiring one seller or SDR at a time leaves no way to benchmark performance, and comparing them against the founder is not reasonable. Onboarding is laborious and expensive, so hiring in pairs is better, bringing competition and collaboration and keeping reps from being lonely. 6. Why the Most Annoying Sales Reps Tend to Be the Most Successful Sellers who constantly ask about account strategy, pipeline and product during onboarding show a direct correlation with being incredible. Quiet reps, or those asking questions an AI or Notion bot could answer, usually give a weak signal, though the inverse is not always true. 7. What Should Sales Talent Focus on When Deciding Which Company to Join? Sellers joining an early-stage company should not index on equity, cash and comp, but on the market, the talent density of the business and the founders. Taking a VP title and lots of equity at a mediocre business growing 20 to 30% a year is the wrong trade. Biggest advice to talent on choosing which company to join @breeves08

Podcasts frustrate me because they are fluffy and high-level. This is the most in-depth discussion on the framework that @tryramp have used to hire, monitor, and scale their sales team. - Why you should overpay reps - Why you should cancel forecasting calls - Why bankers make the best reps - Why comp plans are BS and you should not have one to start @max11free, SVP of Sales is truly epic here and my notes from the discussion below. -- 1. Why You Should Overpay Your Sales Reps The early bet was to pay SDRs well above market rate and expect output well above market in return. Reps earn 2X a standard SDR but book 40, 50 or 60 meetings, four to six times the usual target, so the unit economics still work. 2. You Have to Treat Hiring a Sales Team Like Moneyball: How to Find Hidden Talent Go-to-market recruiting should follow Billy Beane, targeting top sellers at companies that grew revenue past nine figures despite a low NPS score. Those reps had to create their own demand, work complex cycles with weak third-party reviews and sell at a premium to cheaper rivals. The number one or two seller there can thrive in a system like Ramp. Single biggest advice on finding hidden gem talent @rabois? 3. Why Bankers Make Great Sales Reps Bankers are programmed to work, and a nine-to-five is foreign to them. Before the company had brand recognition, a cold call to a CFO from someone who had just left Goldman TMT IB carried weight. Early sales teams lack time to teach cold call and email basics, so bankers kept the team in pure execution mode. Find this also @PeetsChad 4. Why I Advise Not to Have a Comp Plan Founders are advised to pay 100% OTE for at least the first two quarters instead of setting a comp plan. A quota set too low can be blown out in six months, leaving the company owing a seller multiple six figures. A target picked arbitrarily can also prove unattainable and stall momentum. Haha, interested to hear your thoughts on this one @Carles_Reina 5. Never Hire Sales Reps One at a Time Hiring one seller or SDR at a time leaves no way to benchmark performance, and comparing them against the founder is not reasonable. Onboarding is laborious and expensive, so hiring in pairs is better, bringing competition and collaboration and keeping reps from being lonely. 6. Why the Most Annoying Sales Reps Tend to Be the Most Successful Sellers who constantly ask about account strategy, pipeline and product during onboarding show a direct correlation with being incredible. Quiet reps, or those asking questions an AI or Notion bot could answer, usually give a weak signal, though the inverse is not always true. 7. What Should Sales Talent Focus on When Deciding Which Company to Join? Sellers joining an early-stage company should not index on equity, cash and comp, but on the market, the talent density of the business and the founders. Taking a VP title and lots of equity at a mediocre business growing 20 to 30% a year is the wrong trade. Biggest advice to talent on choosing which company to join @breeves08

Podcasts frustrate me because they are fluffy and high-level. This is the most in-depth discussion on the framework that @tryramp have used to hire, monitor, and scale their sales team. - Why you should overpay reps - Why you should cancel forecasting calls - Why bankers make the best reps - Why comp plans are BS and you should not have one to start @max11free, SVP of Sales is truly epic here and my notes from the discussion below. -- 1. Why You Should Overpay Your Sales Reps The early bet was to pay SDRs well above market rate and expect output well above market in return. Reps earn 2X a standard SDR but book 40, 50 or 60 meetings, four to six times the usual target, so the unit economics still work. 2. You Have to Treat Hiring a Sales Team Like Moneyball: How to Find Hidden Talent Go-to-market recruiting should follow Billy Beane, targeting top sellers at companies that grew revenue past nine figures despite a low NPS score. Those reps had to create their own demand, work complex cycles with weak third-party reviews and sell at a premium to cheaper rivals. The number one or two seller there can thrive in a system like Ramp. Single biggest advice on finding hidden gem talent @rabois? 3. Why Bankers Make Great Sales Reps Bankers are programmed to work, and a nine-to-five is foreign to them. Before the company had brand recognition, a cold call to a CFO from someone who had just left Goldman TMT IB carried weight. Early sales teams lack time to teach cold call and email basics, so bankers kept the team in pure execution mode. Find this also @PeetsChad 4. Why I Advise Not to Have a Comp Plan Founders are advised to pay 100% OTE for at least the first two quarters instead of setting a comp plan. A quota set too low can be blown out in six months, leaving the company owing a seller multiple six figures. A target picked arbitrarily can also prove unattainable and stall momentum. Haha, interested to hear your thoughts on this one @Carles_Reina 5. Never Hire Sales Reps One at a Time Hiring one seller or SDR at a time leaves no way to benchmark performance, and comparing them against the founder is not reasonable. Onboarding is laborious and expensive, so hiring in pairs is better, bringing competition and collaboration and keeping reps from being lonely. 6. Why the Most Annoying Sales Reps Tend to Be the Most Successful Sellers who constantly ask about account strategy, pipeline and product during onboarding show a direct correlation with being incredible. Quiet reps, or those asking questions an AI or Notion bot could answer, usually give a weak signal, though the inverse is not always true. 7. What Should Sales Talent Focus on When Deciding Which Company to Join? Sellers joining an early-stage company should not index on equity, cash and comp, but on the market, the talent density of the business and the founders. Taking a VP title and lots of equity at a mediocre business growing 20 to 30% a year is the wrong trade. Biggest advice to talent on choosing which company to join @breeves08

Podcasts frustrate me because they are fluffy and high-level. This is the most in-depth discussion on the framework that @tryramp have used to hire, monitor, and scale their sales team. - Why you should overpay reps - Why you should cancel forecasting calls - Why bankers make the best reps - Why comp plans are BS and you should not have one to start @max11free, SVP of Sales is truly epic here and my notes from the discussion below. -- 1. Why You Should Overpay Your Sales Reps The early bet was to pay SDRs well above market rate and expect output well above market in return. Reps earn 2X a standard SDR but book 40, 50 or 60 meetings, four to six times the usual target, so the unit economics still work. 2. You Have to Treat Hiring a Sales Team Like Moneyball: How to Find Hidden Talent Go-to-market recruiting should follow Billy Beane, targeting top sellers at companies that grew revenue past nine figures despite a low NPS score. Those reps had to create their own demand, work complex cycles with weak third-party reviews and sell at a premium to cheaper rivals. The number one or two seller there can thrive in a system like Ramp. Single biggest advice on finding hidden gem talent @rabois? 3. Why Bankers Make Great Sales Reps Bankers are programmed to work, and a nine-to-five is foreign to them. Before the company had brand recognition, a cold call to a CFO from someone who had just left Goldman TMT IB carried weight. Early sales teams lack time to teach cold call and email basics, so bankers kept the team in pure execution mode. Find this also @PeetsChad 4. Why I Advise Not to Have a Comp Plan Founders are advised to pay 100% OTE for at least the first two quarters instead of setting a comp plan. A quota set too low can be blown out in six months, leaving the company owing a seller multiple six figures. A target picked arbitrarily can also prove unattainable and stall momentum. Haha, interested to hear your thoughts on this one @Carles_Reina 5. Never Hire Sales Reps One at a Time Hiring one seller or SDR at a time leaves no way to benchmark performance, and comparing them against the founder is not reasonable. Onboarding is laborious and expensive, so hiring in pairs is better, bringing competition and collaboration and keeping reps from being lonely. 6. Why the Most Annoying Sales Reps Tend to Be the Most Successful Sellers who constantly ask about account strategy, pipeline and product during onboarding show a direct correlation with being incredible. Quiet reps, or those asking questions an AI or Notion bot could answer, usually give a weak signal, though the inverse is not always true. 7. What Should Sales Talent Focus on When Deciding Which Company to Join? Sellers joining an early-stage company should not index on equity, cash and comp, but on the market, the talent density of the business and the founders. Taking a VP title and lots of equity at a mediocre business growing 20 to 30% a year is the wrong trade. Biggest advice to talent on choosing which company to join @breeves08

Podcasts frustrate me because they are fluffy and high-level. This is the most in-depth discussion on the framework that @tryramp have used to hire, monitor, and scale their sales team. - Why you should overpay reps - Why you should cancel forecasting calls - Why bankers make the best reps - Why comp plans are BS and you should not have one to start @max11free, SVP of Sales is truly epic here and my notes from the discussion below. -- 1. Why You Should Overpay Your Sales Reps The early bet was to pay SDRs well above market rate and expect output well above market in return. Reps earn 2X a standard SDR but book 40, 50 or 60 meetings, four to six times the usual target, so the unit economics still work. 2. You Have to Treat Hiring a Sales Team Like Moneyball: How to Find Hidden Talent Go-to-market recruiting should follow Billy Beane, targeting top sellers at companies that grew revenue past nine figures despite a low NPS score. Those reps had to create their own demand, work complex cycles with weak third-party reviews and sell at a premium to cheaper rivals. The number one or two seller there can thrive in a system like Ramp. Single biggest advice on finding hidden gem talent @rabois? 3. Why Bankers Make Great Sales Reps Bankers are programmed to work, and a nine-to-five is foreign to them. Before the company had brand recognition, a cold call to a CFO from someone who had just left Goldman TMT IB carried weight. Early sales teams lack time to teach cold call and email basics, so bankers kept the team in pure execution mode. Find this also @PeetsChad 4. Why I Advise Not to Have a Comp Plan Founders are advised to pay 100% OTE for at least the first two quarters instead of setting a comp plan. A quota set too low can be blown out in six months, leaving the company owing a seller multiple six figures. A target picked arbitrarily can also prove unattainable and stall momentum. Haha, interested to hear your thoughts on this one @Carles_Reina 5. Never Hire Sales Reps One at a Time Hiring one seller or SDR at a time leaves no way to benchmark performance, and comparing them against the founder is not reasonable. Onboarding is laborious and expensive, so hiring in pairs is better, bringing competition and collaboration and keeping reps from being lonely. 6. Why the Most Annoying Sales Reps Tend to Be the Most Successful Sellers who constantly ask about account strategy, pipeline and product during onboarding show a direct correlation with being incredible. Quiet reps, or those asking questions an AI or Notion bot could answer, usually give a weak signal, though the inverse is not always true. 7. What Should Sales Talent Focus on When Deciding Which Company to Join? Sellers joining an early-stage company should not index on equity, cash and comp, but on the market, the talent density of the business and the founders. Taking a VP title and lots of equity at a mediocre business growing 20 to 30% a year is the wrong trade. Biggest advice to talent on choosing which company to join @breeves08

What a nuts week it has been in tech! - Factory vs Cognition: The Battle Continues - Vinod Khosla Tweets a Storm - OpenAI Hits $70BN Run Rate - Salesforce Buys Listen Labs for $2B More heat in this than a Sequoia tranched round!!! 😉 My notes below with @jasonlk, @rodriscoll, and @dittycheria 1. Why Is It Wrong for CROs to Jump Jobs So Easily? CROs recruit their teams by selling them on the company, the vision and how the role could transform their families' finances. A CRO who leaves for a direct competitor burns their reputation with everyone they recruited. Moving to a company in a different space for a better opportunity is common, but flipping straight to a direct rival is far rarer. 2. CROs Are Promiscuous and This Is How They Find Jobs The CRO role carries high risk and heavy turnover, and when a hot job opens up, CROs tend to move very quickly. Roughly 95% of CROs would likely see a jump to a competitor as normal, since that is how many of them found their last job. For CEOs, watching an advisor leave to join a competitor is still profoundly frustrating. 3. What Are You Doing Vinod Khosla? Vinod Khosla's tweet handed every firm competing with Khosla Ventures a weapon on deals. Rival investors can now ask founders, "Is this the partner you want when things go bad?" The reaction was shocking, given that Khosla Ventures has a great team. 4. Should We Invest in ElevenLabs at 22 Billion? At a $22B valuation, ElevenLabs merits up to 10% of the fund. Its lead keeps widening despite intense competition, backed by strong growth, surprisingly impressive margins and sub-millisecond voice response times. Voice is one of the 10 most important underpinnings of agentic applications, and ElevenLabs could soon be approaching $1B in revenue. 5. What Makes a Durable Company in 2026? Durable companies create a data loop where users generate data no one else has, that data improves the product, and a better product attracts more usage. Competitors can copy the features, but they cannot copy the user interactions behind them. 6. When Agents Pick You, It's a Force of Nature Vercel has reached $600M in ARR, with agents now driving 50% of its new business, up from 3% at the start of the year. Agents form strong opinions about which vendors to use, and those choices are hard to argue with. Companies now need agents to find them and understand what they sell, because missing from an AI-generated answer is like being invisible on Google. (links in comments)

What a nuts week it has been in tech! - Factory vs Cognition: The Battle Continues - Vinod Khosla Tweets a Storm - OpenAI Hits $70BN Run Rate - Salesforce Buys Listen Labs for $2B More heat in this than a Sequoia tranched round!!! 😉 My notes below with @jasonlk, @rodriscoll, and @dittycheria 1. Why Is It Wrong for CROs to Jump Jobs So Easily? CROs recruit their teams by selling them on the company, the vision and how the role could transform their families' finances. A CRO who leaves for a direct competitor burns their reputation with everyone they recruited. Moving to a company in a different space for a better opportunity is common, but flipping straight to a direct rival is far rarer. 2. CROs Are Promiscuous and This Is How They Find Jobs The CRO role carries high risk and heavy turnover, and when a hot job opens up, CROs tend to move very quickly. Roughly 95% of CROs would likely see a jump to a competitor as normal, since that is how many of them found their last job. For CEOs, watching an advisor leave to join a competitor is still profoundly frustrating. 3. What Are You Doing Vinod Khosla? Vinod Khosla's tweet handed every firm competing with Khosla Ventures a weapon on deals. Rival investors can now ask founders, "Is this the partner you want when things go bad?" The reaction was shocking, given that Khosla Ventures has a great team. 4. Should We Invest in ElevenLabs at 22 Billion? At a $22B valuation, ElevenLabs merits up to 10% of the fund. Its lead keeps widening despite intense competition, backed by strong growth, surprisingly impressive margins and sub-millisecond voice response times. Voice is one of the 10 most important underpinnings of agentic applications, and ElevenLabs could soon be approaching $1B in revenue. 5. What Makes a Durable Company in 2026? Durable companies create a data loop where users generate data no one else has, that data improves the product, and a better product attracts more usage. Competitors can copy the features, but they cannot copy the user interactions behind them. 6. When Agents Pick You, It's a Force of Nature Vercel has reached $600M in ARR, with agents now driving 50% of its new business, up from 3% at the start of the year. Agents form strong opinions about which vendors to use, and those choices are hard to argue with. Companies now need agents to find them and understand what they sell, because missing from an AI-generated answer is like being invisible on Google. (links in comments)

What a nuts week it has been in tech! - Factory vs Cognition: The Battle Continues - Vinod Khosla Tweets a Storm - OpenAI Hits $70BN Run Rate - Salesforce Buys Listen Labs for $2B More heat in this than a Sequoia tranched round!!! 😉 My notes below with @jasonlk, @rodriscoll, and @dittycheria 1. Why Is It Wrong for CROs to Jump Jobs So Easily? CROs recruit their teams by selling them on the company, the vision and how the role could transform their families' finances. A CRO who leaves for a direct competitor burns their reputation with everyone they recruited. Moving to a company in a different space for a better opportunity is common, but flipping straight to a direct rival is far rarer. 2. CROs Are Promiscuous and This Is How They Find Jobs The CRO role carries high risk and heavy turnover, and when a hot job opens up, CROs tend to move very quickly. Roughly 95% of CROs would likely see a jump to a competitor as normal, since that is how many of them found their last job. For CEOs, watching an advisor leave to join a competitor is still profoundly frustrating. 3. What Are You Doing Vinod Khosla? Vinod Khosla's tweet handed every firm competing with Khosla Ventures a weapon on deals. Rival investors can now ask founders, "Is this the partner you want when things go bad?" The reaction was shocking, given that Khosla Ventures has a great team. 4. Should We Invest in ElevenLabs at 22 Billion? At a $22B valuation, ElevenLabs merits up to 10% of the fund. Its lead keeps widening despite intense competition, backed by strong growth, surprisingly impressive margins and sub-millisecond voice response times. Voice is one of the 10 most important underpinnings of agentic applications, and ElevenLabs could soon be approaching $1B in revenue. 5. What Makes a Durable Company in 2026? Durable companies create a data loop where users generate data no one else has, that data improves the product, and a better product attracts more usage. Competitors can copy the features, but they cannot copy the user interactions behind them. 6. When Agents Pick You, It's a Force of Nature Vercel has reached $600M in ARR, with agents now driving 50% of its new business, up from 3% at the start of the year. Agents form strong opinions about which vendors to use, and those choices are hard to argue with. Companies now need agents to find them and understand what they sell, because missing from an AI-generated answer is like being invisible on Google. (links in comments)

What a nuts week it has been in tech! - Factory vs Cognition: The Battle Continues - Vinod Khosla Tweets a Storm - OpenAI Hits $70BN Run Rate - Salesforce Buys Listen Labs for $2B More heat in this than a Sequoia tranched round!!! 😉 My notes below with @jasonlk, @rodriscoll, and @dittycheria 1. Why Is It Wrong for CROs to Jump Jobs So Easily? CROs recruit their teams by selling them on the company, the vision and how the role could transform their families' finances. A CRO who leaves for a direct competitor burns their reputation with everyone they recruited. Moving to a company in a different space for a better opportunity is common, but flipping straight to a direct rival is far rarer. 2. CROs Are Promiscuous and This Is How They Find Jobs The CRO role carries high risk and heavy turnover, and when a hot job opens up, CROs tend to move very quickly. Roughly 95% of CROs would likely see a jump to a competitor as normal, since that is how many of them found their last job. For CEOs, watching an advisor leave to join a competitor is still profoundly frustrating. 3. What Are You Doing Vinod Khosla? Vinod Khosla's tweet handed every firm competing with Khosla Ventures a weapon on deals. Rival investors can now ask founders, "Is this the partner you want when things go bad?" The reaction was shocking, given that Khosla Ventures has a great team. 4. Should We Invest in ElevenLabs at 22 Billion? At a $22B valuation, ElevenLabs merits up to 10% of the fund. Its lead keeps widening despite intense competition, backed by strong growth, surprisingly impressive margins and sub-millisecond voice response times. Voice is one of the 10 most important underpinnings of agentic applications, and ElevenLabs could soon be approaching $1B in revenue. 5. What Makes a Durable Company in 2026? Durable companies create a data loop where users generate data no one else has, that data improves the product, and a better product attracts more usage. Competitors can copy the features, but they cannot copy the user interactions behind them. 6. When Agents Pick You, It's a Force of Nature Vercel has reached $600M in ARR, with agents now driving 50% of its new business, up from 3% at the start of the year. Agents form strong opinions about which vendors to use, and those choices are hard to argue with. Companies now need agents to find them and understand what they sell, because missing from an AI-generated answer is like being invisible on Google. (links in comments)

Today Hone launches Engines: AI that owns business outcomes. Chatbots give answers. Agents do tasks. Engines deliver outcomes, and we hold them responsible for creating real value, measured in actual dollars instead of tokens or vibes. You point it at a business metric, and the Engine figures out the rest. It onboards itself, builds the necessary agents, skills, and memories, collaborates with the right people, and gets better with experience. From AI leaders to global enterprises, our customers have already used Engines to: • Uncover millions in procurement savings • Optimize product funnels • Manage credit risk • Increase qualified pipeline in new territories • Accelerate implementations and improve account management To scale this impact across the economy, we’ve raised a $60M seed led by Benchmark and Index with participation from Elad Gil, Hanabi, Definition, Diffusion, Lux, SV Angel, and Align. AI’s raw capabilities are far ahead of the value it creates in the real world, and Engines will help close the gap. Here’s how they work…

What a nuts week it has been in tech! - Factory vs Cognition: The Battle Continues - Vinod Khosla Tweets a Storm - OpenAI Hits $70BN Run Rate - Salesforce Buys Listen Labs for $2B More heat in this than a Sequoia tranched round!!! 😉 My notes below with @jasonlk, @rodriscoll, and @dittycheria 1. Why Is It Wrong for CROs to Jump Jobs So Easily? CROs recruit their teams by selling them on the company, the vision and how the role could transform their families' finances. A CRO who leaves for a direct competitor burns their reputation with everyone they recruited. Moving to a company in a different space for a better opportunity is common, but flipping straight to a direct rival is far rarer. 2. CROs Are Promiscuous and This Is How They Find Jobs The CRO role carries high risk and heavy turnover, and when a hot job opens up, CROs tend to move very quickly. Roughly 95% of CROs would likely see a jump to a competitor as normal, since that is how many of them found their last job. For CEOs, watching an advisor leave to join a competitor is still profoundly frustrating. 3. What Are You Doing Vinod Khosla? Vinod Khosla's tweet handed every firm competing with Khosla Ventures a weapon on deals. Rival investors can now ask founders, "Is this the partner you want when things go bad?" The reaction was shocking, given that Khosla Ventures has a great team. 4. Should We Invest in ElevenLabs at 22 Billion? At a $22B valuation, ElevenLabs merits up to 10% of the fund. Its lead keeps widening despite intense competition, backed by strong growth, surprisingly impressive margins and sub-millisecond voice response times. Voice is one of the 10 most important underpinnings of agentic applications, and ElevenLabs could soon be approaching $1B in revenue. 5. What Makes a Durable Company in 2026? Durable companies create a data loop where users generate data no one else has, that data improves the product, and a better product attracts more usage. Competitors can copy the features, but they cannot copy the user interactions behind them. 6. When Agents Pick You, It's a Force of Nature Vercel has reached $600M in ARR, with agents now driving 50% of its new business, up from 3% at the start of the year. Agents form strong opinions about which vendors to use, and those choices are hard to argue with. Companies now need agents to find them and understand what they sell, because missing from an AI-generated answer is like being invisible on Google. (links in comments)

I know people are worried about fuel prices. And I get it. You get to the pump, watch the numbers climb, and wonder why it's costing so much more than it used to. The reality is that global events are pushing up costs, and families here at home are feeling the impact. But I won't accept that there's nothing I can do to help. That's why from today, you'll be able to see petrol and diesel prices on Google Maps, compare nearby stations and find the cheapest option before you fill up. It's a small change, but if there's a practical way to help people keep more money in their pocket, I'll do it.

What Menlo has done over the last 24 months is exceptional. Yes, they did Anthropic’s round at $4BN that will make them $50BN+ as a firm. NICE! But they then took that and went f****** hard. They do Lovable, Higgsfield, Legora. They compounded the brand win of Anthropic to cement their position from respectfully a slightly ageing firm to being a dominant venture leader at the forefront with Thrive, a16z, Benchmark etc. It is incredible to see and I sat down with @venkyganesan to shoot the s*** on this. 1. Every Seed Investment Is an Option Bet Seed-stage deals function as financial options to discover true outlier potential before committing major capital. VCs must construct a broad enough portfolio to secure sufficient at-bats, only sizing up their position when quantifiable, revenue-based metrics prove an outlier has emerged. 2. What Is the First Sign of the AI Wave Cracking? Market cycles rarely collapse due to equity write-downs. They fracture when levered debt defaults occur. When market participants take on heavy leverage during an AI boom, precise timing becomes as critical as being directionally correct, making debt obligations the primary failure vector when cycles turn. 3. Why Every VC Must Now Prioritize IRR Every startup in the current landscape writes a structural tax to the Mag 7 and hyperscalers for compute and foundation models. Because LPs can buy index exposure to those same public tech giants without fees or carry, private VCs must generate outsized IRR performance, beating public markets by at least 1,000 basis points, to justify private capital allocation. 4. Why LPs Wanting Smaller Funds and Slower Deployment Is BS LPs cannot demand both smaller fund sizes and multi-year deployment paces in the middle of a generational platform shift. Because AI startups face immense capital requirements to scale compute, VC firms that refuse to deploy capital rapidly will watch competitors step in and capture the category winners. 5. The Theory of Reflexivity and How It's Causing Markups Reflexivity creates self-reinforcing loops where fast revenue growth triggers rapid valuation markups, attracting elite human and financial capital that further accelerates growth. However, when copycat investors chase markups for momentum rather than underlying fundamentals, reflexive valuation bubbles inflate until market realities force an inevitable correction. 6. Sometimes You Pay Up Because You Have Unique Insight Premium entry prices are not always the result of irrational bidding. They often stem from an investor's ability to recognize a fundamentally larger total addressable market than the consensus. When a VC possesses unique conviction around an expanding market opportunity, paying above current rates reflects calculated foresight rather than price-taking behavior. 7. There's No Limit to What a Person Can Do as Long as They Don't Care Who Gets the Credit Personal ego remains one of the most expensive liabilities in venture capital and company building. True long-term success requires setting aside individual notoriety, focusing ruthlessly on generating LP returns, and empowering teammates and founders without worrying about who receives the public credit. (links in comments)

What Menlo has done over the last 24 months is exceptional. Yes, they did Anthropic’s round at $4BN that will make them $50BN+ as a firm. NICE! But they then took that and went f****** hard. They do Lovable, Higgsfield, Legora. They compounded the brand win of Anthropic to cement their position from respectfully a slightly ageing firm to being a dominant venture leader at the forefront with Thrive, a16z, Benchmark etc. It is incredible to see and I sat down with @venkyganesan to shoot the s*** on this. 1. Every Seed Investment Is an Option Bet Seed-stage deals function as financial options to discover true outlier potential before committing major capital. VCs must construct a broad enough portfolio to secure sufficient at-bats, only sizing up their position when quantifiable, revenue-based metrics prove an outlier has emerged. 2. What Is the First Sign of the AI Wave Cracking? Market cycles rarely collapse due to equity write-downs. They fracture when levered debt defaults occur. When market participants take on heavy leverage during an AI boom, precise timing becomes as critical as being directionally correct, making debt obligations the primary failure vector when cycles turn. 3. Why Every VC Must Now Prioritize IRR Every startup in the current landscape writes a structural tax to the Mag 7 and hyperscalers for compute and foundation models. Because LPs can buy index exposure to those same public tech giants without fees or carry, private VCs must generate outsized IRR performance, beating public markets by at least 1,000 basis points, to justify private capital allocation. 4. Why LPs Wanting Smaller Funds and Slower Deployment Is BS LPs cannot demand both smaller fund sizes and multi-year deployment paces in the middle of a generational platform shift. Because AI startups face immense capital requirements to scale compute, VC firms that refuse to deploy capital rapidly will watch competitors step in and capture the category winners. 5. The Theory of Reflexivity and How It's Causing Markups Reflexivity creates self-reinforcing loops where fast revenue growth triggers rapid valuation markups, attracting elite human and financial capital that further accelerates growth. However, when copycat investors chase markups for momentum rather than underlying fundamentals, reflexive valuation bubbles inflate until market realities force an inevitable correction. 6. Sometimes You Pay Up Because You Have Unique Insight Premium entry prices are not always the result of irrational bidding. They often stem from an investor's ability to recognize a fundamentally larger total addressable market than the consensus. When a VC possesses unique conviction around an expanding market opportunity, paying above current rates reflects calculated foresight rather than price-taking behavior. 7. There's No Limit to What a Person Can Do as Long as They Don't Care Who Gets the Credit Personal ego remains one of the most expensive liabilities in venture capital and company building. True long-term success requires setting aside individual notoriety, focusing ruthlessly on generating LP returns, and empowering teammates and founders without worrying about who receives the public credit. (links in comments)

What Menlo has done over the last 24 months is exceptional. Yes, they did Anthropic’s round at $4BN that will make them $50BN+ as a firm. NICE! But they then took that and went f****** hard. They do Lovable, Higgsfield, Legora. They compounded the brand win of Anthropic to cement their position from respectfully a slightly ageing firm to being a dominant venture leader at the forefront with Thrive, a16z, Benchmark etc. It is incredible to see and I sat down with @venkyganesan to shoot the s*** on this. 1. Every Seed Investment Is an Option Bet Seed-stage deals function as financial options to discover true outlier potential before committing major capital. VCs must construct a broad enough portfolio to secure sufficient at-bats, only sizing up their position when quantifiable, revenue-based metrics prove an outlier has emerged. 2. What Is the First Sign of the AI Wave Cracking? Market cycles rarely collapse due to equity write-downs. They fracture when levered debt defaults occur. When market participants take on heavy leverage during an AI boom, precise timing becomes as critical as being directionally correct, making debt obligations the primary failure vector when cycles turn. 3. Why Every VC Must Now Prioritize IRR Every startup in the current landscape writes a structural tax to the Mag 7 and hyperscalers for compute and foundation models. Because LPs can buy index exposure to those same public tech giants without fees or carry, private VCs must generate outsized IRR performance, beating public markets by at least 1,000 basis points, to justify private capital allocation. 4. Why LPs Wanting Smaller Funds and Slower Deployment Is BS LPs cannot demand both smaller fund sizes and multi-year deployment paces in the middle of a generational platform shift. Because AI startups face immense capital requirements to scale compute, VC firms that refuse to deploy capital rapidly will watch competitors step in and capture the category winners. 5. The Theory of Reflexivity and How It's Causing Markups Reflexivity creates self-reinforcing loops where fast revenue growth triggers rapid valuation markups, attracting elite human and financial capital that further accelerates growth. However, when copycat investors chase markups for momentum rather than underlying fundamentals, reflexive valuation bubbles inflate until market realities force an inevitable correction. 6. Sometimes You Pay Up Because You Have Unique Insight Premium entry prices are not always the result of irrational bidding. They often stem from an investor's ability to recognize a fundamentally larger total addressable market than the consensus. When a VC possesses unique conviction around an expanding market opportunity, paying above current rates reflects calculated foresight rather than price-taking behavior. 7. There's No Limit to What a Person Can Do as Long as They Don't Care Who Gets the Credit Personal ego remains one of the most expensive liabilities in venture capital and company building. True long-term success requires setting aside individual notoriety, focusing ruthlessly on generating LP returns, and empowering teammates and founders without worrying about who receives the public credit. (links in comments)

What Menlo has done over the last 24 months is exceptional. Yes, they did Anthropic’s round at $4BN that will make them $50BN+ as a firm. NICE! But they then took that and went f****** hard. They do Lovable, Higgsfield, Legora. They compounded the brand win of Anthropic to cement their position from respectfully a slightly ageing firm to being a dominant venture leader at the forefront with Thrive, a16z, Benchmark etc. It is incredible to see and I sat down with @venkyganesan to shoot the s*** on this. 1. Every Seed Investment Is an Option Bet Seed-stage deals function as financial options to discover true outlier potential before committing major capital. VCs must construct a broad enough portfolio to secure sufficient at-bats, only sizing up their position when quantifiable, revenue-based metrics prove an outlier has emerged. 2. What Is the First Sign of the AI Wave Cracking? Market cycles rarely collapse due to equity write-downs. They fracture when levered debt defaults occur. When market participants take on heavy leverage during an AI boom, precise timing becomes as critical as being directionally correct, making debt obligations the primary failure vector when cycles turn. 3. Why Every VC Must Now Prioritize IRR Every startup in the current landscape writes a structural tax to the Mag 7 and hyperscalers for compute and foundation models. Because LPs can buy index exposure to those same public tech giants without fees or carry, private VCs must generate outsized IRR performance, beating public markets by at least 1,000 basis points, to justify private capital allocation. 4. Why LPs Wanting Smaller Funds and Slower Deployment Is BS LPs cannot demand both smaller fund sizes and multi-year deployment paces in the middle of a generational platform shift. Because AI startups face immense capital requirements to scale compute, VC firms that refuse to deploy capital rapidly will watch competitors step in and capture the category winners. 5. The Theory of Reflexivity and How It's Causing Markups Reflexivity creates self-reinforcing loops where fast revenue growth triggers rapid valuation markups, attracting elite human and financial capital that further accelerates growth. However, when copycat investors chase markups for momentum rather than underlying fundamentals, reflexive valuation bubbles inflate until market realities force an inevitable correction. 6. Sometimes You Pay Up Because You Have Unique Insight Premium entry prices are not always the result of irrational bidding. They often stem from an investor's ability to recognize a fundamentally larger total addressable market than the consensus. When a VC possesses unique conviction around an expanding market opportunity, paying above current rates reflects calculated foresight rather than price-taking behavior. 7. There's No Limit to What a Person Can Do as Long as They Don't Care Who Gets the Credit Personal ego remains one of the most expensive liabilities in venture capital and company building. True long-term success requires setting aside individual notoriety, focusing ruthlessly on generating LP returns, and empowering teammates and founders without worrying about who receives the public credit. (links in comments)

What Menlo has done over the last 24 months is exceptional. Yes, they did Anthropic’s round at $4BN that will make them $50BN+ as a firm. NICE! But they then took that and went f****** hard. They do Lovable, Higgsfield, Legora. They compounded the brand win of Anthropic to cement their position from respectfully a slightly ageing firm to being a dominant venture leader at the forefront with Thrive, a16z, Benchmark etc. It is incredible to see and I sat down with @venkyganesan to shoot the s*** on this. 1. Every Seed Investment Is an Option Bet Seed-stage deals function as financial options to discover true outlier potential before committing major capital. VCs must construct a broad enough portfolio to secure sufficient at-bats, only sizing up their position when quantifiable, revenue-based metrics prove an outlier has emerged. 2. What Is the First Sign of the AI Wave Cracking? Market cycles rarely collapse due to equity write-downs. They fracture when levered debt defaults occur. When market participants take on heavy leverage during an AI boom, precise timing becomes as critical as being directionally correct, making debt obligations the primary failure vector when cycles turn. 3. Why Every VC Must Now Prioritize IRR Every startup in the current landscape writes a structural tax to the Mag 7 and hyperscalers for compute and foundation models. Because LPs can buy index exposure to those same public tech giants without fees or carry, private VCs must generate outsized IRR performance, beating public markets by at least 1,000 basis points, to justify private capital allocation. 4. Why LPs Wanting Smaller Funds and Slower Deployment Is BS LPs cannot demand both smaller fund sizes and multi-year deployment paces in the middle of a generational platform shift. Because AI startups face immense capital requirements to scale compute, VC firms that refuse to deploy capital rapidly will watch competitors step in and capture the category winners. 5. The Theory of Reflexivity and How It's Causing Markups Reflexivity creates self-reinforcing loops where fast revenue growth triggers rapid valuation markups, attracting elite human and financial capital that further accelerates growth. However, when copycat investors chase markups for momentum rather than underlying fundamentals, reflexive valuation bubbles inflate until market realities force an inevitable correction. 6. Sometimes You Pay Up Because You Have Unique Insight Premium entry prices are not always the result of irrational bidding. They often stem from an investor's ability to recognize a fundamentally larger total addressable market than the consensus. When a VC possesses unique conviction around an expanding market opportunity, paying above current rates reflects calculated foresight rather than price-taking behavior. 7. There's No Limit to What a Person Can Do as Long as They Don't Care Who Gets the Credit Personal ego remains one of the most expensive liabilities in venture capital and company building. True long-term success requires setting aside individual notoriety, focusing ruthlessly on generating LP returns, and empowering teammates and founders without worrying about who receives the public credit. (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)

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: x.com/i/article/2103…

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)

Nubank rumoured to acquire Monzo for ~$13b. But who gets the 💰? My usual breakdown below 👇 Investors are likely to share a huge $9.3b of profits combined, on $1.9b invested. This represents a 5.8x blended, across 11 priced rounds. 🤑 The Founders & team will share about ~$2.0b. This is a super win for a company that had to take a down round mid-2020. My takeaways below: 1) Passion Capital's absolute home run 👑 @eileentso and Passion led each of Monzo's first three rounds. They also kept following-on in almost every round after that. The Seed alone will return nearly 200x! Across the three seed rounds they led, that's ~$1.6b of proceeds on ~$18m invested. Legendary. 2) Two huge absolute $ winners: Goodwater and Thrive 🏆 Goodwater led the Series C in Nov-17, at a $330m post. It will return over 20x the money. That round will generate the most absolute $ of all rounds at ~$2.0b. 📈 Thrive led the Jul-17 Series B round at $100m post money valuation, and is now looking at a cool~50x on its investment. Two amazing good calls within a few months of each other. 3) The crowd did great 🙌 Monzo raised three crowdfunding rounds, including the famous "£1m on Crowdcube in 96 seconds" in 2016. Those customers-turned-shareholders are sitting on ~35-80x of profits! Probably the second best-performing retail investment in UK tech history, after Revolut's own crowdcube campaign (where investors are now sitting on 1,000x+ paper gains). 4) Buying the dip paid off 📈 The Jun-20 down round (Novator, Nikesh Arora) came in at a ~$1.7b post money valuation. Interestingly, this is below the 2019 price. Back then, everyone thought Monzo might not make it. That round will returns ~5.9x and about $1.6b. 5) The latest growth rounds: good, not great 😐 The Series H (ADQ, Tencent, Coatue) and Series I (CapitalG, GV, Hedosophia) make ~2.5x each. For the Series H specifically, after almost five years: respectable, not spectacular. 6) Team & Founders 🧑‍🤝‍🧑🫂 Firstly: mega congrats to @tomblomfield, TS Anil and the whole team. This would be an incredibly outcome and it is definitely one to be celebrated. The team here will do very well, despite Monzo raising eleven rounds and diluting meaningfully. I estimate that Founders & ESOP will get about 15% of the outcome, or ~$2.0bn.

90% of podcasts you listen to will actually not make you smarter. Today’s show is hotter than Meta stock… - Meta's Muse Hits No. 1. - ChatGPT Finally Has a Rival - Menlo Sounds the AI Bubble Alarm - Factory Triples Its Valuation to $5 Billion - Keith Rabois vs Airwallex: Who is Right? - Crusoe's $3.9 Billion Round. Is the Data Centre Trade Overheating? Imagine Gavin Baker, Squawkbox and Jerry Springer had a baby… that’s this episode today 🤣 1. Why Anthropic pushed its $2 trillion IPO from October to November Anthropic delaying its IPO from October to November is a tactical move to present a clean Q3 prospectus. Rather than listing while Q3 metrics are unaudited, waiting lets bankers include fully audited, record-setting October numbers so the financial results speak for themselves. 2. Why Muse and Instinct are the biggest credible threats to ChatGPT Autonomous agent interfaces like Muse act as a Trojan horse against ChatGPT by bundling solid consumer LLMs with free autonomous agent execution. Offering daily conversational AI and action-taking agents for free with higher token limits makes paying $20 monthly for basic chat hard to justify. 3. Amazon is right to block Muse Amazon blocking Meta's agentic commerce, Muse, defends its core profit model. Autonomous agents bypass sponsored ad listings, Amazon's main e-commerce profit driver, while shrinking total basket sizes. Amazon's massive consumer leverage forces Meta to negotiate a value exchange rather than freely harvesting its store infrastructure. 4. What should the API policy be for every big company? Aggregated consumer demand dictates tech adoption far more than theoretical payment protocols. When platforms like Meta aggregate massive user volume against backend systems, incumbents like Amazon, OpenTable, and Resy must immediately formulate agent API policies. Demand aggregation remains the ultimate driver of enterprise urgency. 5. Why I would make the investment into Factory at 5 billion Investing in Factory at $5 billion is a high-conviction bet on data sovereignty and model choice. C-level executives deeply distrust frontier labs with confidential codebases, fearing competitive data leaks through shared LLMs. As coding inference surges, air-gapped, model-agnostic enterprise platforms will capture massive market share. 6. Why would Rory invest in Factory at $5 billion? Software coding is the mother lode of AI value creation. While corporate boards demand rapid AI adoption, enterprises prefer buying end-to-end dev tools from independent players like Factory rather than frontier model labs whose data retention policies pose existential IP risks. 7. Why Jack at Airwallex needs an army of people fighting for him Running a decacorn requires a dedicated bench of public advocates to disarm narrative attacks. Founders shouldn't waste executive bandwidth arguing on social media against public attacks. Mobilizing an army of vocal supporters lets CEOs focus strictly on execution while third parties manage reputation. (links in comments)

90% of podcasts you listen to will actually not make you smarter. Today’s show is hotter than Meta stock… - Meta's Muse Hits No. 1. - ChatGPT Finally Has a Rival - Menlo Sounds the AI Bubble Alarm - Factory Triples Its Valuation to $5 Billion - Keith Rabois vs Airwallex: Who is Right? - Crusoe's $3.9 Billion Round. Is the Data Centre Trade Overheating? Imagine Gavin Baker, Squawkbox and Jerry Springer had a baby… that’s this episode today 🤣 1. Why Anthropic pushed its $2 trillion IPO from October to November Anthropic delaying its IPO from October to November is a tactical move to present a clean Q3 prospectus. Rather than listing while Q3 metrics are unaudited, waiting lets bankers include fully audited, record-setting October numbers so the financial results speak for themselves. 2. Why Muse and Instinct are the biggest credible threats to ChatGPT Autonomous agent interfaces like Muse act as a Trojan horse against ChatGPT by bundling solid consumer LLMs with free autonomous agent execution. Offering daily conversational AI and action-taking agents for free with higher token limits makes paying $20 monthly for basic chat hard to justify. 3. Amazon is right to block Muse Amazon blocking Meta's agentic commerce, Muse, defends its core profit model. Autonomous agents bypass sponsored ad listings, Amazon's main e-commerce profit driver, while shrinking total basket sizes. Amazon's massive consumer leverage forces Meta to negotiate a value exchange rather than freely harvesting its store infrastructure. 4. What should the API policy be for every big company? Aggregated consumer demand dictates tech adoption far more than theoretical payment protocols. When platforms like Meta aggregate massive user volume against backend systems, incumbents like Amazon, OpenTable, and Resy must immediately formulate agent API policies. Demand aggregation remains the ultimate driver of enterprise urgency. 5. Why I would make the investment into Factory at 5 billion Investing in Factory at $5 billion is a high-conviction bet on data sovereignty and model choice. C-level executives deeply distrust frontier labs with confidential codebases, fearing competitive data leaks through shared LLMs. As coding inference surges, air-gapped, model-agnostic enterprise platforms will capture massive market share. 6. Why would Rory invest in Factory at $5 billion? Software coding is the mother lode of AI value creation. While corporate boards demand rapid AI adoption, enterprises prefer buying end-to-end dev tools from independent players like Factory rather than frontier model labs whose data retention policies pose existential IP risks. 7. Why Jack at Airwallex needs an army of people fighting for him Running a decacorn requires a dedicated bench of public advocates to disarm narrative attacks. Founders shouldn't waste executive bandwidth arguing on social media against public attacks. Mobilizing an army of vocal supporters lets CEOs focus strictly on execution while third parties manage reputation. (links in comments)

I've done a lot of podcasts with @HarryStebbings since 2013 (!). Well before the current show we do with Rory. Without question he makes it easier than anyone else to ... do it. No endless emails or EAs, no drama, no agencies. Just removes all friction. Removing friction always works. Sales, CX, marketing, everything.

Fomo is starting to look a lot like an early Robinhood. the numbers are INSANE: > 2.5M+ users > $500M in ARR 😳 > $7.7B monthly trading volume > a recent $75M Series B a huge part of that growth has come from how well they’ve executed their creator program. they’ve very likely deployed well over $10M into creator spend at this point. open X, TikTok, or Instagram and the @fomo app is all over your feed. what'd be really interesting to see is if they eventually expand beyond trading and start layering in more fintech products like Robinhood did: > Fomo Visa Cards > virtual accounts > yield / savings trading at the core, with an entire fintech product line built around it. all around brilliant execution by their team.

The biggest news in tech this week: - Dario Calls to "Pace the Frontier" - Instinct Raising $1BN at $10BN & Meta Launches Muse - Miro Sells for $1.35BN After a $17.5BN Valuation - Mistral Raises €3BN My notes below with @jasonlk and @rodriscoll: 1. Summary of the Problems and the Solutions Cyber risk is real, mass economic unemployment feels overblown, and losing control of recursive agents is difficult to assess. Meanwhile, proposed solutions range from unlikely to practically impossible to implement. 2. Why Is It Overblown and Not Nearly as Dramatic as Anthropic Makes It? If the government truly believed a San Francisco lab had a 10% chance of destroying humanity, it would intervene immediately. The lack of intervention suggests authorities do not view extreme existential risk claims as an imminent threat. 3. Why Is It Bullshit, Dario, to Think We Will Have International Alignment on AI? Global coordination on frontier AI ignores geopolitical reality. Getting democratic allies to agree is difficult enough, while expecting enforceable alignment with China and Russia makes international pacing agreements even harder to imagine. 4. Is the Personal Assistant Market a Market Where You Can Build a Standalone Company? Standalone AI assistants face a fundamental question: can they build durable businesses while valuations race into the billions and platform incumbents pursue the same market? 5. Why Meta Might Just Win With Muse Meta has a structural advantage because it owns the infrastructure and underlying LLM. While competitors spend $3 to $4 per user on compute, Meta can deliver fast, capable agentic workflows without the same infrastructure burden. 6. Is Dario Simply Being Strategic Ahead of an IPO With His Pacing Argument? One interpretation is that Anthropic’s pacing argument is effectively an S-1 risk factor discussed in public. By addressing existential risk now, leadership can make it a debated, understood issue rather than a roadshow problem later. 7. Why I Would Not Invest in the New Instinct Round Standalone AI assistants face brutal economics when incumbents have their own models, infrastructure, and enormous resources. Subsidizing $5 to $10 per user can turn millions of users into a massive annual compute bill that venture capital must keep funding. (links in comments)

The biggest news in tech this week: - Dario Calls to "Pace the Frontier" - Instinct Raising $1BN at $10BN & Meta Launches Muse - Miro Sells for $1.35BN After a $17.5BN Valuation - Mistral Raises €3BN My notes below with @jasonlk and @rodriscoll: 1. Summary of the Problems and the Solutions Cyber risk is real, mass economic unemployment feels overblown, and losing control of recursive agents is difficult to assess. Meanwhile, proposed solutions range from unlikely to practically impossible to implement. 2. Why Is It Overblown and Not Nearly as Dramatic as Anthropic Makes It? If the government truly believed a San Francisco lab had a 10% chance of destroying humanity, it would intervene immediately. The lack of intervention suggests authorities do not view extreme existential risk claims as an imminent threat. 3. Why Is It Bullshit, Dario, to Think We Will Have International Alignment on AI? Global coordination on frontier AI ignores geopolitical reality. Getting democratic allies to agree is difficult enough, while expecting enforceable alignment with China and Russia makes international pacing agreements even harder to imagine. 4. Is the Personal Assistant Market a Market Where You Can Build a Standalone Company? Standalone AI assistants face a fundamental question: can they build durable businesses while valuations race into the billions and platform incumbents pursue the same market? 5. Why Meta Might Just Win With Muse Meta has a structural advantage because it owns the infrastructure and underlying LLM. While competitors spend $3 to $4 per user on compute, Meta can deliver fast, capable agentic workflows without the same infrastructure burden. 6. Is Dario Simply Being Strategic Ahead of an IPO With His Pacing Argument? One interpretation is that Anthropic’s pacing argument is effectively an S-1 risk factor discussed in public. By addressing existential risk now, leadership can make it a debated, understood issue rather than a roadshow problem later. 7. Why I Would Not Invest in the New Instinct Round Standalone AI assistants face brutal economics when incumbents have their own models, infrastructure, and enormous resources. Subsidizing $5 to $10 per user can turn millions of users into a massive annual compute bill that venture capital must keep funding. (links in comments)

The biggest news in tech this week: - Dario Calls to "Pace the Frontier" - Instinct Raising $1BN at $10BN & Meta Launches Muse - Miro Sells for $1.35BN After a $17.5BN Valuation - Mistral Raises €3BN My notes below with @jasonlk and @rodriscoll: 1. Summary of the Problems and the Solutions Cyber risk is real, mass economic unemployment feels overblown, and losing control of recursive agents is difficult to assess. Meanwhile, proposed solutions range from unlikely to practically impossible to implement. 2. Why Is It Overblown and Not Nearly as Dramatic as Anthropic Makes It? If the government truly believed a San Francisco lab had a 10% chance of destroying humanity, it would intervene immediately. The lack of intervention suggests authorities do not view extreme existential risk claims as an imminent threat. 3. Why Is It Bullshit, Dario, to Think We Will Have International Alignment on AI? Global coordination on frontier AI ignores geopolitical reality. Getting democratic allies to agree is difficult enough, while expecting enforceable alignment with China and Russia makes international pacing agreements even harder to imagine. 4. Is the Personal Assistant Market a Market Where You Can Build a Standalone Company? Standalone AI assistants face a fundamental question: can they build durable businesses while valuations race into the billions and platform incumbents pursue the same market? 5. Why Meta Might Just Win With Muse Meta has a structural advantage because it owns the infrastructure and underlying LLM. While competitors spend $3 to $4 per user on compute, Meta can deliver fast, capable agentic workflows without the same infrastructure burden. 6. Is Dario Simply Being Strategic Ahead of an IPO With His Pacing Argument? One interpretation is that Anthropic’s pacing argument is effectively an S-1 risk factor discussed in public. By addressing existential risk now, leadership can make it a debated, understood issue rather than a roadshow problem later. 7. Why I Would Not Invest in the New Instinct Round Standalone AI assistants face brutal economics when incumbents have their own models, infrastructure, and enormous resources. Subsidizing $5 to $10 per user can turn millions of users into a massive annual compute bill that venture capital must keep funding. (links in comments)

The biggest news in tech this week: - Dario Calls to "Pace the Frontier" - Instinct Raising $1BN at $10BN & Meta Launches Muse - Miro Sells for $1.35BN After a $17.5BN Valuation - Mistral Raises €3BN My notes below with @jasonlk and @rodriscoll: 1. Summary of the Problems and the Solutions Cyber risk is real, mass economic unemployment feels overblown, and losing control of recursive agents is difficult to assess. Meanwhile, proposed solutions range from unlikely to practically impossible to implement. 2. Why Is It Overblown and Not Nearly as Dramatic as Anthropic Makes It? If the government truly believed a San Francisco lab had a 10% chance of destroying humanity, it would intervene immediately. The lack of intervention suggests authorities do not view extreme existential risk claims as an imminent threat. 3. Why Is It Bullshit, Dario, to Think We Will Have International Alignment on AI? Global coordination on frontier AI ignores geopolitical reality. Getting democratic allies to agree is difficult enough, while expecting enforceable alignment with China and Russia makes international pacing agreements even harder to imagine. 4. Is the Personal Assistant Market a Market Where You Can Build a Standalone Company? Standalone AI assistants face a fundamental question: can they build durable businesses while valuations race into the billions and platform incumbents pursue the same market? 5. Why Meta Might Just Win With Muse Meta has a structural advantage because it owns the infrastructure and underlying LLM. While competitors spend $3 to $4 per user on compute, Meta can deliver fast, capable agentic workflows without the same infrastructure burden. 6. Is Dario Simply Being Strategic Ahead of an IPO With His Pacing Argument? One interpretation is that Anthropic’s pacing argument is effectively an S-1 risk factor discussed in public. By addressing existential risk now, leadership can make it a debated, understood issue rather than a roadshow problem later. 7. Why I Would Not Invest in the New Instinct Round Standalone AI assistants face brutal economics when incumbents have their own models, infrastructure, and enormous resources. Subsidizing $5 to $10 per user can turn millions of users into a massive annual compute bill that venture capital must keep funding. (links in comments)

The single biggest mistake VCs make is they do not build relationships with LPs in between funds. You need to know how the best allocators to venture in the world think: - What they like? - What they do not like? - What would make them pull from a fund? - What would make them double down in a fund? - How a fund can build urgency to get them over the line? And so so much more. The challenge is not many CIOs speak openly about this. And an even bigger challenge is many are not independent thinkers and tend to follow what others around them do. David Morehead is one of the most independent-thinking CIOs in the endowment fund world. He has scaled Baylor to $2.6BN and one of the most respected institutions. I sat down with @CIO_Baylor to understand what every VC needs to hear from LPs but does not. I summarised my notes below: 1. Even the Best Venture Returns Don’t Matter to Some Funds For mega-endowments managing $40 billion to $60 billion, writing $20 million checks into elite venture funds may simply not move the needle. Even an extraordinary 50x return that generates $1 billion has limited impact on the overall portfolio, pushing mega-LPs toward much larger platform allocations. 2. How This Endowment Made Millions Betting That Vibe Coding Would Not Replace Core Software When software stocks fell 50% to 60% on fears that AI “vibe coding” would make SaaS obsolete, Baylor looked at how enterprises actually operate. Traditional businesses are unlikely to replace mission-critical systems requiring 100% precision with AI that is only 93% accurate, turning the sell-off into a major buying opportunity. 3. How We Think About Position Sizing A $400,000 distribution from a 7x exit means little to a multi-billion-dollar endowment. Rather than focusing solely on fund size, Baylor works backward from underlying company exposure, targeting roughly $3M per portfolio company so major wins can generate a meaningful dollar impact. 4. Why Is Velocity of Capital More Important Than Multiple of Capital? A 15-18-year venture fund generating 15x can produce less compounded capital than 3 consecutive 6-year growth funds returning 3x each, compounding to 27x. Endowments need capital returned and redeployed quickly enough to maximize long-term compounding. 5. Why Talking About Returns Without a Timeline Attached Means Nothing A 5x return means nothing without knowing the timeframe. A 5x over 30 years is terrible, while a 5x over five months is extraordinary. Endowments therefore evaluate returns alongside duration, because the velocity of capital can matter just as much as the headline multiple. 6. Why We Learn Way More From Our Public Managers Than Our Private Managers Private venture managers can get caught up in speculative technology narratives, like predicting self-driving cars on every road within three years. Public-market managers often provide more grounded perspectives because their frameworks must account for immediate regulatory hurdles, adoption friction, and continuous price discovery. 7. You Have a Blank Canvas. What Do You Do Next? When constructing a multi-asset portfolio from scratch, institutional allocators should establish their private-market allocation first. Because illiquid investments constrain liquidity and future reallocation, allocators need to define strict risk boundaries and box off the private portfolio before deploying capital across public markets. (links in comments)

The single biggest mistake VCs make is they do not build relationships with LPs in between funds. You need to know how the best allocators to venture in the world think: - What they like? - What they do not like? - What would make them pull from a fund? - What would make them double down in a fund? - How a fund can build urgency to get them over the line? And so so much more. The challenge is not many CIOs speak openly about this. And an even bigger challenge is many are not independent thinkers and tend to follow what others around them do. David Morehead is one of the most independent-thinking CIOs in the endowment fund world. He has scaled Baylor to $2.6BN and one of the most respected institutions. I sat down with @CIO_Baylor to understand what every VC needs to hear from LPs but does not. I summarised my notes below: 1. Even the Best Venture Returns Don’t Matter to Some Funds For mega-endowments managing $40 billion to $60 billion, writing $20 million checks into elite venture funds may simply not move the needle. Even an extraordinary 50x return that generates $1 billion has limited impact on the overall portfolio, pushing mega-LPs toward much larger platform allocations. 2. How This Endowment Made Millions Betting That Vibe Coding Would Not Replace Core Software When software stocks fell 50% to 60% on fears that AI “vibe coding” would make SaaS obsolete, Baylor looked at how enterprises actually operate. Traditional businesses are unlikely to replace mission-critical systems requiring 100% precision with AI that is only 93% accurate, turning the sell-off into a major buying opportunity. 3. How We Think About Position Sizing A $400,000 distribution from a 7x exit means little to a multi-billion-dollar endowment. Rather than focusing solely on fund size, Baylor works backward from underlying company exposure, targeting roughly $3M per portfolio company so major wins can generate a meaningful dollar impact. 4. Why Is Velocity of Capital More Important Than Multiple of Capital? A 15-18-year venture fund generating 15x can produce less compounded capital than 3 consecutive 6-year growth funds returning 3x each, compounding to 27x. Endowments need capital returned and redeployed quickly enough to maximize long-term compounding. 5. Why Talking About Returns Without a Timeline Attached Means Nothing A 5x return means nothing without knowing the timeframe. A 5x over 30 years is terrible, while a 5x over five months is extraordinary. Endowments therefore evaluate returns alongside duration, because the velocity of capital can matter just as much as the headline multiple. 6. Why We Learn Way More From Our Public Managers Than Our Private Managers Private venture managers can get caught up in speculative technology narratives, like predicting self-driving cars on every road within three years. Public-market managers often provide more grounded perspectives because their frameworks must account for immediate regulatory hurdles, adoption friction, and continuous price discovery. 7. You Have a Blank Canvas. What Do You Do Next? When constructing a multi-asset portfolio from scratch, institutional allocators should establish their private-market allocation first. Because illiquid investments constrain liquidity and future reallocation, allocators need to define strict risk boundaries and box off the private portfolio before deploying capital across public markets. (links in comments)

The single biggest mistake VCs make is they do not build relationships with LPs in between funds. You need to know how the best allocators to venture in the world think: - What they like? - What they do not like? - What would make them pull from a fund? - What would make them double down in a fund? - How a fund can build urgency to get them over the line? And so so much more. The challenge is not many CIOs speak openly about this. And an even bigger challenge is many are not independent thinkers and tend to follow what others around them do. David Morehead is one of the most independent-thinking CIOs in the endowment fund world. He has scaled Baylor to $2.6BN and one of the most respected institutions. I sat down with @CIO_Baylor to understand what every VC needs to hear from LPs but does not. I summarised my notes below: 1. Even the Best Venture Returns Don’t Matter to Some Funds For mega-endowments managing $40 billion to $60 billion, writing $20 million checks into elite venture funds may simply not move the needle. Even an extraordinary 50x return that generates $1 billion has limited impact on the overall portfolio, pushing mega-LPs toward much larger platform allocations. 2. How This Endowment Made Millions Betting That Vibe Coding Would Not Replace Core Software When software stocks fell 50% to 60% on fears that AI “vibe coding” would make SaaS obsolete, Baylor looked at how enterprises actually operate. Traditional businesses are unlikely to replace mission-critical systems requiring 100% precision with AI that is only 93% accurate, turning the sell-off into a major buying opportunity. 3. How We Think About Position Sizing A $400,000 distribution from a 7x exit means little to a multi-billion-dollar endowment. Rather than focusing solely on fund size, Baylor works backward from underlying company exposure, targeting roughly $3M per portfolio company so major wins can generate a meaningful dollar impact. 4. Why Is Velocity of Capital More Important Than Multiple of Capital? A 15-18-year venture fund generating 15x can produce less compounded capital than 3 consecutive 6-year growth funds returning 3x each, compounding to 27x. Endowments need capital returned and redeployed quickly enough to maximize long-term compounding. 5. Why Talking About Returns Without a Timeline Attached Means Nothing A 5x return means nothing without knowing the timeframe. A 5x over 30 years is terrible, while a 5x over five months is extraordinary. Endowments therefore evaluate returns alongside duration, because the velocity of capital can matter just as much as the headline multiple. 6. Why We Learn Way More From Our Public Managers Than Our Private Managers Private venture managers can get caught up in speculative technology narratives, like predicting self-driving cars on every road within three years. Public-market managers often provide more grounded perspectives because their frameworks must account for immediate regulatory hurdles, adoption friction, and continuous price discovery. 7. You Have a Blank Canvas. What Do You Do Next? When constructing a multi-asset portfolio from scratch, institutional allocators should establish their private-market allocation first. Because illiquid investments constrain liquidity and future reallocation, allocators need to define strict risk boundaries and box off the private portfolio before deploying capital across public markets. (links in comments)

Eleven years ago I was introduced to @a_zatarain about sponsoring a 20VC episode. She did not know but this payment was used to pay for some of my mother’s MS treatment. The partnership happened and a friendship blossomed between me, Alexandra and Matteo. I remember 10 years ago, everyone thought they were crazy. Italian CEO, not an engineer, doing hardware with a supply chain in China. Haha, good luck. How wrong they were. A multi-BN business today doing hundreds of millions in revenue. An insanely fun chat with my friend @m_franceschetti 👇 1. How to Do Paid Marketing Really, Really Well Disciplined paid marketing means scaling one channel at a time, starting with Meta, while maintaining a strict cap on customer acquisition cost (CAC). Overspending destroys unit economics and eventually forces pullbacks that hurt year-over-year growth. Holding firm on CAC today creates the foundation for predictable, compounding growth tomorrow. 2. The Story Behind How Eight Sleep Partnered With Charles Leclerc The best partnerships come from authentic product adoption and long-term conviction. Eight Sleep tried to sponsor Charles Leclerc years before he reached F1. When they eventually met, Leclerc was already an unprompted Eight Sleep user, allowing a shared vision around AI and performance to turn into a partnership within days. 3. We Do $100 Million in Email Marketing With No Employees Operational constraints can force breakthrough automation. When Eight Sleep’s email lead left, co-founder Alexandra built AI bots in three days to manage the entire workflow. Today, the channel operates with zero dedicated employees while generating nearly $100 million in revenue. 4. What Seems Crazy but Will Be Very Commonplace in Five Years’ Time? Autonomous assets managed by personal AI agents could create entirely new forms of passive income. Within five years, an individual’s AI agent could buy an autonomous cybercab, manage its daily operations, and generate revenue with minimal human involvement. 5. Why Angel Investing as a Founder Has Taught Me So Much About Company Building Angel investing can become a powerful market intelligence engine for active founders. Reading investor updates from non-competing startups provides real-time insight into valuation shifts, hiring profiles, and acquisition channels that are working, all of which can directly inform operating decisions. 6. With AI, Every Company Will Turn Into a Holding Company With Multiple Products AI-powered internal tooling dramatically lowers the cost and friction of launching new ventures, allowing single-product startups to evolve into conglomerates. Like Chinese giants such as Xiaomi, AI-native companies could use shared internal infrastructure to launch and manage as many as ten distinct business lines under a single holding company.

Eleven years ago I was introduced to @a_zatarain about sponsoring a 20VC episode. She did not know but this payment was used to pay for some of my mother’s MS treatment. The partnership happened and a friendship blossomed between me, Alexandra and Matteo. I remember 10 years ago, everyone thought they were crazy. Italian CEO, not an engineer, doing hardware with a supply chain in China. Haha, good luck. How wrong they were. A multi-BN business today doing hundreds of millions in revenue. An insanely fun chat with my friend @m_franceschetti 👇 1. How to Do Paid Marketing Really, Really Well Disciplined paid marketing means scaling one channel at a time, starting with Meta, while maintaining a strict cap on customer acquisition cost (CAC). Overspending destroys unit economics and eventually forces pullbacks that hurt year-over-year growth. Holding firm on CAC today creates the foundation for predictable, compounding growth tomorrow. 2. The Story Behind How Eight Sleep Partnered With Charles Leclerc The best partnerships come from authentic product adoption and long-term conviction. Eight Sleep tried to sponsor Charles Leclerc years before he reached F1. When they eventually met, Leclerc was already an unprompted Eight Sleep user, allowing a shared vision around AI and performance to turn into a partnership within days. 3. We Do $100 Million in Email Marketing With No Employees Operational constraints can force breakthrough automation. When Eight Sleep’s email lead left, co-founder Alexandra built AI bots in three days to manage the entire workflow. Today, the channel operates with zero dedicated employees while generating nearly $100 million in revenue. 4. What Seems Crazy but Will Be Very Commonplace in Five Years’ Time? Autonomous assets managed by personal AI agents could create entirely new forms of passive income. Within five years, an individual’s AI agent could buy an autonomous cybercab, manage its daily operations, and generate revenue with minimal human involvement. 5. Why Angel Investing as a Founder Has Taught Me So Much About Company Building Angel investing can become a powerful market intelligence engine for active founders. Reading investor updates from non-competing startups provides real-time insight into valuation shifts, hiring profiles, and acquisition channels that are working, all of which can directly inform operating decisions. 6. With AI, Every Company Will Turn Into a Holding Company With Multiple Products AI-powered internal tooling dramatically lowers the cost and friction of launching new ventures, allowing single-product startups to evolve into conglomerates. Like Chinese giants such as Xiaomi, AI-native companies could use shared internal infrastructure to launch and manage as many as ten distinct business lines under a single holding company.

Eleven years ago I was introduced to @a_zatarain about sponsoring a 20VC episode. She did not know but this payment was used to pay for some of my mother’s MS treatment. The partnership happened and a friendship blossomed between me, Alexandra and Matteo. I remember 10 years ago, everyone thought they were crazy. Italian CEO, not an engineer, doing hardware with a supply chain in China. Haha, good luck. How wrong they were. A multi-BN business today doing hundreds of millions in revenue. An insanely fun chat with my friend @m_franceschetti 👇 1. How to Do Paid Marketing Really, Really Well Disciplined paid marketing means scaling one channel at a time, starting with Meta, while maintaining a strict cap on customer acquisition cost (CAC). Overspending destroys unit economics and eventually forces pullbacks that hurt year-over-year growth. Holding firm on CAC today creates the foundation for predictable, compounding growth tomorrow. 2. The Story Behind How Eight Sleep Partnered With Charles Leclerc The best partnerships come from authentic product adoption and long-term conviction. Eight Sleep tried to sponsor Charles Leclerc years before he reached F1. When they eventually met, Leclerc was already an unprompted Eight Sleep user, allowing a shared vision around AI and performance to turn into a partnership within days. 3. We Do $100 Million in Email Marketing With No Employees Operational constraints can force breakthrough automation. When Eight Sleep’s email lead left, co-founder Alexandra built AI bots in three days to manage the entire workflow. Today, the channel operates with zero dedicated employees while generating nearly $100 million in revenue. 4. What Seems Crazy but Will Be Very Commonplace in Five Years’ Time? Autonomous assets managed by personal AI agents could create entirely new forms of passive income. Within five years, an individual’s AI agent could buy an autonomous cybercab, manage its daily operations, and generate revenue with minimal human involvement. 5. Why Angel Investing as a Founder Has Taught Me So Much About Company Building Angel investing can become a powerful market intelligence engine for active founders. Reading investor updates from non-competing startups provides real-time insight into valuation shifts, hiring profiles, and acquisition channels that are working, all of which can directly inform operating decisions. 6. With AI, Every Company Will Turn Into a Holding Company With Multiple Products AI-powered internal tooling dramatically lowers the cost and friction of launching new ventures, allowing single-product startups to evolve into conglomerates. Like Chinese giants such as Xiaomi, AI-native companies could use shared internal infrastructure to launch and manage as many as ten distinct business lines under a single holding company.