The magnificent seven stocks no longer trading as a monolith and the gap well it could widen in 2026. McKenzie Sagalas joins us now and she will explain. McKenzie.>> Hey David. So there's roughly a 60 point spread in the mag 7 year to date with Alphabet leading the pack and Amazon in last place. And that divergence tells you what Wall Street cares about.It's now rewarding companies that show monetization and margin. Leading the pack is Alphabet. It's pulling ahead because it owns the full stack.AI chips, cl ...

Core Insights - The focus on cloud technology is highlighted with major companies like Salesforce and Snowflake reporting earnings, while other tech giants show mixed trading results [1] - Amazon's strategy in the AI sector is under scrutiny, particularly its recent announcements at the AWS Reinvent event [2][4] Amazon's AI Strategy - Amazon is emphasizing a focused approach to AI, with significant improvements in training AI models using its Nova Forge platform, claiming a 40% to 60% enhancement in performance [5] - The introduction of the Tranium AI chip is seen as a potential challenge to Nvidia's dominance in the GPU market, with availability starting immediately and broader rollout planned for next year [7][8] - AWS is on its third generation of custom silicon with Tranium, indicating a consistent strategy to develop in-house technology alongside partnerships with established companies like Nvidia [9] Market Dynamics - The competitive landscape is evolving, with AWS's announcements suggesting a growing demand for various silicon solutions, which may balance market share among major players like Nvidia and AMD [12] - AWS's focus on enterprise needs is evident, as it aims to deliver AI at scale while addressing foundational infrastructure requirements [15][16] Trading Insights - Amazon's stock has underperformed recently, down approximately 10% from its all-time highs, prompting analysts to suggest bullish strategies [17][18] - A specific trading strategy involves buying a call option with a strike price of 230 and selling a call at 250, creating a bullish vertical spread with a potential profit of over $1,200 if the stock rises above 250 [20][22]

Now that [music] big AI shift is where we start today and we've got some team coverage. Dear Drabosa looking at how Google is building out its AI ecosystem. Christina Partzene is digging into how that's impacting the supply chain.And Julia Borston has the Mag7 stock that can't seem to find its AI footing. Deerra, we're going to start with you. >> So Dom in AI the leaderboard.It changes fast and narratives they can swing harder than the fundamentals. Just look at Google. It went from broken to beloved in a m ...

Core Insights - The AI landscape is rapidly evolving, with Google gaining significant momentum through its custom AI chips and execution of Gemini 3, shifting from a struggling position to a favored one in a short time frame [2][3] - The competition is not solely between Google and Nvidia; rather, it involves Google against other hyperscalers like Amazon and Microsoft, particularly in the context of custom silicon [5][4] Google’s AI Strategy - Google is effectively leveraging its custom AI chips, known as TPUs, which are designed to operate within its cloud ecosystem, providing a competitive edge in distribution [3][4] - The narrative surrounding TPUs suggests they may not easily surpass Nvidia's offerings due to their restrictive nature, which limits flexibility for enterprises [4] Amazon’s Position - Amazon, as the largest cloud computing player, is expected to lead in AI but is currently perceived to be lagging behind Google in terms of custom chip development and AI model recognition [6][7] - The disappointment surrounding Amazon's custom AI chip, Tranium, indicates a gap in momentum compared to Google, with Amazon's AI models not gaining significant traction in the market [7][8]

Group 1: Market Overview - The US stock market is experiencing a decline, with the Dow down nearly 300 points, approximately 0.61% [2] - The tech-heavy NASDAQ is leading the sell-off, with significant drops in large-cap tech stocks [3][5] - Qualcomm shares fell by 1.8% following earnings, while AMD saw a larger decline of about 5% [4] Group 2: Earnings Season Insights - Many tech companies are beating earnings estimates but still seeing stock price declines, indicating high investor expectations [6][12] - Qualcomm's recent earnings report did not provide additional details on a new data center chip, contributing to the stock's negative reaction [9] - Super Micro reported a 15.8% quarter-over-quarter growth in the semiconductor industry, but its stock fell due to design issues [21] Group 3: Warner Brothers Discovery - Warner Brothers Discovery reported a third-quarter loss of $148 million on $9 billion in revenue, with a 6% decline in revenue [33][38] - The company is planning to split into two entities by mid-2026, while also exploring strategic alternatives, including potential sales [35][36] - The studio and streaming businesses are seen as high-growth areas, generating nearly $4 billion in EBITDA, while the TV networks face challenges [41][49] Group 4: AI and Semiconductor Sector - The AI trade remains strong, with companies like Qualcomm, AMD, and Nvidia positioned to benefit from AI infrastructure investments [14][31] - Investors are encouraged to consider buying dips in semiconductor stocks, as earnings beats can lead to lower valuations if stock prices do not react positively [16][17] - The focus is shifting towards AI-powered infrastructure, including energy and networking opportunities [30][31] Group 5: M&A Activity - SoftBank is reportedly considering acquiring Marll Technology to combine it with ARM, indicating potential consolidation in the semiconductor space [24][25] - Marll is viewed as undervalued compared to peers, making it an attractive target for acquisition [25] Group 6: Supreme Court and Tariffs - The Supreme Court is hearing arguments regarding the legality of President Trump's tariffs, which have generated nearly $200 billion in revenue [74][120] - A ruling against the administration could lead to economic uncertainty and impact growth and hiring [120]

Core Insights - The company expects its full-year capital expenditure (capex) to be approximately $125 billion, with an anticipated increase in 2026 primarily directed towards AWS to support AI demand [1] - The company is also investing more in its in-house AI chips called Tranium, although this investment trails Microsoft's annual capex spend of $140 billion [1] - The CEO discussed a significant AI investment project named "Project Rainer," which involves collaboration with Anthropic [2]

Core Viewpoints - Concerns about AI capital expenditure (capex) peaking in 2026 are overstated, with strong growth certainty expected in 2026-2027 [1][2] - Major cloud service providers (CSPs) are well-positioned to sustain capital expenditure growth due to increasing operating cash flow [4][6] - The entry of new investment players and the expansion of AI application scenarios are driving continued investment in AI [2][9] AI Capital Expenditure Growth - Morgan Stanley predicts a minimum growth rate of 20% for AI capex in 2026, with potential for further growth in 2027 if enterprise-level AI adoption increases [2][8] - The top four CSPs (Google, Amazon, Meta, Microsoft) are expected to see a compound annual growth rate (CAGR) of 23% in EBITDA and operating cash flow from 2022 to 2026 [6][8] - Capital expenditure for these CSPs is projected to rise from $150 billion in 2022 to $398 billion in 2026, while free cash flow is expected to maintain a CAGR of 16% [6][8] Investment Opportunities - The Chinese market for AI capex is still in its early stages, with significant potential for growth driven by companies like ByteDance and Alibaba [12] - Data center companies and server manufacturers are positioned to benefit from both NVIDIA and domestic chip supply growth [12] - The semiconductor supply chain, particularly for Google TPU and NVIDIA, is expected to see robust growth, with Google leading in 2026 [13][14] Pricing Trends and Earnings Adjustments - Price increases in non-AI sectors are becoming widespread, which could drive the next round of earnings per share (EPS) adjustments [18] - Areas experiencing price increases include DRAM, BT substrates, and power ICs, while some sectors may still face downward pricing pressure [18] - The valuation of Asian tech stocks remains reasonable, with expectations for further EPS adjustments driven by rising prices and sustained AI demand [19][20]

Group 1: Founder Psychology and Roles - Eric Schmidt emphasizes the difference between founders and professional managers, stating that founders are visionaries while professional managers are "amplifiers" who help scale ideas [4][10] - Schmidt reflects on his experience at Google, noting that he was not a typical entrepreneur but rather a professional manager who contributed during the company's scaling phase [3][4] - He discusses the challenges of attracting talent, highlighting that many talented individuals often choose to start their own companies instead of joining established firms [3][5] Group 2: Market Dynamics and Startup Ecosystem - Schmidt points out that many startups are often acquired for their talent rather than their products, indicating a market structure that can be inefficient [6][7] - He notes that the majority of startups fail, with traditional venture capital experiences suggesting that 4 out of 10 will fail completely, and 5 will become "zombies" with no growth potential [7] - The conversation highlights the importance of competition for startups, suggesting that true leadership is demonstrated when facing challenges from larger companies [11][12] Group 3: AI and Future Trends - Schmidt believes that AI is currently underestimated rather than overhyped, citing the scaling laws that drive AI advancements [33][34] - He discusses the potential of AI to transform business processes and scientific breakthroughs, emphasizing the importance of understanding how humans will coexist with advanced AI systems [35][39] - The conversation touches on the competitive landscape between the U.S. and China in AI development, with China investing heavily in AI as a national strategy [41][42] Group 4: Talent Acquisition and Management - Schmidt stresses the importance of attracting top talent by creating an environment where individuals feel they are solving significant problems [18][20] - He differentiates between "rockstar" employees who drive change and "mediocre" employees who are self-serving, advocating for the retention of the former [21][22] - The discussion includes insights on how to identify and nurture high-potential talent within organizations [24][25] Group 5: Challenges in AI Development - Schmidt highlights the challenges of defining reward functions in reinforcement learning, which is crucial for AI's self-learning capabilities [51] - He warns about the potential pitfalls of over-investing in AI infrastructure without a clear path to profitability, suggesting that many companies may face economic traps [47][48] - The conversation concludes with a call for companies to focus on the most challenging problems in AI, as solving these will yield the most significant rewards [52][53]