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There is a massive misunderstanding about what $APP actually is, and the recent Adam Foroughi interview just handed investors as a class in capital allocation, efficiency, and strategic vertical integration. At its core, AppLovin isn’t solely an ad-network or a gaming business but an arbitrage engine. APP is essentially...

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24,419 views • 4 months ago •via X (Twitter)

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The recent 20VC interview with Adam Foroughi is an incredible example of capital allocation and lean operations. $APP is operating in a league of its own right now. We are talking about a business that generated $5.48B in 2025 revenue and is currently doing roughly $10 M in EBITDA per employee. They operate with a total headcount of around 895 people, but the core advertising unit that prints the cash is only about ~400 people. You never see software companies at a $150 B market cap running this lean. The 84% EBITDA margin profile is completely disconnected from traditional enterprise SaaS norms. The competitive moat is their recommendation engine, AXON 2.0. The advertising environment is brutal. $META and TikTok have elite targeting engines. TikTok’s algorithm is famous for engagement without a social graph. But AppLovin has built an engine that processes over 2M ad auctions per second, optimizing heavily for performance and incrementality. When the stock completely collapsed by 92% in 2022 management did not panic. Instead, they ruthlessly threw out their old ML infrastructure and rebuilt AXON from the ground up. They ignored the noise, shut down investor relations temporarily to focus internally, and executed one of the highest ROI buybacks in modern corporate history. They targeted private market VC sellers who were desperate for liquidity. That specific buyback maneuver alone accounts for roughly $50 BILLION of the company's current market value. Their internal culture is intensely anti-bloat. They have zero traditional management layers. There is no CRO, no COO, no CMO, and no Chief Human Resources Officer. Foroughi cut HR from roughly 80 people down to 15. The product team does not exist. Engineers are required to act as product managers. If an engineer cannot understand the business KPIs that drive revenue, they do not belong there. AI enables them to eliminate process-heavy roles. They demand that only their top 10% to 15% of employees take equity risk, while everyone else gets cash compensation. This keeps their SBC tightly capped at around $300 M annually. For a company valued at over $150 B, that dilution rate is almost a rounding error. Foroughi values the business strictly on cash flow minus SBC. It is the absolute cleanest metric. The bearish narrative assumes AI makes game and app creation easier, which somehow dilutes the value of AppLovin's ecosystem. Foroughi counters that cheaper content creation actually explodes the volume of content, making discovery engines like AXON infinitely more valuable. If they expand this performance-based advertising model into connected TV and the broader $170 B e-commerce sector via their Axon Ads Manager, the path to a trillion-dollar valuation becomes very achievable without even needing to build a native social network. Great interview! Really appreciate hearing Adam.
2:06

The recent 20VC interview with Adam Foroughi is an incredible example of capital allocation and lean operations. $APP is operating in a league of its own right now. We are talking about a business that generated $5.48B in 2025 revenue and is currently doing roughly $10 M in EBITDA per employee. They operate with a total headcount of around 895 people, but the core advertising unit that prints the cash is only about ~400 people. You never see software companies at a $150 B market cap running this lean. The 84% EBITDA margin profile is completely disconnected from traditional enterprise SaaS norms. The competitive moat is their recommendation engine, AXON 2.0. The advertising environment is brutal. $META and TikTok have elite targeting engines. TikTok’s algorithm is famous for engagement without a social graph. But AppLovin has built an engine that processes over 2M ad auctions per second, optimizing heavily for performance and incrementality. When the stock completely collapsed by 92% in 2022 management did not panic. Instead, they ruthlessly threw out their old ML infrastructure and rebuilt AXON from the ground up. They ignored the noise, shut down investor relations temporarily to focus internally, and executed one of the highest ROI buybacks in modern corporate history. They targeted private market VC sellers who were desperate for liquidity. That specific buyback maneuver alone accounts for roughly $50 BILLION of the company's current market value. Their internal culture is intensely anti-bloat. They have zero traditional management layers. There is no CRO, no COO, no CMO, and no Chief Human Resources Officer. Foroughi cut HR from roughly 80 people down to 15. The product team does not exist. Engineers are required to act as product managers. If an engineer cannot understand the business KPIs that drive revenue, they do not belong there. AI enables them to eliminate process-heavy roles. They demand that only their top 10% to 15% of employees take equity risk, while everyone else gets cash compensation. This keeps their SBC tightly capped at around $300 M annually. For a company valued at over $150 B, that dilution rate is almost a rounding error. Foroughi values the business strictly on cash flow minus SBC. It is the absolute cleanest metric. The bearish narrative assumes AI makes game and app creation easier, which somehow dilutes the value of AppLovin's ecosystem. Foroughi counters that cheaper content creation actually explodes the volume of content, making discovery engines like AXON infinitely more valuable. If they expand this performance-based advertising model into connected TV and the broader $170 B e-commerce sector via their Axon Ads Manager, the path to a trillion-dollar valuation becomes very achievable without even needing to build a native social network. Great interview! Really appreciate hearing Adam.

CapexAndChill

10,744 views • 4 months ago

$APP's pivot to e-commerce is a very intriguing strategy. The market perceives the entry of non-gaming advertisers as a potential friction point that could crowd out core gaming clients or inflate pricing, but this ignores the massive asymmetry between AppLovin’s reach of over one billion daily active users and its historically thin roster of active advertisers. This imbalance has meant that the vast amount of inventory were previously under-monetized, as the algorithm could only serve gaming ads to users who had no intent to install new games. By layering in e-commerce demand, the platform effectively monetizes this wasted inventory, driving up overall yield and floor prices without cannibalizing the high-intent impressions reserved for gaming clients. This creates a margin-accretive dynamic where the same unit of supply generates significantly higher revenue per user solely through better demand matching. This efficiency gain feeds directly into a data-driven moat that becomes increasingly difficult for competitors to replicate. The flywheel is the introduction of transactional e-commerce data that radically improves the Axon AI model's predictive capabilities for all participants. Unlike app install data, which is binary and relatively sparse, e-commerce purchase data provides immediate high-fidelity signals about user intent and purchasing power. As the model ingests this new layer of behavioral data, its ability to predict conversion improves universally, meaning that gaming advertisers actually benefit from the presence of e-commerce bids through sharper targeting and higher return on ad spend. The rapid 50% week over week growth in the self-service pilot is a great preliminary validation that this automated demand engine is functional. This signals that AppLovin can scale this new vertical with software operating leverage. The requirement for high-production video ads has left out the long tail of millions of small business advertisers who dominate platforms like $META. The launch of generative AI creative tools targets this specific bottleneck, commoditizing the production of high-performing video assets and allowing AppLovin to unlock global SMB demand instantly. If successful, this creates a self-reinforcing liquidity cycle where increased advertiser density leads to better data, which drives superior model performance, which in turn attracts more diverse advertisers. This helps decouple the company's growth trajectory from the cyclicality of the mobile gaming market. Really interesting biz and great CEO.
5:53

$APP's pivot to e-commerce is a very intriguing strategy. The market perceives the entry of non-gaming advertisers as a potential friction point that could crowd out core gaming clients or inflate pricing, but this ignores the massive asymmetry between AppLovin’s reach of over one billion daily active users and its historically thin roster of active advertisers. This imbalance has meant that the vast amount of inventory were previously under-monetized, as the algorithm could only serve gaming ads to users who had no intent to install new games. By layering in e-commerce demand, the platform effectively monetizes this wasted inventory, driving up overall yield and floor prices without cannibalizing the high-intent impressions reserved for gaming clients. This creates a margin-accretive dynamic where the same unit of supply generates significantly higher revenue per user solely through better demand matching. This efficiency gain feeds directly into a data-driven moat that becomes increasingly difficult for competitors to replicate. The flywheel is the introduction of transactional e-commerce data that radically improves the Axon AI model's predictive capabilities for all participants. Unlike app install data, which is binary and relatively sparse, e-commerce purchase data provides immediate high-fidelity signals about user intent and purchasing power. As the model ingests this new layer of behavioral data, its ability to predict conversion improves universally, meaning that gaming advertisers actually benefit from the presence of e-commerce bids through sharper targeting and higher return on ad spend. The rapid 50% week over week growth in the self-service pilot is a great preliminary validation that this automated demand engine is functional. This signals that AppLovin can scale this new vertical with software operating leverage. The requirement for high-production video ads has left out the long tail of millions of small business advertisers who dominate platforms like $META. The launch of generative AI creative tools targets this specific bottleneck, commoditizing the production of high-performing video assets and allowing AppLovin to unlock global SMB demand instantly. If successful, this creates a self-reinforcing liquidity cycle where increased advertiser density leads to better data, which drives superior model performance, which in turn attracts more diverse advertisers. This helps decouple the company's growth trajectory from the cyclicality of the mobile gaming market. Really interesting biz and great CEO.

CapexAndChill

29,897 views • 7 months ago

$NOW's Financial Analyst Day took place yesterday. A huge focus was on the headline $30 B+ subscription revenue target for 2030, but there is also a margin expansion story as well that management highlighted. The market fears AI inference costs will compress software gross margins. Management focused on dismantling this narrative. AI reasoning represents less than 10% of their cost to serve. The other ~90% is workflow orchestration, governance, and their 20-year CMDB context. They are maintaining 80%+ subscription gross margins while pulling $300 M in annualized agentic AI cost savings straight to their own bottom line for 2026. That self-funded internal efficiency gives them the exact cover needed to commit to 100 basis points of non-GAAP operating and free cash flow margin expansion in 2027. The debate over seat compression versus consumption is looking promising for NOW. ServiceNow has shifted to a hybrid model. Non-seat based pricing already accounts for 50% of their net new ACV. When a customer uses AI to cut a 20 person support team down to five, ServiceNow captures 6.5x more in AI agent consumption. The total spend from that customer actually grows over 5x by year five. This underlying consumption momentum is exactly why management aggressively raised their 2026 AI ACV target from $1 B to $1.5B. They expect AI to drive 30% of total ACV by 2030. They are backing this up with a new go-to-market execution strategy, guaranteeing total satisfaction for AI go-lives in under 100 days. Management is also trying to be more disciplined with capital allocation. They are tackling dilution. They hit their sub-15% stock-based compensation target early in 2025 and just established a hard target of sub-10% by 2029. They doubled their share repurchases with a $2 B accelerated share repurchase in Q1 2026 alone. This move makes them dilution net-neutral for the entirety of 2026. They still have $4.2B in authorization ready. Recent tuck-in acquisitions like Moveworks, Vza, and Armis were heavily scrutinized as buying top-line growth. Management confirmed zero revenue from these hit the last report. They bought them strictly to build out the AI Control Tower and push their TAM to an aggressive $600 B. Overall, the day provided a little more clarity and I appreciated it. Looking more interesting to me. In the clip, Gina addressed seat compression and the margin expansion story.
12:07

$NOW's Financial Analyst Day took place yesterday. A huge focus was on the headline $30 B+ subscription revenue target for 2030, but there is also a margin expansion story as well that management highlighted. The market fears AI inference costs will compress software gross margins. Management focused on dismantling this narrative. AI reasoning represents less than 10% of their cost to serve. The other ~90% is workflow orchestration, governance, and their 20-year CMDB context. They are maintaining 80%+ subscription gross margins while pulling $300 M in annualized agentic AI cost savings straight to their own bottom line for 2026. That self-funded internal efficiency gives them the exact cover needed to commit to 100 basis points of non-GAAP operating and free cash flow margin expansion in 2027. The debate over seat compression versus consumption is looking promising for NOW. ServiceNow has shifted to a hybrid model. Non-seat based pricing already accounts for 50% of their net new ACV. When a customer uses AI to cut a 20 person support team down to five, ServiceNow captures 6.5x more in AI agent consumption. The total spend from that customer actually grows over 5x by year five. This underlying consumption momentum is exactly why management aggressively raised their 2026 AI ACV target from $1 B to $1.5B. They expect AI to drive 30% of total ACV by 2030. They are backing this up with a new go-to-market execution strategy, guaranteeing total satisfaction for AI go-lives in under 100 days. Management is also trying to be more disciplined with capital allocation. They are tackling dilution. They hit their sub-15% stock-based compensation target early in 2025 and just established a hard target of sub-10% by 2029. They doubled their share repurchases with a $2 B accelerated share repurchase in Q1 2026 alone. This move makes them dilution net-neutral for the entirety of 2026. They still have $4.2B in authorization ready. Recent tuck-in acquisitions like Moveworks, Vza, and Armis were heavily scrutinized as buying top-line growth. Management confirmed zero revenue from these hit the last report. They bought them strictly to build out the AI Control Tower and push their TAM to an aggressive $600 B. Overall, the day provided a little more clarity and I appreciated it. Looking more interesting to me. In the clip, Gina addressed seat compression and the margin expansion story.

CapexAndChill

20,719 views • 3 months ago

$HEI has been one of the best compounders of the last 3 decades. A $10K investment in 1990 is worth over $8M today. Listened to this interview with Co-President Eric Mendelson and its pretty clear why this has been such a strong business. The business model is very simple but extremely difficult to replicate. In 1990, HEICO was a struggling business with a ~$25M market cap. Management discovered a regulatory loophole to challenge aerospace OEMs. They used FAA regulation to engineer replacement aerospace parts. They proved to the FAA that their parts strictly matched the fit, form, and function of OEM parts. This broke the OEM pricing monopolies. They have shipped 85M parts with zero in-flight failures. The capital allocation strategy is the secret sauce behind the stock. The Mendelson family runs the company, but they refuse to operate it like a traditional family business. Larry, Eric, and Victor Mendelson require a unanimous three-way vote for any major decision. If one says no, the deal dies instantly. This strict filter kills bad capital deployment. They have acquired 100 companies over the years. 98 of those acquisitions have been successful. They focus exclusively on businesses generating 20% or higher margins. They also run the balance sheet with extreme discipline. They try to target roughly a 1x debt-to-EBITDA ratio. This protects the equity from macroeconomic debt cycles. HEICO operates like a highly decentralized portfolio. It does not run like a typical billion dollar aerospace behemoth. It runs as roughly 140 separate businesses. Each unit makes unde $100M and employs arround less than 100 people. Corporate buys companies led by great operators and leaves them completely alone. They do not force typical corporate integration or micromanagement. They also build extreme employee loyalty. Their recent M&A execution proves the decentralized model scales very well. In August 2023, HEICO acquired Wencor for just over $2.05B from private equity firm. They paid almost 13x EBITDA. This was the highest multiple they ever paid for an asset. However, Wencor has outperformed expectations so aggressively that the effective multiple is now in the single digits. Management also eliminates geopolitical risk. They refuse to manufacture anything in China. They actively protect their intellectual property from theft. Mendelson noted that China is at least 20 years away from matching western commercial aircraft technology, and engine technology parity will not happen in his lifetime.
2:53

$HEI has been one of the best compounders of the last 3 decades. A $10K investment in 1990 is worth over $8M today. Listened to this interview with Co-President Eric Mendelson and its pretty clear why this has been such a strong business. The business model is very simple but extremely difficult to replicate. In 1990, HEICO was a struggling business with a ~$25M market cap. Management discovered a regulatory loophole to challenge aerospace OEMs. They used FAA regulation to engineer replacement aerospace parts. They proved to the FAA that their parts strictly matched the fit, form, and function of OEM parts. This broke the OEM pricing monopolies. They have shipped 85M parts with zero in-flight failures. The capital allocation strategy is the secret sauce behind the stock. The Mendelson family runs the company, but they refuse to operate it like a traditional family business. Larry, Eric, and Victor Mendelson require a unanimous three-way vote for any major decision. If one says no, the deal dies instantly. This strict filter kills bad capital deployment. They have acquired 100 companies over the years. 98 of those acquisitions have been successful. They focus exclusively on businesses generating 20% or higher margins. They also run the balance sheet with extreme discipline. They try to target roughly a 1x debt-to-EBITDA ratio. This protects the equity from macroeconomic debt cycles. HEICO operates like a highly decentralized portfolio. It does not run like a typical billion dollar aerospace behemoth. It runs as roughly 140 separate businesses. Each unit makes unde $100M and employs arround less than 100 people. Corporate buys companies led by great operators and leaves them completely alone. They do not force typical corporate integration or micromanagement. They also build extreme employee loyalty. Their recent M&A execution proves the decentralized model scales very well. In August 2023, HEICO acquired Wencor for just over $2.05B from private equity firm. They paid almost 13x EBITDA. This was the highest multiple they ever paid for an asset. However, Wencor has outperformed expectations so aggressively that the effective multiple is now in the single digits. Management also eliminates geopolitical risk. They refuse to manufacture anything in China. They actively protect their intellectual property from theft. Mendelson noted that China is at least 20 years away from matching western commercial aircraft technology, and engine technology parity will not happen in his lifetime.

CapexAndChill

19,006 views • 4 months ago

.John Coogan says the recent reporting on Meta's 'tokenmaxxing' is less of a sign of bad incentives at the company, and more of a tell about its potential strategy for more vertical integration: "I think it makes clearer the strategy with MSL. Because it's clear that they're spending hundreds of millions of dollars on this, just for internal code-gen tooling — like, running their business." "They are going to spend an inordinate amount of money on frontier inference, and so [by] training a model there, they will be able to amortize the training cost of the next model they build not just over, 'Can they get a product out that goes viral and becomes its own standalone chat app that people pay for.'" "Just on the internal usage, they could be running a multi-billion-dollar token bill that they would have to pay another lab. And so if they develop that internally, it's pure vertical integration." "And then you also have everything that's happening on the actual ad targeting and content delivery side..." "The big question has been like, 'Is Meta going to be able to launch an entirely new AI product like Vibes or something like that?'" "And this is a data point that, to me, says they don't need to. Because just from a pure vertical integration [perspective], the investment in MSL can pencil out."
1:29

.John Coogan says the recent reporting on Meta's 'tokenmaxxing' is less of a sign of bad incentives at the company, and more of a tell about its potential strategy for more vertical integration: "I think it makes clearer the strategy with MSL. Because it's clear that they're spending hundreds of millions of dollars on this, just for internal code-gen tooling — like, running their business." "They are going to spend an inordinate amount of money on frontier inference, and so [by] training a model there, they will be able to amortize the training cost of the next model they build not just over, 'Can they get a product out that goes viral and becomes its own standalone chat app that people pay for.'" "Just on the internal usage, they could be running a multi-billion-dollar token bill that they would have to pay another lab. And so if they develop that internally, it's pure vertical integration." "And then you also have everything that's happening on the actual ad targeting and content delivery side..." "The big question has been like, 'Is Meta going to be able to launch an entirely new AI product like Vibes or something like that?'" "And this is a data point that, to me, says they don't need to. Because just from a pure vertical integration [perspective], the investment in MSL can pencil out."

TBPN

32,654 views • 4 months ago

$MELI management always shares so much value. I hope Leandro does more of these interviews and shout out to Couch Investor🛋️ for making this one. Here are some key insights that most investors are unaware of for $MELI from this interview. Two metrics prove how early Mercado Libre actually is in its growth cycle despite its $86B valuation. Leandro admitted they are only at 1% to 2% of their ultimate goal in using AI to connect Mercado Pago and Mercado Libre data for hyper-personalized consumer experiences. We can potentially see new verticals spin up over the next few years just in this domain alone. Even with their aggressively growing $15B credit book, they are still only the 6th largest credit card issuer in Brazil. The 5th largest competitor is still three times their size! This confirms they have an immense amount of runway left before they hit a market share ceiling. Wall Street models focus heavily on existing market growth, but Mercado Libre has a massive, unactivated pipeline within its current footprint. They only recently launched credit cards in Argentina in August, and the product is not even available in major markets like Chile or Colombia yet. Beyond their current active zones, there is a combined population of ~130M people in secondary Latin American countries where Mercado Libre currently has very little presence. This represents an untapped market the exact size of a "second Mexico" that is entirely ready for future ecosystem expansion. There is not a single analyst pricing this in. Management is highly skeptical of corporate acquisitions. Leandro noted that mergers and acquisitions fail to create value half of the time, so their default strategy is to build their technology and networks in-house. They will only buy an external company if there is a severe "strategic urgency." For example, they acquired a Brazilian network of local mom-and-pop shops purely to establish instant drop-off points for apparel returns, which saved them years of organic development time. Otherwise, they do not rely on buyouts for growth. In Latin America, roughly half of the economy is informal. This means citizens do not have traditional payroll stubs, making it very difficult for standard banks to accurately assess their risk. Mercado Libre’s competitive alpha is that they do not underwrite random people. Instead, they issue credit exclusively based on proprietary behavioral data gathered from within their own closed-loop marketplace. Because they can see exactly how a user buys and sells on the platform, they have a level of granular insight into the real economy that traditional financial institutions cannot match.
3:14

$MELI management always shares so much value. I hope Leandro does more of these interviews and shout out to Couch Investor🛋️ for making this one. Here are some key insights that most investors are unaware of for $MELI from this interview. Two metrics prove how early Mercado Libre actually is in its growth cycle despite its $86B valuation. Leandro admitted they are only at 1% to 2% of their ultimate goal in using AI to connect Mercado Pago and Mercado Libre data for hyper-personalized consumer experiences. We can potentially see new verticals spin up over the next few years just in this domain alone. Even with their aggressively growing $15B credit book, they are still only the 6th largest credit card issuer in Brazil. The 5th largest competitor is still three times their size! This confirms they have an immense amount of runway left before they hit a market share ceiling. Wall Street models focus heavily on existing market growth, but Mercado Libre has a massive, unactivated pipeline within its current footprint. They only recently launched credit cards in Argentina in August, and the product is not even available in major markets like Chile or Colombia yet. Beyond their current active zones, there is a combined population of ~130M people in secondary Latin American countries where Mercado Libre currently has very little presence. This represents an untapped market the exact size of a "second Mexico" that is entirely ready for future ecosystem expansion. There is not a single analyst pricing this in. Management is highly skeptical of corporate acquisitions. Leandro noted that mergers and acquisitions fail to create value half of the time, so their default strategy is to build their technology and networks in-house. They will only buy an external company if there is a severe "strategic urgency." For example, they acquired a Brazilian network of local mom-and-pop shops purely to establish instant drop-off points for apparel returns, which saved them years of organic development time. Otherwise, they do not rely on buyouts for growth. In Latin America, roughly half of the economy is informal. This means citizens do not have traditional payroll stubs, making it very difficult for standard banks to accurately assess their risk. Mercado Libre’s competitive alpha is that they do not underwrite random people. Instead, they issue credit exclusively based on proprietary behavioral data gathered from within their own closed-loop marketplace. Because they can see exactly how a user buys and sells on the platform, they have a level of granular insight into the real economy that traditional financial institutions cannot match.

CapexAndChill

15,782 views • 2 months ago

David Friedberg: “Gaming is the future of entertainment, and the future of gaming is AI.” @jason: “Friedberg, what are your thoughts on the gaming industry versus social media versus traditional media?” david friedberg: “One way to answer that question is to think about how people spend their time.” “Do you spend more minutes on social media, or on traditional media, or playing games? And how is that trending?” “But importantly, which of those will accrue more benefit, and as a result, drive more hours spent from AI?” “One way to think about this thesis is that AI is going to ultimately accrue to video game entertainment far more than social media entertainment or traditional content.” “If you believe in AI, and you believe in the improvements in productivity, generally speaking, people in the industrialized world will generally have more free time on their hands and be able to support themselves with the deflationary effects of AI over time.” “So if there's more time on people's hands, the general market for entertainment is growing, and if the general market for entertainment is growing, gaming is the future of entertainment, and the future of gaming is AI.” “Because I think you can create dynamic, more engaging experiences that will benefit from a back and forth sort of relationship than you can with traditional content or with social media.” “If you're a noob in Fortnite, like you're an early player in Fortnite, you're mostly playing against AI, because what they do is they tune the AI to be easier to beat so that you can slowly develop your skills.” “What was happening early was they were seeing a high degree of churn in Fortnite because kids would go on and play for the first time and they'd get paired up with kids that were better than them, and so they would never win, and they would get frustrated and they would quit the game and stop.” “So the churn rate was high. So AI unlocked higher engagement and higher retention, and I think we're seeing that in a lot of different gaming platforms now.” “So AI can be used, for example, to maximally increase time, engagement, satisfaction, happiness.”
1:43

David Friedberg: “Gaming is the future of entertainment, and the future of gaming is AI.” @jason: “Friedberg, what are your thoughts on the gaming industry versus social media versus traditional media?” david friedberg: “One way to answer that question is to think about how people spend their time.” “Do you spend more minutes on social media, or on traditional media, or playing games? And how is that trending?” “But importantly, which of those will accrue more benefit, and as a result, drive more hours spent from AI?” “One way to think about this thesis is that AI is going to ultimately accrue to video game entertainment far more than social media entertainment or traditional content.” “If you believe in AI, and you believe in the improvements in productivity, generally speaking, people in the industrialized world will generally have more free time on their hands and be able to support themselves with the deflationary effects of AI over time.” “So if there's more time on people's hands, the general market for entertainment is growing, and if the general market for entertainment is growing, gaming is the future of entertainment, and the future of gaming is AI.” “Because I think you can create dynamic, more engaging experiences that will benefit from a back and forth sort of relationship than you can with traditional content or with social media.” “If you're a noob in Fortnite, like you're an early player in Fortnite, you're mostly playing against AI, because what they do is they tune the AI to be easier to beat so that you can slowly develop your skills.” “What was happening early was they were seeing a high degree of churn in Fortnite because kids would go on and play for the first time and they'd get paired up with kids that were better than them, and so they would never win, and they would get frustrated and they would quit the game and stop.” “So the churn rate was high. So AI unlocked higher engagement and higher retention, and I think we're seeing that in a lot of different gaming platforms now.” “So AI can be used, for example, to maximally increase time, engagement, satisfaction, happiness.”

The All-In Podcast

70,365 views • 10 months ago

Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.
3:21

Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.

Milk Road AI

16,317 views • 1 month ago

Every Wall Street giant that owns an AI data center is suddenly looking for a buyer. And NONE of them want to be the last one holding it. Three of them made their move in the last two weeks: Vantage Data Centers is exploring an exit. Its owners, Silver Lake and DigitalBridge, are weighing a listing at around $100 billion, or a sale, or a stake sale. It would be the largest data center IPO ever done. Three days earlier, CyrusOne started the same process. KKR and Global Infrastructure Partners met Goldman Sachs and Morgan Stanley, and the banks pitched for roles on a listing that could come as early as 2027. Last month, Switch hired Goldman and JPMorgan to take it public at close to $80 billion including debt, possibly by the fourth quarter. Three different companies moved inside the same 14 days, and the same handful of investment banks took every call. And these are the exact same firms that BOUGHT these companies off the public market four years ago. Between June 2021 and early 2022, private equity took the data center industry private. Blackstone bought QTS. KKR and Global Infrastructure Partners took CyrusOne private in a deal worth about $15 billion. DigitalBridge and IFM took Switch private for about $11 billion. Together those deals ran past $35 billion. By 2023 there were only two pure-play data center companies left on the public market. The logic at the time was that data centers burn cash for years before they pay, and public shareholders hate that. But private money was patient, and private money could wait. Four years later, the AI boom arrived and every one of those buildings became a gold mine. So follow this: Switch went private at about $11 billion in 2022. Its owners now want close to $80 billion for it. That is roughly 7x, in four years, on the same buildings. And DigitalBridge sits on both sides of this. It owns a piece of Vantage and it took Switch private. It is now looking for the door on BOTH. The question now is who is supposed to buy. There is no bigger private buyer left to sell to. These are already the largest infrastructure funds on Earth, and the price tags now run to $100 billion. The only pocket deep enough is the public market, which means anyone with a brokerage account or an index fund. The people who bought low from the public are now organizing to sell high back to the public. And they are doing it while telling everyone the buildout is just getting started. KKR raised a record $19.2 billion for its newest infrastructure fund this month, and in June launched a separate company with over $10 billion committed to finance more construction. So one hand raises fresh billions to build more data centers, and the other hand sells the finished ones to whoever will take them. None of this proves anyone thinks the boom is ending. Selling into strength is what these firms are paid to do, and every one of these deals is early stage and might never happen. But the timing tells you something: The most sophisticated infrastructure investors alive spent four years accumulating these assets in private, and all decided in the same two weeks that now is the moment to find someone else to own them. Four years ago these firms decided the public market was too impatient to own data centers. Now they want the public market to own them again, at 7x the price. Quite suspicious.
1:52

Every Wall Street giant that owns an AI data center is suddenly looking for a buyer. And NONE of them want to be the last one holding it. Three of them made their move in the last two weeks: Vantage Data Centers is exploring an exit. Its owners, Silver Lake and DigitalBridge, are weighing a listing at around $100 billion, or a sale, or a stake sale. It would be the largest data center IPO ever done. Three days earlier, CyrusOne started the same process. KKR and Global Infrastructure Partners met Goldman Sachs and Morgan Stanley, and the banks pitched for roles on a listing that could come as early as 2027. Last month, Switch hired Goldman and JPMorgan to take it public at close to $80 billion including debt, possibly by the fourth quarter. Three different companies moved inside the same 14 days, and the same handful of investment banks took every call. And these are the exact same firms that BOUGHT these companies off the public market four years ago. Between June 2021 and early 2022, private equity took the data center industry private. Blackstone bought QTS. KKR and Global Infrastructure Partners took CyrusOne private in a deal worth about $15 billion. DigitalBridge and IFM took Switch private for about $11 billion. Together those deals ran past $35 billion. By 2023 there were only two pure-play data center companies left on the public market. The logic at the time was that data centers burn cash for years before they pay, and public shareholders hate that. But private money was patient, and private money could wait. Four years later, the AI boom arrived and every one of those buildings became a gold mine. So follow this: Switch went private at about $11 billion in 2022. Its owners now want close to $80 billion for it. That is roughly 7x, in four years, on the same buildings. And DigitalBridge sits on both sides of this. It owns a piece of Vantage and it took Switch private. It is now looking for the door on BOTH. The question now is who is supposed to buy. There is no bigger private buyer left to sell to. These are already the largest infrastructure funds on Earth, and the price tags now run to $100 billion. The only pocket deep enough is the public market, which means anyone with a brokerage account or an index fund. The people who bought low from the public are now organizing to sell high back to the public. And they are doing it while telling everyone the buildout is just getting started. KKR raised a record $19.2 billion for its newest infrastructure fund this month, and in June launched a separate company with over $10 billion committed to finance more construction. So one hand raises fresh billions to build more data centers, and the other hand sells the finished ones to whoever will take them. None of this proves anyone thinks the boom is ending. Selling into strength is what these firms are paid to do, and every one of these deals is early stage and might never happen. But the timing tells you something: The most sophisticated infrastructure investors alive spent four years accumulating these assets in private, and all decided in the same two weeks that now is the moment to find someone else to own them. Four years ago these firms decided the public market was too impatient to own data centers. Now they want the public market to own them again, at 7x the price. Quite suspicious.

Ricardo

70,858 views • 19 days ago

You've seen the Big Short? THIS IS THE BIG LONG. The market is not trading the world as it is. The market is in La La Land, and it is handing us a massive opportunity. The strait of Hormuz has been shut for months. On Sunday NOT A SINGLE SHIP PASSED THROUGH THE STRAIT. (Kpler) They clearly don't want you in this trade, and to be in this trade is probably a little distasteful: You're betting against the administration, and you're betting that normal people will have to suffer because the government dug a hole and is now unable to get us out. The jawboning and the inventory draws have tricked the market into a state of complacency. The market is asleep at the wheel, and the trade is ours to be had. Check out my latest report with my 13 stock picks. A big chunk of it is FREE.
3:01

You've seen the Big Short? THIS IS THE BIG LONG. The market is not trading the world as it is. The market is in La La Land, and it is handing us a massive opportunity. The strait of Hormuz has been shut for months. On Sunday NOT A SINGLE SHIP PASSED THROUGH THE STRAIT. (Kpler) They clearly don't want you in this trade, and to be in this trade is probably a little distasteful: You're betting against the administration, and you're betting that normal people will have to suffer because the government dug a hole and is now unable to get us out. The jawboning and the inventory draws have tricked the market into a state of complacency. The market is asleep at the wheel, and the trade is ours to be had. Check out my latest report with my 13 stock picks. A big chunk of it is FREE.

Sam Kovacs

98,009 views • 15 days ago

Regens, the time has come. The Redux beta launch is here! Redux is an AI-powered portfolio management vault where a strategy is employed and depositors get their share of the corresponding returns. The beta will consist of whitelisted wallets from friends and family, and early supporters of $RVST. There will be a $100,000 starting cap, with $20,000 maximum cap per whitelisted wallet. This is a massive day for Revest as a company, and we look forward to stress testing this revolutionary product and compiling more information on performance to eventually bring Redux to the general public. In the past, tools like Redux were only available to the wealthy and well connected. Our goal is to open the floodgates and bring advanced products like Redux to each and every person that wishes they had access to AI-powered portfolio management tools. Redux aims to level the playing field. Make sure to stay up to date on everything Redux on our official Twitter and in our Discord!
0:48

Regens, the time has come. The Redux beta launch is here! Redux is an AI-powered portfolio management vault where a strategy is employed and depositors get their share of the corresponding returns. The beta will consist of whitelisted wallets from friends and family, and early supporters of $RVST. There will be a $100,000 starting cap, with $20,000 maximum cap per whitelisted wallet. This is a massive day for Revest as a company, and we look forward to stress testing this revolutionary product and compiling more information on performance to eventually bring Redux to the general public. In the past, tools like Redux were only available to the wealthy and well connected. Our goal is to open the floodgates and bring advanced products like Redux to each and every person that wishes they had access to AI-powered portfolio management tools. Redux aims to level the playing field. Make sure to stay up to date on everything Redux on our official Twitter and in our Discord!

Revest

23,909 views • 2 years ago

"We have a pump and dump contagion that I fully expect is going to continue. A month ago it was just Bitcoin and natural gas. Now it's gold, silver, platinum, palladium, and corn" Mike on why there's no hope for crypto until the stock market breaks "The list is getting longer every day. The bottom line for most of those markets to stay up, most notably Bitcoin, is the stock market has to stay up" "We are talking about the spaces with excess supply. There's a bit of a blip with crude oil in the Middle East that's going to be fixed. Natural gas, cryptos, major oversupply, and they don't have income" "The biggest game in town now is this massive pump in earnings and in the stock market. There is no hope for cryptos in this environment. At some point, they need to find their low price cure"
0:49

"We have a pump and dump contagion that I fully expect is going to continue. A month ago it was just Bitcoin and natural gas. Now it's gold, silver, platinum, palladium, and corn" Mike on why there's no hope for crypto until the stock market breaks "The list is getting longer every day. The bottom line for most of those markets to stay up, most notably Bitcoin, is the stock market has to stay up" "We are talking about the spaces with excess supply. There's a bit of a blip with crude oil in the Middle East that's going to be fixed. Natural gas, cryptos, major oversupply, and they don't have income" "The biggest game in town now is this massive pump in earnings and in the stock market. There is no hope for cryptos in this environment. At some point, they need to find their low price cure"

The Wolf Of All Streets

66,856 views • 2 months ago

$PLTR When ChatGPT went viral, Palantir launched AIP about 4 months after in April of 2023. The entire world had begun to understand the implications of LLMs and Palantir realized they had been working on the foundational software, the ontology, for 20 years. The ontology was necessary to make sense of LLMs, so they immediately decided that AIP would be the future of the company. A tech company lives or dies based on their ability to recogonize the future far before it becomes reality -- as a result, they can build the products and craft the narrative that is needed to succeed in a competitive technology environment. The latest craze has been "vibe-coding," essentially an easier way for engineers to use AI to help them code. Is the trend validated? Well... - Cursor, a vibe-coding startup, was the fastest company in the history of startups to reach $100M ARR and now has a $10B valuation. - $MSFT CEO said 2 days ago that 30% of the company's code is now written by AI. Palantir saw what was happening...and they did what they did best. Took their foundational software and constructed it in a way to now incorporate the concept of "vibe-coding" via the ontology in AIP. A tech company lives or dies based on their ability to recognize the future far before it becomes reality -- as a result, they can build the products and craft the narrative that is needed to succeed in a competitive technology environment. Thanks for taking the time to Chad!
9:07

$PLTR When ChatGPT went viral, Palantir launched AIP about 4 months after in April of 2023. The entire world had begun to understand the implications of LLMs and Palantir realized they had been working on the foundational software, the ontology, for 20 years. The ontology was necessary to make sense of LLMs, so they immediately decided that AIP would be the future of the company. A tech company lives or dies based on their ability to recogonize the future far before it becomes reality -- as a result, they can build the products and craft the narrative that is needed to succeed in a competitive technology environment. The latest craze has been "vibe-coding," essentially an easier way for engineers to use AI to help them code. Is the trend validated? Well... - Cursor, a vibe-coding startup, was the fastest company in the history of startups to reach $100M ARR and now has a $10B valuation. - $MSFT CEO said 2 days ago that 30% of the company's code is now written by AI. Palantir saw what was happening...and they did what they did best. Took their foundational software and constructed it in a way to now incorporate the concept of "vibe-coding" via the ontology in AIP. A tech company lives or dies based on their ability to recognize the future far before it becomes reality -- as a result, they can build the products and craft the narrative that is needed to succeed in a competitive technology environment. Thanks for taking the time to Chad!

amit

84,653 views • 1 year ago

Yesterday I was accused of being a Chinese propagandist for 2 posts warning of the dangers of Trump’s illiterate foreign policy gross negligence. It is absolutely critical that Americans wake up to the real threat presented by his presidential incompetence. It is no different to his incompetent handling of the COVID crisis, which caused an estimated 200,000 unnecessary deaths in the U.S. The reality is, Trump is the price the rich have had to pay to have a useful idiot in the White House. But in my view, it is a potential huge miscalculation. China will soon become the biggest economy in the world that’s inevitable. Their rise to power is a result of a self inflicted wound delivered by western powers who sought to inflate their bottom line by shipping manufacturing to China. They assumed China was simply a pool of cheap labor in a country that was happy to be exploited. BIG MISTAKE. China played dumb for decades, all the while educating their population, building infrastructure and embracing technology. They were regarded by the world as imitators and China was happy with that label, all the while learning from their perceived slave owners. They played the west and achieved this by exploiting their arrogance and complacency. By the time the U.S. was awake to the monster THEY had created, it was too late. China now has a highly educated population, world class infrastructure, a decade ahead of the U.S. They no longer need to imitate because they are now innovators. Their global dominance of the EV market has left the U.S. standing. They are possibly the only country in the world that could be self sufficient TODAY. It is not good fortune that China has a $1 trillion global trade surplus. They own almost a trillion dollars of U.S. debt. That’s before we talk about real estate holdings and the fact that a Chinese AI startup that wiped almost a trillion dollars off tech stock values. It is slowly selling off US debt and reinvesting in Africa and the Belt and Road initiative. They are a big picture economy 6 moves ahead in the game of geopolitical chess. I suspect they are close to making a move on Taiwan and if that happens, military experts are of the growing opinion there would be nothing the U.S. military could do to stop them. If that happens, they will inherit a 68% global market share of the semiconductor industry. What do you think that will do for their advancement of AI technology? China is playing Trump like a fiddle just waiting to use the BRICS alliance to replace the U.S. dollar as the global reserve currency to deliver the final killer blow. America needs to wake up to the threat Wun Dum Fuc represents to the U.S. way of life.
1:04

Yesterday I was accused of being a Chinese propagandist for 2 posts warning of the dangers of Trump’s illiterate foreign policy gross negligence. It is absolutely critical that Americans wake up to the real threat presented by his presidential incompetence. It is no different to his incompetent handling of the COVID crisis, which caused an estimated 200,000 unnecessary deaths in the U.S. The reality is, Trump is the price the rich have had to pay to have a useful idiot in the White House. But in my view, it is a potential huge miscalculation. China will soon become the biggest economy in the world that’s inevitable. Their rise to power is a result of a self inflicted wound delivered by western powers who sought to inflate their bottom line by shipping manufacturing to China. They assumed China was simply a pool of cheap labor in a country that was happy to be exploited. BIG MISTAKE. China played dumb for decades, all the while educating their population, building infrastructure and embracing technology. They were regarded by the world as imitators and China was happy with that label, all the while learning from their perceived slave owners. They played the west and achieved this by exploiting their arrogance and complacency. By the time the U.S. was awake to the monster THEY had created, it was too late. China now has a highly educated population, world class infrastructure, a decade ahead of the U.S. They no longer need to imitate because they are now innovators. Their global dominance of the EV market has left the U.S. standing. They are possibly the only country in the world that could be self sufficient TODAY. It is not good fortune that China has a $1 trillion global trade surplus. They own almost a trillion dollars of U.S. debt. That’s before we talk about real estate holdings and the fact that a Chinese AI startup that wiped almost a trillion dollars off tech stock values. It is slowly selling off US debt and reinvesting in Africa and the Belt and Road initiative. They are a big picture economy 6 moves ahead in the game of geopolitical chess. I suspect they are close to making a move on Taiwan and if that happens, military experts are of the growing opinion there would be nothing the U.S. military could do to stop them. If that happens, they will inherit a 68% global market share of the semiconductor industry. What do you think that will do for their advancement of AI technology? China is playing Trump like a fiddle just waiting to use the BRICS alliance to replace the U.S. dollar as the global reserve currency to deliver the final killer blow. America needs to wake up to the threat Wun Dum Fuc represents to the U.S. way of life.

𝔗𝔯𝔲𝔱𝔥 𝔐𝔞𝔱𝔱𝔢𝔯𝔰

48,842 views • 1 year ago

Nebius will be a trillion dollar company (Save this). The neocloud market, purpose-built AI cloud infrastructure, separate from legacy hyperscalers generated roughly $25 billion in revenue in 2025, up 223% year over year. Synergy Research projects it will approach $400 billion by 2031, compounding at 58% annually one of the fastest sustained growth rates ever recorded for an infrastructure category of this scale. The CEO's explanation for why they win is worth understanding in detail. GPU compute is scarce and that part everyone knows but Nebius is not simply renting GPUs by the hour and marking them up, which is what most neocloud imitators do. They have built their own physical capacity for inference, optimized the full technology stack from the software layer all the way down to the rack hardware and recently acquired a company called Agen specifically to push inference latency even lower and throughput even higher. The CEO frames the core problem directly that in 2026, every product you build is powered by tokens, AI intelligence and while you can get those tokens from OpenAI or Anthropic via a simple API call, the moment you want to run open source models, specialized vertical models, or anything other than the two dominant frontier labs, you run into a wall. You can download the weights from Hugging Face and assemble the pieces. But getting those workloads to run at scale, at the economics you need, with the reliability your product requires, is an extraordinarily complex engineering challenge that most companies cannot staff or afford to solve in-house. That is the problem Nebius is solving, and that is why their inference product called Token Factory exists. The financial results are among the most dramatic growth numbers reported by any public company this year. In Q1 2026, Nebius posted $399 million in revenue, a 684% increase from the same quarter a year earlier. In the span of twelve months, the company swung from a $104 million net loss to $621 million in net income. Cash from operations went from negative $184 million to positive $2.26 billion in the same period meaning this is not growth funded by burning investor capital, it is growth that is now generating its own fuel. For the full year 2026, Nebius is guiding for an annualized revenue run rate of $7 billion to $9 billion, with pipeline creation tracking to surpass $4 billion. The contracted backlog sits at $49 billion, anchored by a $27 billion agreement with Meta, a deal worth up to $19.4 billion with Microsoft, and a public endorsement from Jensen Huang at NVIDIA's GTC conference in 2026. The current market cap is approximately $56 billion. A company with $7 to $9 billion in annualized revenue, growing at 684%, turning cash-flow positive, sitting on $49 billion in contracted backlog, operating in a market compounding at 58% annually toward $400 billion, that company has a credible path to 20x from its current valuation if execution holds. That is the trillion dollar case, and it does not require any heroic assumptions and it requires Nebius to keep doing what it is already demonstrably doing. Milk Road Pro called this one early. Our analysts added Nebius to the portfolio when it was still flying under the radar, and we are sitting on a massive gain on that position right now. If you want to see what else we are building conviction on before the rest of the market catches up, come join us at Milk Road Pro using the link below!
1:04

Nebius will be a trillion dollar company (Save this). The neocloud market, purpose-built AI cloud infrastructure, separate from legacy hyperscalers generated roughly $25 billion in revenue in 2025, up 223% year over year. Synergy Research projects it will approach $400 billion by 2031, compounding at 58% annually one of the fastest sustained growth rates ever recorded for an infrastructure category of this scale. The CEO's explanation for why they win is worth understanding in detail. GPU compute is scarce and that part everyone knows but Nebius is not simply renting GPUs by the hour and marking them up, which is what most neocloud imitators do. They have built their own physical capacity for inference, optimized the full technology stack from the software layer all the way down to the rack hardware and recently acquired a company called Agen specifically to push inference latency even lower and throughput even higher. The CEO frames the core problem directly that in 2026, every product you build is powered by tokens, AI intelligence and while you can get those tokens from OpenAI or Anthropic via a simple API call, the moment you want to run open source models, specialized vertical models, or anything other than the two dominant frontier labs, you run into a wall. You can download the weights from Hugging Face and assemble the pieces. But getting those workloads to run at scale, at the economics you need, with the reliability your product requires, is an extraordinarily complex engineering challenge that most companies cannot staff or afford to solve in-house. That is the problem Nebius is solving, and that is why their inference product called Token Factory exists. The financial results are among the most dramatic growth numbers reported by any public company this year. In Q1 2026, Nebius posted $399 million in revenue, a 684% increase from the same quarter a year earlier. In the span of twelve months, the company swung from a $104 million net loss to $621 million in net income. Cash from operations went from negative $184 million to positive $2.26 billion in the same period meaning this is not growth funded by burning investor capital, it is growth that is now generating its own fuel. For the full year 2026, Nebius is guiding for an annualized revenue run rate of $7 billion to $9 billion, with pipeline creation tracking to surpass $4 billion. The contracted backlog sits at $49 billion, anchored by a $27 billion agreement with Meta, a deal worth up to $19.4 billion with Microsoft, and a public endorsement from Jensen Huang at NVIDIA's GTC conference in 2026. The current market cap is approximately $56 billion. A company with $7 to $9 billion in annualized revenue, growing at 684%, turning cash-flow positive, sitting on $49 billion in contracted backlog, operating in a market compounding at 58% annually toward $400 billion, that company has a credible path to 20x from its current valuation if execution holds. That is the trillion dollar case, and it does not require any heroic assumptions and it requires Nebius to keep doing what it is already demonstrably doing. Milk Road Pro called this one early. Our analysts added Nebius to the portfolio when it was still flying under the radar, and we are sitting on a massive gain on that position right now. If you want to see what else we are building conviction on before the rest of the market catches up, come join us at Milk Road Pro using the link below!

Milk Road AI

28,622 views • 3 months ago

Chamath: “Private equity in general is totally hosed.” 🏢🚨 “I think the history of this is important.” “There was a long standing belief that the best way to generate the best risk adjusted return was to have what's called a 60/40 allocation. 60% to bonds and 40% to equities.” “Over many years, especially when we artificially suppressed rates at zero, a lot of people started to move their allocations away from 60/40 and they started to make more and more investments further out on the risk curve.” “The biggest beneficiaries of that were venture capital, private equity, and hedge funds.” “The thing with private equity is that because rates were zero, they had an infinite amount of borrowing capacity at very little downside to them, and so they were able to manufacture returns much faster than venture capital and hedge funds could.” “So as a result, you had an initial group of people that were defining the asset class, making a ton of money, and then you had all these fast followers that said, ‘Well, if they're doing it, I can do it too.’” “But then always what happens is then you have this flood of laggards that just flood the zone.” “And it's these laggards that make it very difficult to generate returns because they start overpaying for assets, they start mismanaging and under managing the assets that they do own.” “That created a lot of competition, and so that's why you see this hockey stick graph.” “And when you see that kind of graph, it doesn't matter what asset class it is. The returns go to zero.” “And so we've seen this in venture capital. We've seen this in hedge funds. And we're now going to see this in private equity.”
1:36

Chamath: “Private equity in general is totally hosed.” 🏢🚨 “I think the history of this is important.” “There was a long standing belief that the best way to generate the best risk adjusted return was to have what's called a 60/40 allocation. 60% to bonds and 40% to equities.” “Over many years, especially when we artificially suppressed rates at zero, a lot of people started to move their allocations away from 60/40 and they started to make more and more investments further out on the risk curve.” “The biggest beneficiaries of that were venture capital, private equity, and hedge funds.” “The thing with private equity is that because rates were zero, they had an infinite amount of borrowing capacity at very little downside to them, and so they were able to manufacture returns much faster than venture capital and hedge funds could.” “So as a result, you had an initial group of people that were defining the asset class, making a ton of money, and then you had all these fast followers that said, ‘Well, if they're doing it, I can do it too.’” “But then always what happens is then you have this flood of laggards that just flood the zone.” “And it's these laggards that make it very difficult to generate returns because they start overpaying for assets, they start mismanaging and under managing the assets that they do own.” “That created a lot of competition, and so that's why you see this hockey stick graph.” “And when you see that kind of graph, it doesn't matter what asset class it is. The returns go to zero.” “And so we've seen this in venture capital. We've seen this in hedge funds. And we're now going to see this in private equity.”

The All-In Podcast

800,205 views • 11 months ago

Chamath just delivered the clearest diagnosis of what is happening to enterprise software and the OpenAI Deployment Company is the most damning piece of evidence he could have picked. "The low end of the market is basically finished. There is no safe space." 90% of public SaaS stocks are down 30-80% from their 52 week highs, the median software stock is now negative over the last 3-6 months. Goldman Sachs reported that software forward P/E multiples fell from 35x to 20x, the lowest absolute level since 2014 and the smallest premium to the S&P 500 since 2010. The low end died first and fastest, because AI replaced it most directly. The small business tools, the lightweight project managers, the single function SaaS products that charged $49 a month per seat, those are being replaced by AI agents that do the same work as a workflow, not a product. You do not buy an AI powered tool, you describe what you need and it builds it and the seat based model that created the SaaS industry simply does not apply to that transaction. But Chamath's more interesting argument is about the high end and the tell he points to is perfect. OpenAI just raised $4 billion from 19 investors including TPG, Brookfield, Bain, and McKinsey to launch a consulting company and guaranteed those investors a 17.5% annual return to do it. On $4 billion in committed capital, that is roughly $700 million per year in guaranteed payouts, owed by a company that is projected to lose $14 billion in 2026. The goal of this venture is to compete directly with Deloitte, PwC, Ernst & Young, Andersen, and Cognizant. Think about what that structure reveals. OpenAI lost half of its enterprise LLM API market share from 50% to 25% between late 2023 and mid-2025, with Anthropic now leading at 32%. Its response was not to build a better model but rather to raise $4 billion, offer guaranteed PE-tier returns and hire embedded engineers to physically sit inside client organizations and make AI actually work in production. The reason, as Chamath identified, is that the high end of the market is not easy. "It's not like boop boop boop, put in a prompt and beep bap boop, it all works," he said and the data confirms exactly that. 88% of organizations running AI agents reported a security incident in the past year, 42% of C-suite executives say AI adoption is creating internal organizational conflict. The average enterprise AI consulting implementation costs $228,000 in year one versus $77,000 for platform-based approaches and most still stall before reaching production. Anthropic immediately matched OpenAI with a competing $1.5 billion consulting venture backed by Blackstone, Goldman Sachs, and Hellman & Friedman bringing the combined spend by the two leading AI labs on human powered enterprise deployment to $5.5 billion in a single month Chamath's read is that the high end, the large enterprise platforms like Salesforce with proprietary data flywheels, Palantir with its FDE model already proven at scale, Oracle with vertical specific data moats will survive and consolidate. The mid-market point solutions, the single function tools, the lightweight enterprise apps without defensible data assets, those are on the conveyor belt. The AI industry is not just disrupting the companies that use software but rather disrupting the companies that sell it.
1:05

Chamath just delivered the clearest diagnosis of what is happening to enterprise software and the OpenAI Deployment Company is the most damning piece of evidence he could have picked. "The low end of the market is basically finished. There is no safe space." 90% of public SaaS stocks are down 30-80% from their 52 week highs, the median software stock is now negative over the last 3-6 months. Goldman Sachs reported that software forward P/E multiples fell from 35x to 20x, the lowest absolute level since 2014 and the smallest premium to the S&P 500 since 2010. The low end died first and fastest, because AI replaced it most directly. The small business tools, the lightweight project managers, the single function SaaS products that charged $49 a month per seat, those are being replaced by AI agents that do the same work as a workflow, not a product. You do not buy an AI powered tool, you describe what you need and it builds it and the seat based model that created the SaaS industry simply does not apply to that transaction. But Chamath's more interesting argument is about the high end and the tell he points to is perfect. OpenAI just raised $4 billion from 19 investors including TPG, Brookfield, Bain, and McKinsey to launch a consulting company and guaranteed those investors a 17.5% annual return to do it. On $4 billion in committed capital, that is roughly $700 million per year in guaranteed payouts, owed by a company that is projected to lose $14 billion in 2026. The goal of this venture is to compete directly with Deloitte, PwC, Ernst & Young, Andersen, and Cognizant. Think about what that structure reveals. OpenAI lost half of its enterprise LLM API market share from 50% to 25% between late 2023 and mid-2025, with Anthropic now leading at 32%. Its response was not to build a better model but rather to raise $4 billion, offer guaranteed PE-tier returns and hire embedded engineers to physically sit inside client organizations and make AI actually work in production. The reason, as Chamath identified, is that the high end of the market is not easy. "It's not like boop boop boop, put in a prompt and beep bap boop, it all works," he said and the data confirms exactly that. 88% of organizations running AI agents reported a security incident in the past year, 42% of C-suite executives say AI adoption is creating internal organizational conflict. The average enterprise AI consulting implementation costs $228,000 in year one versus $77,000 for platform-based approaches and most still stall before reaching production. Anthropic immediately matched OpenAI with a competing $1.5 billion consulting venture backed by Blackstone, Goldman Sachs, and Hellman & Friedman bringing the combined spend by the two leading AI labs on human powered enterprise deployment to $5.5 billion in a single month Chamath's read is that the high end, the large enterprise platforms like Salesforce with proprietary data flywheels, Palantir with its FDE model already proven at scale, Oracle with vertical specific data moats will survive and consolidate. The mid-market point solutions, the single function tools, the lightweight enterprise apps without defensible data assets, those are on the conveyor belt. The AI industry is not just disrupting the companies that use software but rather disrupting the companies that sell it.

Milk Road AI

1,660,134 views • 3 months ago

IFPI has released an interview with its Deputy Director of Global Markets, Tom Bendall, explaining how their data is collected as of March 20, 2026, this confirms that JENNIE’s Ruby sales were not included in this year’s report due to the label not submitting the data. “(…) So if I give an example, if the reporting companies submitted revenues of 70 million, and they contributed 70% of consumption in a market for a reporting format, then we'd extrapolate up to get a total market size of 100 million. What this means is that ultimately we have accurate and consistent reporting across every single market globally.” In example: they collect data from companies/labels that actually submit it, then they use that submitted data to estimate the full market Key point: if something isn’t submitted at all, it can’t be counted in the first place. it took them long but they’ve finally addressed the matter. Source of everything in the post: IFPI
0:22

IFPI has released an interview with its Deputy Director of Global Markets, Tom Bendall, explaining how their data is collected as of March 20, 2026, this confirms that JENNIE’s Ruby sales were not included in this year’s report due to the label not submitting the data. “(…) So if I give an example, if the reporting companies submitted revenues of 70 million, and they contributed 70% of consumption in a market for a reporting format, then we'd extrapolate up to get a total market size of 100 million. What this means is that ultimately we have accurate and consistent reporting across every single market globally.” In example: they collect data from companies/labels that actually submit it, then they use that submitted data to estimate the full market Key point: if something isn’t submitted at all, it can’t be counted in the first place. it took them long but they’ve finally addressed the matter. Source of everything in the post: IFPI

Cia.

97,879 views • 5 months ago

S&P just cut Oracle to one notch above junk, and the stock went UP anyway. Think about that for a second... Back in December I told you the AI arms race would keep rewarding capex right up until the moment it didn't, and I pointed straight at Oracle. The stock is now down more than 55% from its high and this week S&P downgraded its credit to the lowest rung of investment grade, which means one more cut and Oracle wears a junk rating for the first time in its history. The downgrade landed because the cash bleed is getting MUCH worse. S&P now sees Oracle burning close to $42 billion in free cash flow next year, nearly double its earlier estimate, with capex rocketing toward $90 billion and a single customer (OpenAI) sitting behind roughly half of that $638 billion backlog. The bond market looked at all of that and reached for insurance. The stock market looked at the exact same company and bid it higher. When those two disagree like this, 45 years in this business has taught me to side with the bondholders every single time. They get paid before shareholders do, so they tend to see the trouble first. And Oracle is now funding this buildout with equity instead of debt, with another $20 billion in stock issuance slated for this year. A company confident in its own cash flows borrows against them. A company bracing for a downgrade dilutes its shareholders instead. Oracle showed you which one it is. If you want to know how to actually make money in a market this dominated by Big Tech narratives, that is what July 22nd is for. 14 elite investors are sharing the specific longs and shorts they are backing with their own capital - for just $99. We entered the golden era of stock picking. Grab your ticket today:
0:48

S&P just cut Oracle to one notch above junk, and the stock went UP anyway. Think about that for a second... Back in December I told you the AI arms race would keep rewarding capex right up until the moment it didn't, and I pointed straight at Oracle. The stock is now down more than 55% from its high and this week S&P downgraded its credit to the lowest rung of investment grade, which means one more cut and Oracle wears a junk rating for the first time in its history. The downgrade landed because the cash bleed is getting MUCH worse. S&P now sees Oracle burning close to $42 billion in free cash flow next year, nearly double its earlier estimate, with capex rocketing toward $90 billion and a single customer (OpenAI) sitting behind roughly half of that $638 billion backlog. The bond market looked at all of that and reached for insurance. The stock market looked at the exact same company and bid it higher. When those two disagree like this, 45 years in this business has taught me to side with the bondholders every single time. They get paid before shareholders do, so they tend to see the trouble first. And Oracle is now funding this buildout with equity instead of debt, with another $20 billion in stock issuance slated for this year. A company confident in its own cash flows borrows against them. A company bracing for a downgrade dilutes its shareholders instead. Oracle showed you which one it is. If you want to know how to actually make money in a market this dominated by Big Tech narratives, that is what July 22nd is for. 14 elite investors are sharing the specific longs and shorts they are backing with their own capital - for just $99. We entered the golden era of stock picking. Grab your ticket today:

George Noble

18,966 views • 1 month ago

California's Wealth Tax Has a Secret Prohibition On Founder Control That's Forcing Entrepreneurs Out Sacks: “Larry and Sergey have left the state, and they’re probably getting dragged for that, but one of the reasons they have to is because of the super-voting stock provision in this thing.” “The way they calculate the value of your stock isn’t based on its liquid market value, but if you own super-voting shares, they multiply your ownership, your super voting, by the market cap of the company, and they deem your shares be worth that.” How it works: – Larry and Sergey have a combined 52% voting share of Google, which is worth $4T – They would be deemed to be worth roughly $1T each, not the ~$200B they are actually worth – So a 5% tax for them is more like a 25% tax on their entire net worth Chamath: “It actually becomes closer to 50%, because you'd have to sell more than that to overcome the drag of selling to generate $25B or $50B of net worth.” “You have to sell two times that, because you have to pay taxes on that.” Sacks: “So what’s the point of having that super-voting provision in there?” “It’s just totally punitive and vicious.”
1:04

California's Wealth Tax Has a Secret Prohibition On Founder Control That's Forcing Entrepreneurs Out Sacks: “Larry and Sergey have left the state, and they’re probably getting dragged for that, but one of the reasons they have to is because of the super-voting stock provision in this thing.” “The way they calculate the value of your stock isn’t based on its liquid market value, but if you own super-voting shares, they multiply your ownership, your super voting, by the market cap of the company, and they deem your shares be worth that.” How it works: – Larry and Sergey have a combined 52% voting share of Google, which is worth $4T – They would be deemed to be worth roughly $1T each, not the ~$200B they are actually worth – So a 5% tax for them is more like a 25% tax on their entire net worth Chamath: “It actually becomes closer to 50%, because you'd have to sell more than that to overcome the drag of selling to generate $25B or $50B of net worth.” “You have to sell two times that, because you have to pay taxes on that.” Sacks: “So what’s the point of having that super-voting provision in there?” “It’s just totally punitive and vicious.”

The All-In Podcast

155,130 views • 7 months ago