Intel Earnings Signal Possible Comeback | Bloomberg Tech 10/24/2025

Key Concepts

• Intel's Return to Profitability: Intel has achieved profitability on a net income basis for the first time in a while, driven by better-than-expected PC market performance and some progress in servers and foundry.
• AI Infrastructure Debt Offering: A significant $38 billion debt offering is being prepared to fund data centers tied to Oracle, highlighting the massive financial requirements for AI infrastructure.
• Mistral AI's New Platform: Mistral AI has launched a new platform designed to help enterprise clients build customizable AI tools, emphasizing integration, data ownership, and private cloud deployment.
• Quantum Computing Milestone: Google has achieved a significant milestone in quantum computing by running an algorithm that is 13,000 times faster than current supercomputers, bringing quantum computing closer to practical applications.
• AWS Challenges: Amazon Web Services (AWS) is facing increased competition in the cloud market, particularly in AI workloads, and experienced a major outage, raising concerns about its leadership position.
• Trade Policy Impact: The long-term implications of deglobalization on earnings are being considered, with a recognition that policy changes may not have immediate discernible effects.
• Valuation Concerns: Broadly, the U.S. market, especially the technology sector, is considered expensive, with potential for further gains if earnings exceed expectations, but low immunity to negative surprises.

Intel's Comeback Efforts and Profitability

Intel has reported a return to profitability on a net income basis, marking a significant step in its comeback efforts. While an initial post-earnings rally faded, the company is showing progress in several areas.

• Key Points:

  • Profitability: Intel is profitable for the first time in a long time on a net income basis.
  • PC Market: The Personal Computer (PC) market performed better than anticipated, contributing to the positive results.
  • Server Progress: The company is making some progress in the server market.
  • Foundry: There is a small but present improvement in Intel's foundry business.
  • Demand vs. Supply: Demand is currently running ahead of supply, a trend expected to continue into the first half of 2026.
  • Gross Margins: Gross margin improvements are noted, though an expansion is not expected next year due to new debt.
  • Structural Changes: The move of operations from Oregon to Arizona is seen as a positive structural change, with more volume expected to return to foundries.
  • Debt Management: A significant portion of the company's strategy involves clearing debt, but uncertainties remain about the remaining challenges.

• Analyst Perspective (Bloomberg Intelligence):

  • The results show the "first sign in the right direction" and the "beginning of a turnaround."
  • Challenges are not gone, and there is "still a lot to chew on."
  • Concerns remain about the next iteration of chip fabrication and Intel's ability to rival TSMC and others in advanced manufacturing.
  • The performance of the 18A manufacturing process is described as "disappointing" in terms of yield and quality, despite ramping volumes expected by early next year.
  • The long-term success of Intel's manufacturing in the U.S. requires more than just government and NVIDIA support; it needs a "mid-tier commitment of a coalition" and strong execution.

• CEO/CFO Commentary:

  • The CFO expressed surprise at some end-market performance, particularly in servers.
  • The argument is that hyper-scalers are realizing the necessity of updating their CPUs and investing in them, leading to year-over-year unit growth.
  • The PC segment shipments were stronger than anticipated, despite concerns about tariffs.

• Technical Terms:

  • Foundry: A manufacturing plant that produces semiconductor chips.
  • Gross Margin: The difference between revenue and the cost of goods sold.
  • 18A: Refers to Intel's 18 Angstrom process node, a key advanced manufacturing technology.
  • CPU (Central Processing Unit): The primary component of a computer that performs most of the processing.
  • Hyper-scalers: Large cloud computing providers like Amazon, Microsoft, and Google.

AI Infrastructure Financing and Debt Markets

The immense demand for AI infrastructure is driving significant financial activity, including large debt offerings.

• Key Points:

  • $38 Billion Debt Offering: Banks are preparing a $38 billion debt offering to fund data centers tied to Oracle. This is a record for AI infrastructure financing.
  • Vantage Developer: The debt is being taken on by Vantage, the developer of the project, not Oracle itself.
  • Asset-Backed Securitization: The debt structure involves asset-backed securitization, where future revenue from contracts is used as collateral.
  • Yield and Exposure: The offering provides a yield of 2.5 points, offering investors exposure to the AI narrative through debt rather than solely equity.
  • Market Appetite: A key question is the appetite for such large debt deals going forward, which is critical for converting RPO (Remaining Performance Obligations) into revenue.
  • Financing AI Deals: The scale of deals, such as OpenAI's reported $300 billion deal with Oracle, necessitates massive financing for data centers and equipment.
  • Private Debt Market Capacity: The ability of the private debt market to absorb this level of debt is a significant consideration.
  • Bank of England Concern: The Bank of England is reportedly probing data center lending due to concerns about potential AI bubbles.

• Analyst Perspective (Marta Norton, Empower):

  • The rationale for complacency around the AI trade was that it was funded by well-capitalized hyper-scalers using existing capital.
  • However, recent debt issuance signals a broader range of financing will be needed to meet targets.
  • There's a difference between "extreme valuations" and an "AI bubble," with the former being the current state.
  • More aggressive sentiment and far more debt issuance would be needed for a true AI bubble.
  • The primary exposure to AI remains in the equity market, though active credit managers can gain added yield.
  • Investors should be cautious about adding debt exposure, and tight credit spreads are a valuation risk to watch.

• Technical Terms:

  • Debt Offering: The issuance of debt securities to raise capital.
  • Data Centers: Facilities that house computer systems and associated components, such as telecommunications and storage systems.
  • Asset-Backed Securitization: A financial process where assets are pooled together and used as collateral for the issuance of securities.
  • RPO (Remaining Performance Obligations): The value of contracted services that have not yet been performed.
  • Private Debt Market: Debt financing provided by non-bank lenders.

Quantum Computing Advancements

Google has announced a significant breakthrough in quantum computing, demonstrating a quantum algorithm that is substantially faster than current supercomputers.

• Key Points:

  • Quantum Algorithm Milestone: Google ran a quantum algorithm that is verifiable and 13,000 times faster than today's best supercomputers.
  • Quantum vs. Classical Computing: Classical computers use bits (0s and 1s), while quantum computers use qubits, which can represent combinations of 0s and 1s simultaneously, enabling them to tackle different types of problems.
  • Scalability and Fault Tolerance: A concern raised is the use of a non-scalable or fault-tolerant chip for this demonstration, posing a challenge for commercialization.
  • Error Correction: Google announced in December that error correction can work and has demonstrated this, continuing to push the number of qubits and reduce errors.
  • Timeline for Application: Google is optimistic about seeing real-world applications of quantum computing in science and medicine within the next five years.
  • Molecular Simulation: The announced breakthrough can be used to simulate and calculate the exact structure of molecules.
  • Talent and Investment: The team includes Nobel Prize winners, highlighting the caliber of talent and significant investment in the field.
  • Government Support: The U.S. government is a strong supporter and partner in quantum computing research.
  • Hardware Progress: A key marker of progress is continuing to push hardware, with the goal of reaching a million qubits from the current 105 qubits on the "Willow" chip.
  • Supply Chain and Talent: Talent acquisition and development are crucial for advancing quantum computing.

• Technical Terms:

  • Quantum Computing: A type of computation that harnesses quantum-mechanical phenomena, such as superposition and entanglement, to perform calculations.
  • Bits: The basic unit of information in classical computing, representing either a 0 or a 1.
  • Qubits (Quantum Bits): The basic unit of quantum information, which can exist in a superposition of 0 and 1.
  • Superposition: A quantum mechanical principle that allows a qubit to be in multiple states simultaneously.
  • Entanglement: A quantum mechanical phenomenon where two or more qubits become linked in such a way that they share the same fate, regardless of the distance between them.
  • Fault Tolerance: The ability of a quantum computer to perform computations reliably even in the presence of errors.
  • Error Correction: Techniques used to detect and correct errors that occur in quantum computations.
  • Algorithm: A set of rules or instructions to be followed in calculations or other problem-solving operations.

Mistral AI's Enterprise Focus

European AI startup Mistral AI has launched a new platform aimed at enabling enterprises to build and deploy custom AI tools.

• Key Points:

  • New Platform Release: Mistral AI has released a platform to help clients create customizable AI tools that are easy to operate within their businesses.
  • Enterprise Focus: The platform is designed for the enterprise category, competing with players like Anthropic and OpenAI.
  • Value Creation: The platform aims to create value for enterprises by enabling them to build AI applications that belong to them, contain their intellectual property (IP), and leverage their internal knowledge.
  • Differentiation:
    • Integrated Approach: Mistral's solution is more integrated, encompassing the model, front-end, back-end, and learning mechanisms needed to go from a model to an application.
    • Data Ownership: Mistral believes enterprises should own their AI systems, with IP and data remaining with the company and not necessarily flowing back to Mistral.
    • Private Cloud Deployment: The platform can be deployed on private clouds.
  • Onboarding and Time to Production: Mistral aims for a quarter-long cycle from idea to production for enterprise AI applications.
  • Example Case Study: A shipping company, a major investor, struggled with AI adoption (95% of cases not reaching production). Mistral deployed AI applied engineers to work with business owners, automating processes like container dispatching, achieving global scale in four months.
  • Funding: Mistral has raised $1.7 billion in equity and has a commercial agreement with CMA CGM, a French shipping giant. They have also raised $3 billion Euros in total.
  • U.S. Market Penetration: Mistral is working with U.S. companies like Cisco (for private cloud deployment and customer experience) and Snowflake (for data processing and structuring). They are also a partner with Microsoft.
  • Mistral Compute: To address the growing need for compute capacity, Mistral has created "Mistral Compute" to build digital GPUs in Europe, acquiring hundreds of megawatts of capacity.
  • Financing: Mistral uses equity to train models and has long-term contracts for compute capacity, financed through bank debt, which they believe mitigates overexposure to debt.

• Technical Terms:

  • Foundational Models: Large, pre-trained AI models that can be adapted for various downstream tasks.
  • Intellectual Property (IP): Creations of the mind, such as inventions and literary and artistic works.
  • Private Cloud: A cloud computing environment that is dedicated to a single organization.
  • AI Agents: Software programs that can perform tasks autonomously.
  • Legacy Software: Older computer systems and applications that are still in use.
  • GPU (Graphics Processing Unit): A specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. GPUs are also used for general-purpose computing.

Trade Policy and Market Volatility

The discussion touches upon the impact of trade policy and its potential for market volatility.

• Key Points:

  • Tariff Impact: Despite concerns about tariffs, their impact on earnings and inflation has not been immediately discernible.
  • Policy vs. Immediate Effect: A significant change in policy does not necessarily translate to an immediate impact.
  • Long-Term Implications of Deglobalization: The rollback of globalization is expected to be a headwind for earnings, though potentially one that can be overcome.
  • Trade Talk Uncertainty: Trade talks, particularly with Canada and China, are characterized by ongoing negotiation and uncertainty, contributing to market volatility.
  • President Trump's Actions: President Trump's halt of trade talks with Canada due to an advertisement highlights the unpredictable nature of trade policy.

• Analyst Perspective (Marta Norton):

  • Trade talks are like a "toddler that just won't go to bed," indicating the story is far from over.
  • There is a lot of uncertainty regarding the overall effective tariff rate and the fine details of trade talks.
  • This ongoing uncertainty will lead to "lingering volatility within the market."

Market Valuations and Sector Performance

The current market environment is characterized by high valuations, particularly in the technology sector, with a cautious outlook on future performance.

• Key Points:

  • Expensive Market: The U.S. market broadly, and almost every sector, is considered quite expensive, with valuations in the ninth and tenth deciles historically.
  • Technology Sector: High valuations are observed across various industries within the technology sector.
  • Potential for Further Gains: The market could continue to rise if earnings exceed expectations.
  • Low Immunity: The market has "low immunity" to negative surprises, meaning an earnings season that meets expectations might not significantly move the stock market higher.
  • Healthcare as an Exception: Healthcare is highlighted as a more attractively priced sector with potential to benefit from AI, despite facing its own challenges.

• Analyst Perspective (Marta Norton):

  • The primary exposure to AI is still within the equity market.
  • Credit market spreads are tight, which is a valuation risk.

AWS Challenges and AI Workloads

Amazon Web Services (AWS) is facing increased competition and operational challenges, particularly in the context of AI workloads.

• Key Points:

  • Increased Competition: AWS is no longer the sole leader in cloud computing, facing credible rivals like Microsoft, Oracle, and Google.
  • Market Share Decline: AWS's traditional cloud market share has declined from 50% to 38% over the past five to six years.
  • Commoditization of Services: Services that AWS pioneered are becoming commoditized, with other companies offering similar packages.
  • AI Workload Lag: AWS is perceived as trailing its rivals in the AI workload business.
  • Anthropic's Hedge: Anthropic, a major AI player, is co-developing AI chips with Amazon but also has an option to move towards Google, posing a risk to Amazon's AI business.
  • Major Outage: A significant outage on Monday, described as one of the worst in AWS history, has raised concerns about the reliability of the service.

• Analyst Perspective (Matt Day):

  • The headline is that "Sam [AWS] is no longer the only game in town."
  • The AI workload business is not an area where AWS is currently leading.

JPMorgan's Deeper Dive into Crypto

JPMorgan is expanding its involvement in the cryptocurrency space by allowing clients to use Bitcoin and Ether as collateral for loans.

• Key Points:

  • Collateral for Loans: JPMorgan will allow institutional clients to use their holdings of Bitcoin and Ether as collateral for financing by the end of the year.
  • Third-Party Custodian: The crypto assets will be transferred to a third-party custodian appointed by JPMorgan, meaning the bank does not directly handle the crypto.
  • Institutional Client Focus: This move is targeted at institutional clients who have significant crypto gains they wish to leverage.
  • Shift in Stance: This represents a deeper engagement with crypto for JPMorgan, contrasting with previous statements by CEO Jamie Dimon calling them "pet rocks."
  • Regulatory Hurdles: Banks face regulatory challenges, such as Basel rules, that can make direct handling of crypto expensive due to capital requirements.
  • Client-Centric Approach: JPMorgan is facilitating client access to crypto as collateral rather than holding it directly, reflecting a cautious approach.

• Technical Terms:

  • Collateral: An asset that a borrower offers to a lender to secure a loan.
  • Custodian: An institution that holds and safeguards financial assets on behalf of its clients.
  • Institutional Clients: Large organizations, such as pension funds, endowments, and investment firms, that engage in financial transactions.
  • Basel Rules: A set of international banking regulations issued by the Basel Committee on Banking Supervision.

Intel's Foundry and Packaging Strategy in AI

Intel's strategy for competing in the AI space, particularly within its foundry and packaging business, is being closely examined.

• Key Points:

  • Foundry Opportunity in AI: Intel's CFO sees opportunity in AI accelerators, specifically within the foundry bit, focusing on waivers and packaging.
  • Waivers and Packaging: This strategy is seen as realistic and pragmatic, focusing on areas where Intel has existing strengths.
  • Focus on PC and Server: Management is likely to maximize and concentrate on the PC and server businesses.
  • NVIDIA Partnership: Intel's ability to deal with complex work and the demand for their products has triggered NVIDIA's move to open up high connectivity and invest in Intel for optimized system integration.
  • X86 vs. NVIDIA: While Intel's X86 chips are expected to hold a single-digit market share, NVIDIA is anticipated to dominate the remaining 90+ percent.
  • Manufacturing in the U.S.: For Intel to successfully manufacture and package chips in the U.S., it requires government funding, major progress on coalition building, and strong execution.

• Analyst Perspective (Pierre Ferragu):

  • The foundry opportunity in AI being in waivers and packaging "doesn't sound hugely exciting" but "sounds realistic and plain."
  • Intel's management likely wants to build on its existing packaging capabilities.
  • The partnership with NVIDIA is described as a "genuine, thorough, and deep interest for Intel products in the industry."
  • Achieving victory in U.S. chip manufacturing requires more than just money and NVIDIA's support; it needs a "mid-tier commitment of a coalition" and "execution."

Conclusion and Future Outlook

The broadcast covered a range of critical developments in the tech industry, from Intel's financial turnaround and the massive financing needs of AI infrastructure to breakthroughs in quantum computing and the evolving competitive landscape of cloud services. The overarching themes highlight the rapid pace of innovation, the significant capital required to fuel growth, and the increasing complexity of the technological ecosystem. Investors and industry players are navigating a period of both immense opportunity and considerable risk, with a keen eye on future earnings reports and technological advancements. The upcoming earnings season, particularly for tech giants like Meta, Microsoft, and Amazon, is anticipated to be a key indicator of market direction.