We currently have a Prime Minister on LADbible discussing: VAR. The price of a pint. Oasis. If northerners are funnier than southerners. Andy Burnham is an unserious man in a very serious time. Bad combination.

Pricing in venture is wilder than ever. $100M pre-seed is now normal. Bessemer just raised $1.75BN for “early” and $4BN for growth. So can you even do seed today without $1BN? - Is capital truly one of the last remaining moats? - Do all small seed funds and mid-sized firms just fade into irrelevance? - Does entry price matter anymore when there are $TRN companies… - Is this real or a bubble that is about to burst I sat down with @spdholakia to discuss this and have added my notes from the discussion below. 1. This Is a Supercycle Like Never Before Sameer has spent 31 years in software and has felt magic only a handful of times. A trillion-dollar supercycle followed every one of those moments, and he notes that it does not happen very often. What is different and what is similar to prior cycles @altcap @infoarbitrage 2. Capital Is a Moat Today Markets pick winners and the choice becomes a self-fulfilling prophecy, as it did when developers settled on SendGrid and Twilio. In legal AI, big law firms name just two major providers. Capital lets leaders scale at an extraordinary pace and build a brand, which draws talent and customers in a reinforcing flywheel. Do you agree with this @venkyganesan @dafrankel 3. Who Wins and Who Loses in the Future of Venture Capital? As industries professionalize, firms must be either a giant global platform that consolidates or an extreme specialist, such as an early-stage AI investor in San Francisco. Sameer counts Bessemer as a big global platform. Firms caught in between face a hard world to make money. Is it go big or go home @rodriscoll @rabois @chetanp 4. The Rationale for Why Anthropic Would Compete With the Hyperscalers and Why We Invested As in the cloud era, AI would be massively capital intensive, so it would consolidate to a handful of players with hundreds of apps on top. Microsoft had partnered with OpenAI and Google was building Gemini, while AWS had an undistinctive internal model and had just invested heavily in Anthropic. 5. Why Price Does Not Matter in a World of AI and the Metric That Truly Determines Value Bessemer invested in Abridge at $5M of ARR for a $700M to $800M valuation, in Anthropic at roughly 150 times current ARR, and led Legora's $1.8B round at $20M of ARR. Sameer judges these deals on what the company can become in the next twelve months. How has your relationship to price changed in a world of AI @mignano @mmurph 6. Why the Rise of Open Models Does Not Denigrate the Market for Closed Models In a market this large, predicting the mix between closed and open tokens with any precision is irrelevant. Consumers, enterprises and governments will all want different flavors of intelligence. Bessemer has invested in both Anthropic and Fireworks to hold exposure to closed and open tokens. Love to hear how you think about this @lqiao @bernhardsson 7. Why It Is a Permanent Structural Shift That Companies Will Stay Private for Longer, but Why It Will Open in 2028 With more dollars and more firms in private venture, companies will stay private longer, and most of the run-up in this boom will happen in private markets. Sameer still expects an unlock in IPOs later next year and in 2028. Love to hear your thoughts on this @bgurley and how you see it evolving in 24 months? (links in comments)

Pricing in venture is wilder than ever. $100M pre-seed is now normal. Bessemer just raised $1.75BN for “early” and $4BN for growth. So can you even do seed today without $1BN? - Is capital truly one of the last remaining moats? - Do all small seed funds and mid-sized firms just fade into irrelevance? - Does entry price matter anymore when there are $TRN companies… - Is this real or a bubble that is about to burst I sat down with @spdholakia to discuss this and have added my notes from the discussion below. 1. This Is a Supercycle Like Never Before Sameer has spent 31 years in software and has felt magic only a handful of times. A trillion-dollar supercycle followed every one of those moments, and he notes that it does not happen very often. What is different and what is similar to prior cycles @altcap @infoarbitrage 2. Capital Is a Moat Today Markets pick winners and the choice becomes a self-fulfilling prophecy, as it did when developers settled on SendGrid and Twilio. In legal AI, big law firms name just two major providers. Capital lets leaders scale at an extraordinary pace and build a brand, which draws talent and customers in a reinforcing flywheel. Do you agree with this @venkyganesan @dafrankel 3. Who Wins and Who Loses in the Future of Venture Capital? As industries professionalize, firms must be either a giant global platform that consolidates or an extreme specialist, such as an early-stage AI investor in San Francisco. Sameer counts Bessemer as a big global platform. Firms caught in between face a hard world to make money. Is it go big or go home @rodriscoll @rabois @chetanp 4. The Rationale for Why Anthropic Would Compete With the Hyperscalers and Why We Invested As in the cloud era, AI would be massively capital intensive, so it would consolidate to a handful of players with hundreds of apps on top. Microsoft had partnered with OpenAI and Google was building Gemini, while AWS had an undistinctive internal model and had just invested heavily in Anthropic. 5. Why Price Does Not Matter in a World of AI and the Metric That Truly Determines Value Bessemer invested in Abridge at $5M of ARR for a $700M to $800M valuation, in Anthropic at roughly 150 times current ARR, and led Legora's $1.8B round at $20M of ARR. Sameer judges these deals on what the company can become in the next twelve months. How has your relationship to price changed in a world of AI @mignano @mmurph 6. Why the Rise of Open Models Does Not Denigrate the Market for Closed Models In a market this large, predicting the mix between closed and open tokens with any precision is irrelevant. Consumers, enterprises and governments will all want different flavors of intelligence. Bessemer has invested in both Anthropic and Fireworks to hold exposure to closed and open tokens. Love to hear how you think about this @lqiao @bernhardsson 7. Why It Is a Permanent Structural Shift That Companies Will Stay Private for Longer, but Why It Will Open in 2028 With more dollars and more firms in private venture, companies will stay private longer, and most of the run-up in this boom will happen in private markets. Sameer still expects an unlock in IPOs later next year and in 2028. Love to hear your thoughts on this @bgurley and how you see it evolving in 24 months? (links in comments)

Pricing in venture is wilder than ever. $100M pre-seed is now normal. Bessemer just raised $1.75BN for “early” and $4BN for growth. So can you even do seed today without $1BN? - Is capital truly one of the last remaining moats? - Do all small seed funds and mid-sized firms just fade into irrelevance? - Does entry price matter anymore when there are $TRN companies… - Is this real or a bubble that is about to burst I sat down with @spdholakia to discuss this and have added my notes from the discussion below. 1. This Is a Supercycle Like Never Before Sameer has spent 31 years in software and has felt magic only a handful of times. A trillion-dollar supercycle followed every one of those moments, and he notes that it does not happen very often. What is different and what is similar to prior cycles @altcap @infoarbitrage 2. Capital Is a Moat Today Markets pick winners and the choice becomes a self-fulfilling prophecy, as it did when developers settled on SendGrid and Twilio. In legal AI, big law firms name just two major providers. Capital lets leaders scale at an extraordinary pace and build a brand, which draws talent and customers in a reinforcing flywheel. Do you agree with this @venkyganesan @dafrankel 3. Who Wins and Who Loses in the Future of Venture Capital? As industries professionalize, firms must be either a giant global platform that consolidates or an extreme specialist, such as an early-stage AI investor in San Francisco. Sameer counts Bessemer as a big global platform. Firms caught in between face a hard world to make money. Is it go big or go home @rodriscoll @rabois @chetanp 4. The Rationale for Why Anthropic Would Compete With the Hyperscalers and Why We Invested As in the cloud era, AI would be massively capital intensive, so it would consolidate to a handful of players with hundreds of apps on top. Microsoft had partnered with OpenAI and Google was building Gemini, while AWS had an undistinctive internal model and had just invested heavily in Anthropic. 5. Why Price Does Not Matter in a World of AI and the Metric That Truly Determines Value Bessemer invested in Abridge at $5M of ARR for a $700M to $800M valuation, in Anthropic at roughly 150 times current ARR, and led Legora's $1.8B round at $20M of ARR. Sameer judges these deals on what the company can become in the next twelve months. How has your relationship to price changed in a world of AI @mignano @mmurph 6. Why the Rise of Open Models Does Not Denigrate the Market for Closed Models In a market this large, predicting the mix between closed and open tokens with any precision is irrelevant. Consumers, enterprises and governments will all want different flavors of intelligence. Bessemer has invested in both Anthropic and Fireworks to hold exposure to closed and open tokens. Love to hear how you think about this @lqiao @bernhardsson 7. Why It Is a Permanent Structural Shift That Companies Will Stay Private for Longer, but Why It Will Open in 2028 With more dollars and more firms in private venture, companies will stay private longer, and most of the run-up in this boom will happen in private markets. Sameer still expects an unlock in IPOs later next year and in 2028. Love to hear your thoughts on this @bgurley and how you see it evolving in 24 months? (links in comments)

Pricing in venture is wilder than ever. $100M pre-seed is now normal. Bessemer just raised $1.75BN for “early” and $4BN for growth. So can you even do seed today without $1BN? - Is capital truly one of the last remaining moats? - Do all small seed funds and mid-sized firms just fade into irrelevance? - Does entry price matter anymore when there are $TRN companies… - Is this real or a bubble that is about to burst I sat down with @spdholakia to discuss this and have added my notes from the discussion below. 1. This Is a Supercycle Like Never Before Sameer has spent 31 years in software and has felt magic only a handful of times. A trillion-dollar supercycle followed every one of those moments, and he notes that it does not happen very often. What is different and what is similar to prior cycles @altcap @infoarbitrage 2. Capital Is a Moat Today Markets pick winners and the choice becomes a self-fulfilling prophecy, as it did when developers settled on SendGrid and Twilio. In legal AI, big law firms name just two major providers. Capital lets leaders scale at an extraordinary pace and build a brand, which draws talent and customers in a reinforcing flywheel. Do you agree with this @venkyganesan @dafrankel 3. Who Wins and Who Loses in the Future of Venture Capital? As industries professionalize, firms must be either a giant global platform that consolidates or an extreme specialist, such as an early-stage AI investor in San Francisco. Sameer counts Bessemer as a big global platform. Firms caught in between face a hard world to make money. Is it go big or go home @rodriscoll @rabois @chetanp 4. The Rationale for Why Anthropic Would Compete With the Hyperscalers and Why We Invested As in the cloud era, AI would be massively capital intensive, so it would consolidate to a handful of players with hundreds of apps on top. Microsoft had partnered with OpenAI and Google was building Gemini, while AWS had an undistinctive internal model and had just invested heavily in Anthropic. 5. Why Price Does Not Matter in a World of AI and the Metric That Truly Determines Value Bessemer invested in Abridge at $5M of ARR for a $700M to $800M valuation, in Anthropic at roughly 150 times current ARR, and led Legora's $1.8B round at $20M of ARR. Sameer judges these deals on what the company can become in the next twelve months. How has your relationship to price changed in a world of AI @mignano @mmurph 6. Why the Rise of Open Models Does Not Denigrate the Market for Closed Models In a market this large, predicting the mix between closed and open tokens with any precision is irrelevant. Consumers, enterprises and governments will all want different flavors of intelligence. Bessemer has invested in both Anthropic and Fireworks to hold exposure to closed and open tokens. Love to hear how you think about this @lqiao @bernhardsson 7. Why It Is a Permanent Structural Shift That Companies Will Stay Private for Longer, but Why It Will Open in 2028 With more dollars and more firms in private venture, companies will stay private longer, and most of the run-up in this boom will happen in private markets. Sameer still expects an unlock in IPOs later next year and in 2028. Love to hear your thoughts on this @bgurley and how you see it evolving in 24 months? (links in comments)

I have always considered @milesgrimshaw one of the smartest investors in venture. I remember Sept 2023, Miles came to our recording studio in London having just joined the @benchmark partnership. At the time, he had done one deal; a small company called @LangChain. Unknown to many but Miles raved about the founder @hwchase17. His excitement for the company was palpable. Today the company is worth $1.2BN and serves 35% of the Fortune 500. The other day I sat down with Harrison to discuss the big questions of the day and he allowed me to add my notes from the discussion below. -- 1. What Percent of Developer Salary Do You Spend on Coding Tools and How Has It Changed? LangChain spends roughly 30% of its engineering salary on coding tools, a share that has fallen over the last two months even as token usage has gone up. The team tracks coding agent usage in LangSmith, sets budgets the VP of engineering can raise, and controls the harness. Its in-house cloud coding agent uses prompt caching and routing to cut costs. What will this be in 12 months @BrendanFoody? 2. What Will Happen to the Inference Market? Harrison says he underestimated the demand for inference and for coding agents, both far bigger markets than he expected three or four years ago. Echoing Eric Vishria's line that "Everything just works," he believes any inference provider will work, and he does not expect demand to drop anytime soon. What does no one see about the inference market that everyone should see @spdholakia? 3. Why Harnesses Need to Be Neutral Companies want to be able to switch models, but the harness can be tightly tied to a model, and context today is intricately linked to the harness. The harness connects the model to data, APIs and memory. If memory is built in a way that is linked to the harness, and the harness is linked to the model, the company ends up stuck. Love to hear your thoughts on this one man @EnoReyes 4. Why the Memory Layer Will Become More and More Important and How Instinct Crushed It Memory is rare in agents today but will be incredibly common in the next few years. Instinct and Muse have nailed personalization and learning, so the product feels like it is for the individual user, through its tone or proactive follow-ups. Harrison still sees memory as super early. What does no one see that everyone should see about the state of memory and where it goes @noahrshinn @mvernal? 5. Will We Have One Horizontal Agent or Tons of Vertical Agents in the Consumer Realm? Consumers will have both, with horizontal agents interacting with vertical ones. Most apps should have a vertical agent because they can learn more about how a user interacts with them than a general agent can. Wrapping an app's many APIs in a single natural language agent also gives horizontal agents a better experience than raw APIs. Love to hear your thoughts on this @FDavidsonT 6. What Do Large Enterprises Care Most About Today? Cost has risen as a concern for large enterprises, though it still ranks below accuracy and above latency. Security is a bigger concern for large enterprises but still sits below accuracy and cost. 7. Why Is Benchmark the Best Fund? Benchmark invested in LangChain when it was just Harrison and Ankush, and the founders treated the firm as a third co-founder. As the company grew from two people to around 400, Benchmark has been its most consistent partner. @chetanp what does no one know about how Benchmark works with founders that everyone should know?

I have always considered @milesgrimshaw one of the smartest investors in venture. I remember Sept 2023, Miles came to our recording studio in London having just joined the @benchmark partnership. At the time, he had done one deal; a small company called @LangChain. Unknown to many but Miles raved about the founder @hwchase17. His excitement for the company was palpable. Today the company is worth $1.2BN and serves 35% of the Fortune 500. The other day I sat down with Harrison to discuss the big questions of the day and he allowed me to add my notes from the discussion below. -- 1. What Percent of Developer Salary Do You Spend on Coding Tools and How Has It Changed? LangChain spends roughly 30% of its engineering salary on coding tools, a share that has fallen over the last two months even as token usage has gone up. The team tracks coding agent usage in LangSmith, sets budgets the VP of engineering can raise, and controls the harness. Its in-house cloud coding agent uses prompt caching and routing to cut costs. What will this be in 12 months @BrendanFoody? 2. What Will Happen to the Inference Market? Harrison says he underestimated the demand for inference and for coding agents, both far bigger markets than he expected three or four years ago. Echoing Eric Vishria's line that "Everything just works," he believes any inference provider will work, and he does not expect demand to drop anytime soon. What does no one see about the inference market that everyone should see @spdholakia? 3. Why Harnesses Need to Be Neutral Companies want to be able to switch models, but the harness can be tightly tied to a model, and context today is intricately linked to the harness. The harness connects the model to data, APIs and memory. If memory is built in a way that is linked to the harness, and the harness is linked to the model, the company ends up stuck. Love to hear your thoughts on this one man @EnoReyes 4. Why the Memory Layer Will Become More and More Important and How Instinct Crushed It Memory is rare in agents today but will be incredibly common in the next few years. Instinct and Muse have nailed personalization and learning, so the product feels like it is for the individual user, through its tone or proactive follow-ups. Harrison still sees memory as super early. What does no one see that everyone should see about the state of memory and where it goes @noahrshinn @mvernal? 5. Will We Have One Horizontal Agent or Tons of Vertical Agents in the Consumer Realm? Consumers will have both, with horizontal agents interacting with vertical ones. Most apps should have a vertical agent because they can learn more about how a user interacts with them than a general agent can. Wrapping an app's many APIs in a single natural language agent also gives horizontal agents a better experience than raw APIs. Love to hear your thoughts on this @FDavidsonT 6. What Do Large Enterprises Care Most About Today? Cost has risen as a concern for large enterprises, though it still ranks below accuracy and above latency. Security is a bigger concern for large enterprises but still sits below accuracy and cost. 7. Why Is Benchmark the Best Fund? Benchmark invested in LangChain when it was just Harrison and Ankush, and the founders treated the firm as a third co-founder. As the company grew from two people to around 400, Benchmark has been its most consistent partner. @chetanp what does no one know about how Benchmark works with founders that everyone should know?

I have always considered @milesgrimshaw one of the smartest investors in venture. I remember Sept 2023, Miles came to our recording studio in London having just joined the @benchmark partnership. At the time, he had done one deal; a small company called @LangChain. Unknown to many but Miles raved about the founder @hwchase17. His excitement for the company was palpable. Today the company is worth $1.2BN and serves 35% of the Fortune 500. The other day I sat down with Harrison to discuss the big questions of the day and he allowed me to add my notes from the discussion below. -- 1. What Percent of Developer Salary Do You Spend on Coding Tools and How Has It Changed? LangChain spends roughly 30% of its engineering salary on coding tools, a share that has fallen over the last two months even as token usage has gone up. The team tracks coding agent usage in LangSmith, sets budgets the VP of engineering can raise, and controls the harness. Its in-house cloud coding agent uses prompt caching and routing to cut costs. What will this be in 12 months @BrendanFoody? 2. What Will Happen to the Inference Market? Harrison says he underestimated the demand for inference and for coding agents, both far bigger markets than he expected three or four years ago. Echoing Eric Vishria's line that "Everything just works," he believes any inference provider will work, and he does not expect demand to drop anytime soon. What does no one see about the inference market that everyone should see @spdholakia? 3. Why Harnesses Need to Be Neutral Companies want to be able to switch models, but the harness can be tightly tied to a model, and context today is intricately linked to the harness. The harness connects the model to data, APIs and memory. If memory is built in a way that is linked to the harness, and the harness is linked to the model, the company ends up stuck. Love to hear your thoughts on this one man @EnoReyes 4. Why the Memory Layer Will Become More and More Important and How Instinct Crushed It Memory is rare in agents today but will be incredibly common in the next few years. Instinct and Muse have nailed personalization and learning, so the product feels like it is for the individual user, through its tone or proactive follow-ups. Harrison still sees memory as super early. What does no one see that everyone should see about the state of memory and where it goes @noahrshinn @mvernal? 5. Will We Have One Horizontal Agent or Tons of Vertical Agents in the Consumer Realm? Consumers will have both, with horizontal agents interacting with vertical ones. Most apps should have a vertical agent because they can learn more about how a user interacts with them than a general agent can. Wrapping an app's many APIs in a single natural language agent also gives horizontal agents a better experience than raw APIs. Love to hear your thoughts on this @FDavidsonT 6. What Do Large Enterprises Care Most About Today? Cost has risen as a concern for large enterprises, though it still ranks below accuracy and above latency. Security is a bigger concern for large enterprises but still sits below accuracy and cost. 7. Why Is Benchmark the Best Fund? Benchmark invested in LangChain when it was just Harrison and Ankush, and the founders treated the firm as a third co-founder. As the company grew from two people to around 400, Benchmark has been its most consistent partner. @chetanp what does no one know about how Benchmark works with founders that everyone should know?

I have always considered @milesgrimshaw one of the smartest investors in venture. I remember Sept 2023, Miles came to our recording studio in London having just joined the @benchmark partnership. At the time, he had done one deal; a small company called @LangChain. Unknown to many but Miles raved about the founder @hwchase17. His excitement for the company was palpable. Today the company is worth $1.2BN and serves 35% of the Fortune 500. The other day I sat down with Harrison to discuss the big questions of the day and he allowed me to add my notes from the discussion below. -- 1. What Percent of Developer Salary Do You Spend on Coding Tools and How Has It Changed? LangChain spends roughly 30% of its engineering salary on coding tools, a share that has fallen over the last two months even as token usage has gone up. The team tracks coding agent usage in LangSmith, sets budgets the VP of engineering can raise, and controls the harness. Its in-house cloud coding agent uses prompt caching and routing to cut costs. What will this be in 12 months @BrendanFoody? 2. What Will Happen to the Inference Market? Harrison says he underestimated the demand for inference and for coding agents, both far bigger markets than he expected three or four years ago. Echoing Eric Vishria's line that "Everything just works," he believes any inference provider will work, and he does not expect demand to drop anytime soon. What does no one see about the inference market that everyone should see @spdholakia? 3. Why Harnesses Need to Be Neutral Companies want to be able to switch models, but the harness can be tightly tied to a model, and context today is intricately linked to the harness. The harness connects the model to data, APIs and memory. If memory is built in a way that is linked to the harness, and the harness is linked to the model, the company ends up stuck. Love to hear your thoughts on this one man @EnoReyes 4. Why the Memory Layer Will Become More and More Important and How Instinct Crushed It Memory is rare in agents today but will be incredibly common in the next few years. Instinct and Muse have nailed personalization and learning, so the product feels like it is for the individual user, through its tone or proactive follow-ups. Harrison still sees memory as super early. What does no one see that everyone should see about the state of memory and where it goes @noahrshinn @mvernal? 5. Will We Have One Horizontal Agent or Tons of Vertical Agents in the Consumer Realm? Consumers will have both, with horizontal agents interacting with vertical ones. Most apps should have a vertical agent because they can learn more about how a user interacts with them than a general agent can. Wrapping an app's many APIs in a single natural language agent also gives horizontal agents a better experience than raw APIs. Love to hear your thoughts on this @FDavidsonT 6. What Do Large Enterprises Care Most About Today? Cost has risen as a concern for large enterprises, though it still ranks below accuracy and above latency. Security is a bigger concern for large enterprises but still sits below accuracy and cost. 7. Why Is Benchmark the Best Fund? Benchmark invested in LangChain when it was just Harrison and Ankush, and the founders treated the firm as a third co-founder. As the company grew from two people to around 400, Benchmark has been its most consistent partner. @chetanp what does no one know about how Benchmark works with founders that everyone should know?

I have always considered @milesgrimshaw one of the smartest investors in venture. I remember Sept 2023, Miles came to our recording studio in London having just joined the @benchmark partnership. At the time, he had done one deal; a small company called @LangChain. Unknown to many but Miles raved about the founder @hwchase17. His excitement for the company was palpable. Today the company is worth $1.2BN and serves 35% of the Fortune 500. The other day I sat down with Harrison to discuss the big questions of the day and he allowed me to add my notes from the discussion below. -- 1. What Percent of Developer Salary Do You Spend on Coding Tools and How Has It Changed? LangChain spends roughly 30% of its engineering salary on coding tools, a share that has fallen over the last two months even as token usage has gone up. The team tracks coding agent usage in LangSmith, sets budgets the VP of engineering can raise, and controls the harness. Its in-house cloud coding agent uses prompt caching and routing to cut costs. What will this be in 12 months @BrendanFoody? 2. What Will Happen to the Inference Market? Harrison says he underestimated the demand for inference and for coding agents, both far bigger markets than he expected three or four years ago. Echoing Eric Vishria's line that "Everything just works," he believes any inference provider will work, and he does not expect demand to drop anytime soon. What does no one see about the inference market that everyone should see @spdholakia? 3. Why Harnesses Need to Be Neutral Companies want to be able to switch models, but the harness can be tightly tied to a model, and context today is intricately linked to the harness. The harness connects the model to data, APIs and memory. If memory is built in a way that is linked to the harness, and the harness is linked to the model, the company ends up stuck. Love to hear your thoughts on this one man @EnoReyes 4. Why the Memory Layer Will Become More and More Important and How Instinct Crushed It Memory is rare in agents today but will be incredibly common in the next few years. Instinct and Muse have nailed personalization and learning, so the product feels like it is for the individual user, through its tone or proactive follow-ups. Harrison still sees memory as super early. What does no one see that everyone should see about the state of memory and where it goes @noahrshinn @mvernal? 5. Will We Have One Horizontal Agent or Tons of Vertical Agents in the Consumer Realm? Consumers will have both, with horizontal agents interacting with vertical ones. Most apps should have a vertical agent because they can learn more about how a user interacts with them than a general agent can. Wrapping an app's many APIs in a single natural language agent also gives horizontal agents a better experience than raw APIs. Love to hear your thoughts on this @FDavidsonT 6. What Do Large Enterprises Care Most About Today? Cost has risen as a concern for large enterprises, though it still ranks below accuracy and above latency. Security is a bigger concern for large enterprises but still sits below accuracy and cost. 7. Why Is Benchmark the Best Fund? Benchmark invested in LangChain when it was just Harrison and Ankush, and the founders treated the firm as a third co-founder. As the company grew from two people to around 400, Benchmark has been its most consistent partner. @chetanp what does no one know about how Benchmark works with founders that everyone should know?

For the last 5 decades straight, we have created (as a society) circa 20 decacorns per decade. In the last 12 months, we have created 42. The world is changing faster than ever before and if you do not stay up to date. You will be left behind. That is why I created this show with Jason and Rory. This is the only show in tech, you have to listen to every week. No egos, no politics, just tech. On the agenda: - Valar Atomics Sues Day One Ventures - Meta Returns to the AI Frontier With Muse Wonder - NVIDIA Eyes $30B Reflection Deal - Jev Raises $870M at a $7.5B Valuation My notes below with @jasonlk and @rodriscoll 1. We Generated 20 $10B Companies per Decade for the Last 5 Decades and Now 42 in a Year Tech produced roughly 20 decacorns per decade for five decades, yet 42 have been created in less than a year of this decade. Only about 130 tech companies have ever been worth $10B or more. Rising market caps explain part of the jump, but the pace is stunning. 2. Who Is Right and Who Is Wrong: Day One or Valar? X wrongly claimed Day One was suing its portfolio company, when Valar Atomics wants a declaratory judgment to move on. Waiving pro rata for Sequoia is common, but letting an existing investor in as a strategic is playing favorites. Still, once founders decide, early investors must get with the program because it is not their company. 3. Why Meta's New Model Is a Zero Meta's new model scores well on evals, but there is no API and it can only be used inside Muse. A model claiming parity at the same price, not cheaper and not usable by developers, is the height of performative evals. 4. Why We Should Lead Jev's Round at 8 Billion Jev is a super fast, super cheap classifier, accurate enough for maybe 10% of AI use cases. Cloudflare and Amazon can match it today, but the bet is that a focused team out of OpenAI pulls ahead while those efforts stay niche. If Jev becomes hyper accurate, it changes the game. 5. What Is the Single Greatest Potential for AI? Drug discovery is probably AI's largest opportunity, with drugs that dramatically extend longevity and quality of life. If AI companies can accelerate discovery and own the IP while others do the hard work, AI could reboot the industry. Trials would not speed up, but the prize is a GLP-1 out five years faster. 6. Haiku 5.5 Is the First Time AI Has Been Deflationary Falling token prices have been offset by consuming far more tokens, so AI costs have not fallen. Haiku 5.5 is almost as good as Sonnet for many use cases at a tenth of the price, and Sonnet 5.5 is twice as fast. After a 90% price cut, usage must rise 10 times for revenue to hold. 7. Why the SaaSpocalypse Is Worse Than We Thought The SaaSpocalypse passed only for companies well above $1B in revenue, and everyone without massive scale is living a worse one than in March or April. Leaders will acquire some companies and grind out the rest. A safe software IPO may now need $1.5B to $2B of ARR rather than $500M. (links in comments)

For the last 5 decades straight, we have created (as a society) circa 20 decacorns per decade. In the last 12 months, we have created 42. The world is changing faster than ever before and if you do not stay up to date. You will be left behind. That is why I created this show with Jason and Rory. This is the only show in tech, you have to listen to every week. No egos, no politics, just tech. On the agenda: - Valar Atomics Sues Day One Ventures - Meta Returns to the AI Frontier With Muse Wonder - NVIDIA Eyes $30B Reflection Deal - Jev Raises $870M at a $7.5B Valuation My notes below with @jasonlk and @rodriscoll 1. We Generated 20 $10B Companies per Decade for the Last 5 Decades and Now 42 in a Year Tech produced roughly 20 decacorns per decade for five decades, yet 42 have been created in less than a year of this decade. Only about 130 tech companies have ever been worth $10B or more. Rising market caps explain part of the jump, but the pace is stunning. 2. Who Is Right and Who Is Wrong: Day One or Valar? X wrongly claimed Day One was suing its portfolio company, when Valar Atomics wants a declaratory judgment to move on. Waiving pro rata for Sequoia is common, but letting an existing investor in as a strategic is playing favorites. Still, once founders decide, early investors must get with the program because it is not their company. 3. Why Meta's New Model Is a Zero Meta's new model scores well on evals, but there is no API and it can only be used inside Muse. A model claiming parity at the same price, not cheaper and not usable by developers, is the height of performative evals. 4. Why We Should Lead Jev's Round at 8 Billion Jev is a super fast, super cheap classifier, accurate enough for maybe 10% of AI use cases. Cloudflare and Amazon can match it today, but the bet is that a focused team out of OpenAI pulls ahead while those efforts stay niche. If Jev becomes hyper accurate, it changes the game. 5. What Is the Single Greatest Potential for AI? Drug discovery is probably AI's largest opportunity, with drugs that dramatically extend longevity and quality of life. If AI companies can accelerate discovery and own the IP while others do the hard work, AI could reboot the industry. Trials would not speed up, but the prize is a GLP-1 out five years faster. 6. Haiku 5.5 Is the First Time AI Has Been Deflationary Falling token prices have been offset by consuming far more tokens, so AI costs have not fallen. Haiku 5.5 is almost as good as Sonnet for many use cases at a tenth of the price, and Sonnet 5.5 is twice as fast. After a 90% price cut, usage must rise 10 times for revenue to hold. 7. Why the SaaSpocalypse Is Worse Than We Thought The SaaSpocalypse passed only for companies well above $1B in revenue, and everyone without massive scale is living a worse one than in March or April. Leaders will acquire some companies and grind out the rest. A safe software IPO may now need $1.5B to $2B of ARR rather than $500M. (links in comments)

For the last 5 decades straight, we have created (as a society) circa 20 decacorns per decade. In the last 12 months, we have created 42. The world is changing faster than ever before and if you do not stay up to date. You will be left behind. That is why I created this show with Jason and Rory. This is the only show in tech, you have to listen to every week. No egos, no politics, just tech. On the agenda: - Valar Atomics Sues Day One Ventures - Meta Returns to the AI Frontier With Muse Wonder - NVIDIA Eyes $30B Reflection Deal - Jev Raises $870M at a $7.5B Valuation My notes below with @jasonlk and @rodriscoll 1. We Generated 20 $10B Companies per Decade for the Last 5 Decades and Now 42 in a Year Tech produced roughly 20 decacorns per decade for five decades, yet 42 have been created in less than a year of this decade. Only about 130 tech companies have ever been worth $10B or more. Rising market caps explain part of the jump, but the pace is stunning. 2. Who Is Right and Who Is Wrong: Day One or Valar? X wrongly claimed Day One was suing its portfolio company, when Valar Atomics wants a declaratory judgment to move on. Waiving pro rata for Sequoia is common, but letting an existing investor in as a strategic is playing favorites. Still, once founders decide, early investors must get with the program because it is not their company. 3. Why Meta's New Model Is a Zero Meta's new model scores well on evals, but there is no API and it can only be used inside Muse. A model claiming parity at the same price, not cheaper and not usable by developers, is the height of performative evals. 4. Why We Should Lead Jev's Round at 8 Billion Jev is a super fast, super cheap classifier, accurate enough for maybe 10% of AI use cases. Cloudflare and Amazon can match it today, but the bet is that a focused team out of OpenAI pulls ahead while those efforts stay niche. If Jev becomes hyper accurate, it changes the game. 5. What Is the Single Greatest Potential for AI? Drug discovery is probably AI's largest opportunity, with drugs that dramatically extend longevity and quality of life. If AI companies can accelerate discovery and own the IP while others do the hard work, AI could reboot the industry. Trials would not speed up, but the prize is a GLP-1 out five years faster. 6. Haiku 5.5 Is the First Time AI Has Been Deflationary Falling token prices have been offset by consuming far more tokens, so AI costs have not fallen. Haiku 5.5 is almost as good as Sonnet for many use cases at a tenth of the price, and Sonnet 5.5 is twice as fast. After a 90% price cut, usage must rise 10 times for revenue to hold. 7. Why the SaaSpocalypse Is Worse Than We Thought The SaaSpocalypse passed only for companies well above $1B in revenue, and everyone without massive scale is living a worse one than in March or April. Leaders will acquire some companies and grind out the rest. A safe software IPO may now need $1.5B to $2B of ARR rather than $500M. (links in comments)