Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Ackman’s core thesis for 2026 is exploiting market distraction. While capital rotates heavily into the 'AI race,' semiconductor manufacturers, and new IPOs like $SPCX, Pershing Square is taking concentrated, heavy positions in what the market currently dismisses as 'old tech'. Ackman argued the market is ignoring $MSFT and $AMZN's...

CapexAndChill's profile picture

CapexAndChill

11,341 subscribers

66,285 Aufrufe • vor 1 Monat •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

In Ackman's latest interview he divides the AI market, stating that it is not a bubble at the foundational layer, but highly risky at the software/model layer. He sees "near infinite demand" for compute. He believes infrastructure providers such as Microsoft, Amazon, Meta, and even SpaceX will earn massive returns on their data center investments. He is highly skeptical of pure model layer businesses like OpenAI and Anthropic. He notes they face massive capital burn rates and an existential threat from open-source models such as Chinese model and SpaceX that are driving the cost of AI access effectively to zero. A business model reliant on charging for proprietary models will collapse if open-source alternatives are essentially free. $AMZN $META $MSFT $SPCX
3:09

In Ackman's latest interview he divides the AI market, stating that it is not a bubble at the foundational layer, but highly risky at the software/model layer. He sees "near infinite demand" for compute. He believes infrastructure providers such as Microsoft, Amazon, Meta, and even SpaceX will earn massive returns on their data center investments. He is highly skeptical of pure model layer businesses like OpenAI and Anthropic. He notes they face massive capital burn rates and an existential threat from open-source models such as Chinese model and SpaceX that are driving the cost of AI access effectively to zero. A business model reliant on charging for proprietary models will collapse if open-source alternatives are essentially free. $AMZN $META $MSFT $SPCX

CapexAndChill

137,444 Aufrufe • vor 1 Monat

$META is the largest position in my family portfolio because I don't just view it as just one of the world's most profitable advertising businesses but as an AI infrastructure and distribution platform with unmatched scale. As AI improves ad monetization, custom silicon lowers compute costs and new revenue streams emerge through models, agents and cloud infrastructure then I think the market will increasingly value Meta as an AI platform rather than simply an ad company. If that plays out then I think $META can rerate closer to 25x earnings.
1:21

$META is the largest position in my family portfolio because I don't just view it as just one of the world's most profitable advertising businesses but as an AI infrastructure and distribution platform with unmatched scale. As AI improves ad monetization, custom silicon lowers compute costs and new revenue streams emerge through models, agents and cloud infrastructure then I think the market will increasingly value Meta as an AI platform rather than simply an ad company. If that plays out then I think $META can rerate closer to 25x earnings.

Shay Boloor

120,085 Aufrufe • vor 1 Monat

When asked about the ratio of successful bets to failures, Zuckerberg dismissed the concept of a "batting average" in tech creation. He argued that in creation, "the upside is almost infinite," meaning a single product reaching billions of users financially eclipses numerous failed, expensive hardware experiments. He said that Meta's company culture is "not very afraid of failure" and can "tolerate being ridiculed for long periods of time". Zuckerberg views expensive experimentation not as sunk costs or failures, but as the necessary "price of learning" in a market where you must build things physically to see if they work. For a long-term strategist, this means Meta's R&D budget will remain highly aggressive, and they are structurally insulated against short-term Wall Street pressure to pull back on currently unprofitable bets. $META
3:13

When asked about the ratio of successful bets to failures, Zuckerberg dismissed the concept of a "batting average" in tech creation. He argued that in creation, "the upside is almost infinite," meaning a single product reaching billions of users financially eclipses numerous failed, expensive hardware experiments. He said that Meta's company culture is "not very afraid of failure" and can "tolerate being ridiculed for long periods of time". Zuckerberg views expensive experimentation not as sunk costs or failures, but as the necessary "price of learning" in a market where you must build things physically to see if they work. For a long-term strategist, this means Meta's R&D budget will remain highly aggressive, and they are structurally insulated against short-term Wall Street pressure to pull back on currently unprofitable bets. $META

CapexAndChill

126,419 Aufrufe • vor 1 Monat

Bill Nygren of Oakmark fund noted that the S&P 500 used to be a diversified hedge against general living expenses, but has devolved into a concentrated bet on tech. He also believes as the market is hyper-fixated on AI hardware that it is mispricing software and financials.
1:20

Bill Nygren of Oakmark fund noted that the S&P 500 used to be a diversified hedge against general living expenses, but has devolved into a concentrated bet on tech. He also believes as the market is hyper-fixated on AI hardware that it is mispricing software and financials.

CapexAndChill

53,769 Aufrufe • vor 27 Tagen

.Bill Ackman discussed why he believed $GOOGL (Alphabet) was undervalued and overlooked in his February 2024 interview with Lex Fridman. During the conversation, Ackman explained that concerns over Google's competitive position in AI caused the market to overreact, pushing down the stock price and creating a compelling buying opportunity. He argued that Google's AI capabilities were possibly stronger than what the market perceived, and the stock was trading at a valuation not seen in over a decade, making it a rare opportunity among the largest tech companies. $GOOGL up over 130% since this interview and nearing a $4T market cap taking some market share away from $NVDA as $META looking to buy their TPUs
5:41

.Bill Ackman discussed why he believed $GOOGL (Alphabet) was undervalued and overlooked in his February 2024 interview with Lex Fridman. During the conversation, Ackman explained that concerns over Google's competitive position in AI caused the market to overreact, pushing down the stock price and creating a compelling buying opportunity. He argued that Google's AI capabilities were possibly stronger than what the market perceived, and the stock was trading at a valuation not seen in over a decade, making it a rare opportunity among the largest tech companies. $GOOGL up over 130% since this interview and nearing a $4T market cap taking some market share away from $NVDA as $META looking to buy their TPUs

Ted Zhang

185,116 Aufrufe • vor 8 Monaten

🇺🇸🇮🇷 AI mania is completely masking the Iran war’s damage. Since the fighting started, the world’s biggest companies have gained $5.5T in market value… but it’s almost all AI. Semiconductor stocks (Nvidia + Broadcom) alone added $4.3T. Big Tech is rebounding hard because investors see it as “insulated” from the chaos. Meanwhile, airlines, supply chains, consumer spending, and old-school industries are getting wrecked by higher energy prices and disruption. The AI rally is so massive it’s making the stock market look great while the real economy quietly takes a beating. That's some high-tech smoke and mirrors if I'd ever seen one. Source: Financial Times (YT)
1:18

🇺🇸🇮🇷 AI mania is completely masking the Iran war’s damage. Since the fighting started, the world’s biggest companies have gained $5.5T in market value… but it’s almost all AI. Semiconductor stocks (Nvidia + Broadcom) alone added $4.3T. Big Tech is rebounding hard because investors see it as “insulated” from the chaos. Meanwhile, airlines, supply chains, consumer spending, and old-school industries are getting wrecked by higher energy prices and disruption. The AI rally is so massive it’s making the stock market look great while the real economy quietly takes a beating. That's some high-tech smoke and mirrors if I'd ever seen one. Source: Financial Times (YT)

Mario Nawfal

27,363 Aufrufe • vor 2 Monaten

$MSFT is trading at a decade-low valuation because the market is focused on the near-term cost of AI while underpricing Microsoft’s role as one of the most important enterprise AI platforms in the world. The bigger shift is that Microsoft may move from only monetizing users to monetizing work where companies eventually pay not just for employee licenses but for the AI work being performed around each employee. That creates a ton of upside if Microsoft can monetize agents, tokens, workflows, security, governance and productivity across the enterprise stack in the new AI economy.
0:50

$MSFT is trading at a decade-low valuation because the market is focused on the near-term cost of AI while underpricing Microsoft’s role as one of the most important enterprise AI platforms in the world. The bigger shift is that Microsoft may move from only monetizing users to monetizing work where companies eventually pay not just for employee licenses but for the AI work being performed around each employee. That creates a ton of upside if Microsoft can monetize agents, tokens, workflows, security, governance and productivity across the enterprise stack in the new AI economy.

Shay Boloor

606,243 Aufrufe • vor 1 Monat

$ORCL is borrowing aggressively to build AI data centers for a customer base heavily tied to OpenAI and the market is starting to question whether Oracle can fund that buildout without $MSFT, $GOOGL, $AMZN or $META level cash generation. The upside is that OCI now has one of the largest AI backlogs in the market at ~$640B which gives Oracle a real shot at becoming the fourth major AI cloud platform if that demand converts into durable revenue. If cloud growth stays above 50% and customer prepayments ease the funding burden then this selloff could eventually look like the painful capital cycle before the harvest phase.
0:56

$ORCL is borrowing aggressively to build AI data centers for a customer base heavily tied to OpenAI and the market is starting to question whether Oracle can fund that buildout without $MSFT, $GOOGL, $AMZN or $META level cash generation. The upside is that OCI now has one of the largest AI backlogs in the market at ~$640B which gives Oracle a real shot at becoming the fourth major AI cloud platform if that demand converts into durable revenue. If cloud growth stays above 50% and customer prepayments ease the funding burden then this selloff could eventually look like the painful capital cycle before the harvest phase.

Shay Boloor

132,000 Aufrufe • vor 1 Monat

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.
1:12

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.

Mike

182,273 Aufrufe • vor 8 Monaten

Indian IT is the only sector on earth being sold off for AI working, not for AI failing. The market treats Nvidia as proof AI works and Indian IT as proof it does not. It may be exactly backwards. - Nifty IT: down ~39% from its peak, now near 16x forward earnings. - TCS is cutting 12,000 jobs. Entry-level tech hiring fell 44% in a year. Nikhil Kamath laid out their bull math: disrupt 10% of the world's $50 trillion services economy, and that $5 trillion justifies the spend. The catch he left open: disrupting $5 trillion of services is not the same as capturing it. Indian IT is where that gap already shows. The disruption is landing as falling revenue, not a new profit pool. Better at delivery, worse at billing. The same labs said to be killing it, OpenAI and Anthropic, just partnered with TCS and Infosys to sell through them. One of these AI bets is mispriced. Maybe both. The canary is not in a Santa Clara data centre. It is on the NSE. | Credits: YouTube, WTF Podcast |
0:41

Indian IT is the only sector on earth being sold off for AI working, not for AI failing. The market treats Nvidia as proof AI works and Indian IT as proof it does not. It may be exactly backwards. - Nifty IT: down ~39% from its peak, now near 16x forward earnings. - TCS is cutting 12,000 jobs. Entry-level tech hiring fell 44% in a year. Nikhil Kamath laid out their bull math: disrupt 10% of the world's $50 trillion services economy, and that $5 trillion justifies the spend. The catch he left open: disrupting $5 trillion of services is not the same as capturing it. Indian IT is where that gap already shows. The disruption is landing as falling revenue, not a new profit pool. Better at delivery, worse at billing. The same labs said to be killing it, OpenAI and Anthropic, just partnered with TCS and Infosys to sell through them. One of these AI bets is mispriced. Maybe both. The canary is not in a Santa Clara data centre. It is on the NSE. | Credits: YouTube, WTF Podcast |

Vishnu Agarwal

97,220 Aufrufe • vor 1 Monat

Cloud capital versus AI - What DeepSeek’s spectacular success means for technofeudalism & the New Cold War DeepSeek, a Chinese artificial intelligence (AI) company, this week changed the global AI landscape, not to mention caused $1 trillion losses in the New York stock exchange and the NASDAC. In the process, it demonstrated the difference between cloud capital, which drives technofeudalism onward and upward, and AI-services, which were always a bubble waiting to burst. What remains to be seen is DeepSeek's impact on the New Cold War between the US and China which, from its beginning, was motivated by the clash between US and Chinese cloud capital. DeepSeek is China's response to OpenAI's ChatGPT. Its models perform as efficiently as their US counterparts. The difference is that DeepSeek is offered for free, making money only by selling services to developers - not to the public – at a fraction of the price OpenAI charges! The gist of DeepSeek’s arrival on the AI scene is a sudden transition from proprietary to open source technology. It is, therefore, no great wonder that, the moment DeekSeek became the most downloaded app on the Apple Store, it pulverised the market capitalisation of the, hitherto overinflated, US Big Tech companies. But how did this happen? How is a private commodity suddenly being offered for free? And does this mean that technofeudalism is in trouble? To begin with it is important to note AI was never a proprietary technology in itself. The underlying code has always been open source. What made AI quasi-private was the way these models were trained using huge amounts of privatised (that is stolen from us) data. A leaked Google memo in 2017, that was widely discussed in the industry at the time, but also widely refuted, explained: "If an open source LLM trained for a few million dollars outperforms the effectiveness of proprietary models... There will be no firewall to safeguard OpenAI either." DeepSeek pierced the US AI companies’ bubble by decommodifying the results of the model’s training, shifting them from behind a paywall to the public arena. Within days, developers around the world started building their own models on top of DeepSeek's. This is was the nightmare for US Big Tech's AI service providers who offered the results of prompts as a commodity, in the form of subscriptions. DeepSeek-type applications can now produce high-quality translations for free and, in so doing, undermine companies specialising in, for example, translation services, such as Germany's Deepl. In the broader scheme of things, this means that the morsels of cloud capital that Europe owns has lost its market value. Nevertheless, and this is a huge nevertheless, it is only AI-as-a-commodity that has lost its (grossly exaggerated) value. In sharp contrast, cloud capital utilised not as a commodity producing piece of tech but as produced means of behavioural modification is not at all threatened by companies like DeepSeek. And since technofeudalism is powered by cloud capital working that way, rather than commodity-like AI services of the ChaptGPT type, our technofeudal order is not threatened by competitors such as DeepSeek. To help understand the difference between cloud capital and AI-based commodified services it helps to compare and contrast Alexa and ChatGPT. Alexa is not offering you a commodified service. It is your free pretend-slave. Unlike ChatGPT you do not pay a subscription to Amazon for the right to order Alexa to order you milk or to switch off your lights. Instead, you train Alexa to train you to train it to know you so that it wins your trust with good recommendations so that it can modify your behaviour – ‘encourage’ you to buy a commodity from with Bezos retaining up to 40% of the price you pay (as a cloud rent). In short, the work that Alexa performs for you is not a commodity, unlike ChatGPT which works to sell you a commodity. In other words, ChatGPT is subject to market competition, to the likes of DeepSeek, but Alexa is not. This is why OpenAI, ChatGPT’s maker, is seriously damaged by the emergence of DeepSeek but Amazon is not. Thus, my basic point: Cloud capital is in a league of its own, beyond market competition from DeepSeek-like upstarts, because its power lies in its capacity to modify our behaviour and remove us from any market (e.g., to shift us from real markets to cloud fiefs like Amazon and Alibaba). In conclusion, cloud capital’s capacity to drive technofeudalism is not challenged by companies like DeepSeek. Only companies like OpenAI, which invested so much and so foolishly in providing a commodified service, stand to lose enormously. Yet another sign that capitalism is dead at the hand of cloud capital while technofeudalism is going from strength to strength and, as it does so, fuels even further the New Cold War between the US and China which in my book, Technofeudalism, I have explained away as the clash of the two huge concentrations of cloud capital: the American dollar-denominated super cloudalist power and the Chinese yuan-denominated one. Speaking of this New Cold War, which I have argued is mostly fuelled by the clash between American and Chinese cloud capital, I wonder what impact DeepSeek’s success will have on the US government. Silicon Valley and Washington DC had convinced themselves that America had a huge AI lead over China. Now, a tiny Chinese company has destroyed that confidence by producing on a shoestring better AI tech than Sillicon Valley had imagined possible. I can almost hear the whirring inside the heads of people in power on both America’s East and West Coast thinking that if the Chinese can do this out of the blue, what else can they do tomorrow? It is reminiscent of the Sputnik moment, isn’t it? It will be interesting to see how Trump reacts to this threat to companies American AI companies, especially since Elon Musk understands, and has spoken out against, the folly of commodifying AI services rather than going full on technofeudal. These are interesting times, in the traditional Chinese sense of the phrase.
9:27

Cloud capital versus AI - What DeepSeek’s spectacular success means for technofeudalism & the New Cold War DeepSeek, a Chinese artificial intelligence (AI) company, this week changed the global AI landscape, not to mention caused $1 trillion losses in the New York stock exchange and the NASDAC. In the process, it demonstrated the difference between cloud capital, which drives technofeudalism onward and upward, and AI-services, which were always a bubble waiting to burst. What remains to be seen is DeepSeek's impact on the New Cold War between the US and China which, from its beginning, was motivated by the clash between US and Chinese cloud capital. DeepSeek is China's response to OpenAI's ChatGPT. Its models perform as efficiently as their US counterparts. The difference is that DeepSeek is offered for free, making money only by selling services to developers - not to the public – at a fraction of the price OpenAI charges! The gist of DeepSeek’s arrival on the AI scene is a sudden transition from proprietary to open source technology. It is, therefore, no great wonder that, the moment DeekSeek became the most downloaded app on the Apple Store, it pulverised the market capitalisation of the, hitherto overinflated, US Big Tech companies. But how did this happen? How is a private commodity suddenly being offered for free? And does this mean that technofeudalism is in trouble? To begin with it is important to note AI was never a proprietary technology in itself. The underlying code has always been open source. What made AI quasi-private was the way these models were trained using huge amounts of privatised (that is stolen from us) data. A leaked Google memo in 2017, that was widely discussed in the industry at the time, but also widely refuted, explained: "If an open source LLM trained for a few million dollars outperforms the effectiveness of proprietary models... There will be no firewall to safeguard OpenAI either." DeepSeek pierced the US AI companies’ bubble by decommodifying the results of the model’s training, shifting them from behind a paywall to the public arena. Within days, developers around the world started building their own models on top of DeepSeek's. This is was the nightmare for US Big Tech's AI service providers who offered the results of prompts as a commodity, in the form of subscriptions. DeepSeek-type applications can now produce high-quality translations for free and, in so doing, undermine companies specialising in, for example, translation services, such as Germany's Deepl. In the broader scheme of things, this means that the morsels of cloud capital that Europe owns has lost its market value. Nevertheless, and this is a huge nevertheless, it is only AI-as-a-commodity that has lost its (grossly exaggerated) value. In sharp contrast, cloud capital utilised not as a commodity producing piece of tech but as produced means of behavioural modification is not at all threatened by companies like DeepSeek. And since technofeudalism is powered by cloud capital working that way, rather than commodity-like AI services of the ChaptGPT type, our technofeudal order is not threatened by competitors such as DeepSeek. To help understand the difference between cloud capital and AI-based commodified services it helps to compare and contrast Alexa and ChatGPT. Alexa is not offering you a commodified service. It is your free pretend-slave. Unlike ChatGPT you do not pay a subscription to Amazon for the right to order Alexa to order you milk or to switch off your lights. Instead, you train Alexa to train you to train it to know you so that it wins your trust with good recommendations so that it can modify your behaviour – ‘encourage’ you to buy a commodity from with Bezos retaining up to 40% of the price you pay (as a cloud rent). In short, the work that Alexa performs for you is not a commodity, unlike ChatGPT which works to sell you a commodity. In other words, ChatGPT is subject to market competition, to the likes of DeepSeek, but Alexa is not. This is why OpenAI, ChatGPT’s maker, is seriously damaged by the emergence of DeepSeek but Amazon is not. Thus, my basic point: Cloud capital is in a league of its own, beyond market competition from DeepSeek-like upstarts, because its power lies in its capacity to modify our behaviour and remove us from any market (e.g., to shift us from real markets to cloud fiefs like Amazon and Alibaba). In conclusion, cloud capital’s capacity to drive technofeudalism is not challenged by companies like DeepSeek. Only companies like OpenAI, which invested so much and so foolishly in providing a commodified service, stand to lose enormously. Yet another sign that capitalism is dead at the hand of cloud capital while technofeudalism is going from strength to strength and, as it does so, fuels even further the New Cold War between the US and China which in my book, Technofeudalism, I have explained away as the clash of the two huge concentrations of cloud capital: the American dollar-denominated super cloudalist power and the Chinese yuan-denominated one. Speaking of this New Cold War, which I have argued is mostly fuelled by the clash between American and Chinese cloud capital, I wonder what impact DeepSeek’s success will have on the US government. Silicon Valley and Washington DC had convinced themselves that America had a huge AI lead over China. Now, a tiny Chinese company has destroyed that confidence by producing on a shoestring better AI tech than Sillicon Valley had imagined possible. I can almost hear the whirring inside the heads of people in power on both America’s East and West Coast thinking that if the Chinese can do this out of the blue, what else can they do tomorrow? It is reminiscent of the Sputnik moment, isn’t it? It will be interesting to see how Trump reacts to this threat to companies American AI companies, especially since Elon Musk understands, and has spoken out against, the folly of commodifying AI services rather than going full on technofeudal. These are interesting times, in the traditional Chinese sense of the phrase.

Yanis Varoufakis

96,322 Aufrufe • vor 1 Jahr

Arthur Hayes: Selling Altcoins Because the AI Trade is About to Peak On June 13, 2026, Arthur Hayes Arthur Hayes stated in an interview with Cointelegraph that he had liquidated his altcoin positions, including HYPE, NEAR, and Worldcoin. Reflecting on his previous macro forecasts, he mentioned that current fiat liquidity did not flow into Bitcoin, but was instead entirely absorbed by the AI sector. Hayes believes the AI narrative is currently facing three problems: the vexing price of energy, the risk of US policy reversals, and the liquidity drain from the mega IPOs of Anthropic and OpenAI. He expressed that it is not as if AI goes down 50%, people have capital to invest in Bitcoin; rather, Bitcoin would get "thrown out with the bathwater." Given these risks are present, he decided to exit.
2:18

Arthur Hayes: Selling Altcoins Because the AI Trade is About to Peak On June 13, 2026, Arthur Hayes Arthur Hayes stated in an interview with Cointelegraph that he had liquidated his altcoin positions, including HYPE, NEAR, and Worldcoin. Reflecting on his previous macro forecasts, he mentioned that current fiat liquidity did not flow into Bitcoin, but was instead entirely absorbed by the AI sector. Hayes believes the AI narrative is currently facing three problems: the vexing price of energy, the risk of US policy reversals, and the liquidity drain from the mega IPOs of Anthropic and OpenAI. He expressed that it is not as if AI goes down 50%, people have capital to invest in Bitcoin; rather, Bitcoin would get "thrown out with the bathwater." Given these risks are present, he decided to exit.

Wu Blockchain

45,748 Aufrufe • vor 1 Monat

Hohn’s strategy essentially redefines quality as extreme resilience against substitution and competition over a multi-decade timeline. Hohn targets super-companies where physical, non-replicable assets create monopolistic barriers to entry, rendering executive talent largely irrelevant to the underlying cash flows. He deliberately avoids high-growth sectors vulnerable to disruption, viewing terminal value as the greatest source of alpha missed by short-term analysts. For instance, in infrastructure, he targets assets like cell towers, rail lines, and literal toll roads where tenancy ratios allow for near-100% margin expansion on zero-cost additions, and cash flow yields are contractually linked to inflation. In aerospace, his conviction is absolute. TCI's largest holding is $GE Aerospace. Hohn categorizes this as an unbreakable moat because the complexity of building aircraft engines has prevented any new market entrants in over 50 years. The true economic value, however, is not the engine itself, but the captive aftermarket spare parts and servicing ecosystem. Once an airline adopts an engine, the switching costs are effectively insurmountable, guaranteeing decades of highly predictable, high-margin revenue. Hohn’s view on moats is dynamic. He believes even the strongest moats can erode. In a definitive move in early 2026, TCI slashed a large portion of its $8B stake in $MSFT, which is a position held for nearly a decade. Hohn explicitly cited that the rapid advancement of Generative AI posed an existential risk to Microsoft's core Office and Azure moats, proving that he will mercilessly abandon a historically 'high quality' asset the moment technological disruption threatens its terminal value.
2:12

Hohn’s strategy essentially redefines quality as extreme resilience against substitution and competition over a multi-decade timeline. Hohn targets super-companies where physical, non-replicable assets create monopolistic barriers to entry, rendering executive talent largely irrelevant to the underlying cash flows. He deliberately avoids high-growth sectors vulnerable to disruption, viewing terminal value as the greatest source of alpha missed by short-term analysts. For instance, in infrastructure, he targets assets like cell towers, rail lines, and literal toll roads where tenancy ratios allow for near-100% margin expansion on zero-cost additions, and cash flow yields are contractually linked to inflation. In aerospace, his conviction is absolute. TCI's largest holding is $GE Aerospace. Hohn categorizes this as an unbreakable moat because the complexity of building aircraft engines has prevented any new market entrants in over 50 years. The true economic value, however, is not the engine itself, but the captive aftermarket spare parts and servicing ecosystem. Once an airline adopts an engine, the switching costs are effectively insurmountable, guaranteeing decades of highly predictable, high-margin revenue. Hohn’s view on moats is dynamic. He believes even the strongest moats can erode. In a definitive move in early 2026, TCI slashed a large portion of its $8B stake in $MSFT, which is a position held for nearly a decade. Hohn explicitly cited that the rapid advancement of Generative AI posed an existential risk to Microsoft's core Office and Azure moats, proving that he will mercilessly abandon a historically 'high quality' asset the moment technological disruption threatens its terminal value.

CapexAndChill

27,269 Aufrufe • vor 3 Monaten

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 run by a skeleton crew of about ~400 core engineers and product builders generating $1.3B in cash flow a quarter. The gaming studio acquisitions were purely strategic. From roughly 2018 to 2023, APP bought up mobile gaming studios. It was accumulating over 15 studios and 1,500 headcount. This was not because they wanted to be a gaming company, but strictly to harvest first-party conversion data. They needed proprietary behavioral and return on ad spend data to train their Axon deep learning models because third-party advertisers wouldn't share it. Once the Axon 2.0 model launched in April 2023, it became so hyper-effective that the entire gaming industry had no choice but to plug in and share their data to access the platform's unparalleled returns. Having served its purpose, APP then shed the distraction, selling off the gaming portfolio to Tripledot Studios. This was pure strategic rent extraction. They used the asset to train the AI, built the moat, and immediately dumped the asset. Then there is the capital allocation execution. Back in 2022, when the broader market abandoned the stock and pushed APP's market cap to a floor of $3.8B, the business was still printing over $1 B in EBITDA. Instead of executing standard open-market share repurchases, management identified that their private equity backers and early insiders held nearly 50% of the float and eventually wanted liquidity. Management bypassed the public market entirely. They negotiated directly with those institutional holders to execute a massive $6 billion leveraged buyback. They effectively retired a huge portion of the company at rock-bottom valuations. Foroughi noted this singular move generated $50 to $60 B in retained value for remaining shareholders as the stock rebounded. While the rest of big tech hoards headcount, APP actively fired 40% of its workforce in recent years while growing revenue by nearly 100% YoY. The C-suite consists only of the CEO, CFO, CTO, and General Counsel. There is no CRO, no COO, and virtually no salesforce. They replaced standard enterprise bloat with LLMs. Over 80% of their codebase is now written by AI, multiplying the output of their highest-tier engineers up to 100x. The product nearly sells itself. Advertisers plug in, set a performance goal, and if the ROAS is positive, they scale spend infinitely. It turns advertisers into blind arbitrageurs. The TAM expansion is also clear. Now that the model has perfected gaming monetization, APP is unleashing Axon onto e-commerce and local SMBs. They don't need a massive sales team to do this. The AI matches the right intent with the right ad, pushing conversion rates up. The company literally has internal compensation triggers tied to a $1 T market cap. Given they have grown from under $4 B in 2022 to roughly ~$154 B in market cap today, betting against this lean, hyper-competent team does not make much sense.
7:31

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 run by a skeleton crew of about ~400 core engineers and product builders generating $1.3B in cash flow a quarter. The gaming studio acquisitions were purely strategic. From roughly 2018 to 2023, APP bought up mobile gaming studios. It was accumulating over 15 studios and 1,500 headcount. This was not because they wanted to be a gaming company, but strictly to harvest first-party conversion data. They needed proprietary behavioral and return on ad spend data to train their Axon deep learning models because third-party advertisers wouldn't share it. Once the Axon 2.0 model launched in April 2023, it became so hyper-effective that the entire gaming industry had no choice but to plug in and share their data to access the platform's unparalleled returns. Having served its purpose, APP then shed the distraction, selling off the gaming portfolio to Tripledot Studios. This was pure strategic rent extraction. They used the asset to train the AI, built the moat, and immediately dumped the asset. Then there is the capital allocation execution. Back in 2022, when the broader market abandoned the stock and pushed APP's market cap to a floor of $3.8B, the business was still printing over $1 B in EBITDA. Instead of executing standard open-market share repurchases, management identified that their private equity backers and early insiders held nearly 50% of the float and eventually wanted liquidity. Management bypassed the public market entirely. They negotiated directly with those institutional holders to execute a massive $6 billion leveraged buyback. They effectively retired a huge portion of the company at rock-bottom valuations. Foroughi noted this singular move generated $50 to $60 B in retained value for remaining shareholders as the stock rebounded. While the rest of big tech hoards headcount, APP actively fired 40% of its workforce in recent years while growing revenue by nearly 100% YoY. The C-suite consists only of the CEO, CFO, CTO, and General Counsel. There is no CRO, no COO, and virtually no salesforce. They replaced standard enterprise bloat with LLMs. Over 80% of their codebase is now written by AI, multiplying the output of their highest-tier engineers up to 100x. The product nearly sells itself. Advertisers plug in, set a performance goal, and if the ROAS is positive, they scale spend infinitely. It turns advertisers into blind arbitrageurs. The TAM expansion is also clear. Now that the model has perfected gaming monetization, APP is unleashing Axon onto e-commerce and local SMBs. They don't need a massive sales team to do this. The AI matches the right intent with the right ad, pushing conversion rates up. The company literally has internal compensation triggers tied to a $1 T market cap. Given they have grown from under $4 B in 2022 to roughly ~$154 B in market cap today, betting against this lean, hyper-competent team does not make much sense.

CapexAndChill

24,419 Aufrufe • vor 3 Monaten

GOOD NEWS 🚨 DAN IVES PREDICTS TESLA WILL OWN '80%' OF ROBOTAXI MARKET — SAYS $TSLA IN HIS TOP 5 LIST FOR 2026 🆙 Ives is doubling down on Tesla as one of his top conviction picks for 2026, insisting that investors need to stop viewing it as a "car story" and start treating it as a robotics and AI play. He sees the stock fundamentally heading toward $1,000, with a current base case of $600 and a bull case of $800. According to Ives, this is a defining moment for Elon Musk—arguably his most important year ever—as the company fully commits to the AI path. He expects this pivot to unlock massive value, projecting a $2 trillion market cap over the coming year and a bull case where Tesla hits $3 trillion by the end of 2026. The real engine behind this growth is the Robotaxi network, which Ives views as integral to the company's future. He predicts Tesla will eventually corner 80% of the autonomous driving market. While he admits that competitors like Waymo have had a huge head start with "phenomenal technology," he argues that no one else can match the sheer scale and scope of Tesla's venture in the long run. To prove the concept is working, Ives is looking for Tesla to hit a specific benchmark this year: launching Robotaxis in 30 cities to achieve critical scale, expanding beyond their initial start in Austin and moving into markets like California. When it comes to the speed of this rollout, Ives is fully supportive of Musk’s "brick by brick" strategy rather than a rushed launch. He emphasizes that because lives are at stake, safety has to take priority; it is far more important to expand the geospaced areas correctly than to just do it quickly. Ultimately, while he acknowledges Nvidia as the chip fueling the broader AI revolution, he sees Tesla standing apart as the dominant force in the physical autonomous world.
13:00

GOOD NEWS 🚨 DAN IVES PREDICTS TESLA WILL OWN '80%' OF ROBOTAXI MARKET — SAYS $TSLA IN HIS TOP 5 LIST FOR 2026 🆙 Ives is doubling down on Tesla as one of his top conviction picks for 2026, insisting that investors need to stop viewing it as a "car story" and start treating it as a robotics and AI play. He sees the stock fundamentally heading toward $1,000, with a current base case of $600 and a bull case of $800. According to Ives, this is a defining moment for Elon Musk—arguably his most important year ever—as the company fully commits to the AI path. He expects this pivot to unlock massive value, projecting a $2 trillion market cap over the coming year and a bull case where Tesla hits $3 trillion by the end of 2026. The real engine behind this growth is the Robotaxi network, which Ives views as integral to the company's future. He predicts Tesla will eventually corner 80% of the autonomous driving market. While he admits that competitors like Waymo have had a huge head start with "phenomenal technology," he argues that no one else can match the sheer scale and scope of Tesla's venture in the long run. To prove the concept is working, Ives is looking for Tesla to hit a specific benchmark this year: launching Robotaxis in 30 cities to achieve critical scale, expanding beyond their initial start in Austin and moving into markets like California. When it comes to the speed of this rollout, Ives is fully supportive of Musk’s "brick by brick" strategy rather than a rushed launch. He emphasizes that because lives are at stake, safety has to take priority; it is far more important to expand the geospaced areas correctly than to just do it quickly. Ultimately, while he acknowledges Nvidia as the chip fueling the broader AI revolution, he sees Tesla standing apart as the dominant force in the physical autonomous world.

Ming

29,004 Aufrufe • vor 7 Monaten

Kevin Warsh is already signaling what could be a major shift in Fed policy. While rising oil and a softer labor market have investors worried about inflation, he argues an AI-driven productivity boom from companies like $NVDA, $MSFT, $GOOGL and $AMZN could actually push prices lower. If that view gains traction, it could reshape how the market thinks about growth and rates. A productivity wave powered by AI could allow the economy to expand faster while inflation cools, changing the framework investors have used since the post-pandemic cycle.
1:09

Kevin Warsh is already signaling what could be a major shift in Fed policy. While rising oil and a softer labor market have investors worried about inflation, he argues an AI-driven productivity boom from companies like $NVDA, $MSFT, $GOOGL and $AMZN could actually push prices lower. If that view gains traction, it could reshape how the market thinks about growth and rates. A productivity wave powered by AI could allow the economy to expand faster while inflation cools, changing the framework investors have used since the post-pandemic cycle.

Sam Badawi

120,848 Aufrufe • vor 5 Monaten

Ahead of big tech earnings next week, Dan Ives of Wedbush tells Nicole Petallides that he sees $MSFT hitting a $5 trillion market cap in the next 18 months as it enters "a golden era" of AI. The "AI party," according to Ives, is going to "power the tech bull market" for the next 2-3 years. His price target on some of the companies set to report: — $TSLA: Outperform, $500 price target — $AAPL: Outperform, $270 price target — $MSFT: Outperform, $600 price target Here's what else he had to say, including his outlook for Tesla and Meta — and why it's time for Apple to "change its DNA" and do "something big."
12:38

Ahead of big tech earnings next week, Dan Ives of Wedbush tells Nicole Petallides that he sees $MSFT hitting a $5 trillion market cap in the next 18 months as it enters "a golden era" of AI. The "AI party," according to Ives, is going to "power the tech bull market" for the next 2-3 years. His price target on some of the companies set to report: — $TSLA: Outperform, $500 price target — $AAPL: Outperform, $270 price target — $MSFT: Outperform, $600 price target Here's what else he had to say, including his outlook for Tesla and Meta — and why it's time for Apple to "change its DNA" and do "something big."

Schwab Network

52,873 Aufrufe • vor 1 Jahr

🇺🇸 SOMETHING INSANELY BAD IS GOING TO HAPPEN TO THE U.S. STOCK MARKET. According to an old Wall Street market cycle paper, the S&P-500 is currently in the Euphoria stage. What comes next is an insane market crash below $2,000. If that happens, it will be worse then what we saw in 2008 and the dot-com crash in 2000. It will be written down in history books as the worst market crash. Most people are over-leveraged and not prepared for this. Regardless of what happens, always expect the worst. Stay prepared.
0:22

🇺🇸 SOMETHING INSANELY BAD IS GOING TO HAPPEN TO THE U.S. STOCK MARKET. According to an old Wall Street market cycle paper, the S&P-500 is currently in the Euphoria stage. What comes next is an insane market crash below $2,000. If that happens, it will be worse then what we saw in 2008 and the dot-com crash in 2000. It will be written down in history books as the worst market crash. Most people are over-leveraged and not prepared for this. Regardless of what happens, always expect the worst. Stay prepared.

Coinvo Trading

81,642 Aufrufe • vor 2 Monaten

Bill Ackman on his $UBER thesis from an interview released today: "We think they're a big beneficiary of you know autonomous driving is going to reduce the cost of getting from one place to another is going to reduce insurance costs. These are and that will of course drive more demand right as the price comes down."
1:33

Bill Ackman on his $UBER thesis from an interview released today: "We think they're a big beneficiary of you know autonomous driving is going to reduce the cost of getting from one place to another is going to reduce insurance costs. These are and that will of course drive more demand right as the price comes down."

CapexAndChill

89,396 Aufrufe • vor 9 Tagen

The message from Gautam Adani is clear and urgent: At the inauguration of the Sharadchandra Pawar Artificial Intelligence Centre of Excellence (AI CoE) in Baramati, Gautam Adani delivered an address that went far beyond technology. Colonisation today won’t come through territory. It will come through data, narratives, and cognitive capture. AI has the power to reorganise our memory as a civilisation — what we celebrate, what we forget, and what we believe. India must step up not just as an AI user, but as a guardian of its cultural and philosophical heritage in the AI age.
3:11

The message from Gautam Adani is clear and urgent: At the inauguration of the Sharadchandra Pawar Artificial Intelligence Centre of Excellence (AI CoE) in Baramati, Gautam Adani delivered an address that went far beyond technology. Colonisation today won’t come through territory. It will come through data, narratives, and cognitive capture. AI has the power to reorganise our memory as a civilisation — what we celebrate, what we forget, and what we believe. India must step up not just as an AI user, but as a guardian of its cultural and philosophical heritage in the AI age.

Mona Patel 🇮🇳🐅🌳

25,979 Aufrufe • vor 7 Monaten