Everyone’s watching tech but the next big deals may be industrial

Key Concepts

• Enterprise AI: AI applications focused on internal business processes (customer service, coding, HR, finance) for automation, efficiency, and cost savings.
• Rack Scale Two-Ton Systems: High-density computing systems designed for AI workloads, announced by Nvidia and AMD.
• Hyperscalers: Large-scale cloud providers (e.g., AWS, Azure, Google Cloud) heavily investing in AI infrastructure.
• Moat: A sustainable competitive advantage that protects a company’s market share.
• TPU (Tensor Processing Unit): Custom AI accelerator chip developed by Google.
• Support and Resistance: Technical analysis concepts identifying potential price levels where a stock may stall or reverse direction.
• Regime Change: A significant shift in a company’s leadership or strategy, particularly relevant to Apple.
• Vibe Coding: Utilizing AI-powered tools for simplified code generation, often through web applications and chatbots.

Enterprise AI and the 2026 Landscape

Daniel Newman highlighted 2026 as the “year of monetization of AI,” moving beyond hype to demonstrable earnings gains. While Nvidia and TSMC currently benefit, the expectation is for broader distribution of AI-driven revenue across various sectors. CEOs will face pressure to demonstrate the ROI of AI investments. The current focus on Large Language Models (LLMs) is described as a “parlor trick” compared to the potential unlocked by leveraging the 95% of data currently “behind the firewall.” IBM CEO Arvin Krishna estimates only 1% of the world’s data has been exposed to AI, representing a massive opportunity.

Newman emphasized the importance of data governance, compliance, and data sovereignty when applying AI to enterprise data. He personally demonstrated the accessibility of AI-assisted coding, building a CEO dashboard application despite lacking a computer science background, illustrating the democratization of AI development. This democratization will enable companies to rethink their go-to-market strategies and achieve more with existing resources.

The Rise of Enterprise AI – Industry Applications & Key Players

The discussion identified several key areas for enterprise AI adoption:

• Healthcare: A $1 billion partnership between Eli Lilly and Nvidia aims to accelerate drug development by democratizing access to supercomputing capabilities.
• Defense: Palantir is positioned to benefit from continued US investment in defense technology and unique insights.
• Software as a Service (SaaS): While predicting 50% of SaaS companies will fail by the end of the decade, Newman believes top players like ServiceNow and Salesforce will adapt by democratizing data utilization. ServiceNow is particularly well-suited to address complex enterprise workflows and data access challenges.
• Code Development: Tools like cloud code and rock code are disrupting the software development process.
• Manufacturing & Distribution: Amazon’s investment in robotics and autonomous vehicles will provide a significant competitive advantage.

Market Show and Tell: Support and Resistance with Intel

The segment explained the technical analysis concepts of support and resistance using Intel’s 10-year chart. Resistance levels (around $70 and $45) represent potential price ceilings where the stock has historically stalled. Support (previously at $45) represents a potential price floor. The failed breakout above $45 in late 2023 illustrates that these levels aren’t always absolute barriers. A new catalyst is needed for Intel to surpass the $70 resistance.

Intel’s Catalyst: US Government Support & Nvidia Partnership

Newman identified US government backing and a partnership with Nvidia as the primary catalysts for Intel’s potential resurgence. He framed the government support as a “made” situation, positioning Intel as the US champion in chip manufacturing. Nvidia’s backing, despite being a potential competitor, signals a need for increased manufacturing capacity and a recognition of Intel’s progress in foundry technology. Intel’s advancements in packaging technology are also crucial.

Trillion Dollar IPOs: OpenAI vs. SpaceX

The discussion centered on the potential for the first trillion-dollar IPOs from OpenAI and SpaceX.

• OpenAI: While experiencing rapid valuation growth, concerns were raised about its lack of a strong “moat” and potential competition from Google’s Gemini. Its financial obligations and potential legal challenges also pose risks.
• SpaceX: Considered a stronger candidate due to its unique capabilities in space technology, including data centers in space and connectivity solutions. Its backing from Google and the US government further strengthens its position.

Newman predicted SpaceX would ultimately be 1.5 to 2 times larger than OpenAI.

Google’s Resurgence & Apple’s Future

Google’s Gemini is gaining traction, leveraging its existing user base and infrastructure. Its ability to integrate AI into existing products (Search, Workspace, YouTube) and its vertical integration (TPUs, data centers) provide a significant advantage.

Apple is facing a need for a “regime change” in leadership to drive innovation. While Tim Cook has been a successful CEO, a return to a Steve Jobs-like visionary is needed to navigate the evolving technological landscape. Apple’s strength lies in its distribution network, but it needs to innovate to remain competitive.

Runway Battle: Amazon vs. Meta

The segment framed Amazon and Meta as competing for AI dominance, but with different approaches. Amazon is focused on building the infrastructure (AWS) to support AI, while Meta is focused on developing AI-powered products for consumers. Newman favored Meta due to its significant investments in human capital, its strong cash flow, and its potential to deliver impactful AI innovations. He highlighted Meta’s strategic hires and the expectation of breakthroughs from its internal AI development efforts.

Conclusion

The discussion painted a picture of a rapidly evolving AI landscape in 2026. The focus is shifting from basic AI demonstrations to practical enterprise applications that drive efficiency and revenue. Key players like Nvidia, TSMC, Google, Amazon, Meta, and Intel are positioning themselves to capitalize on this shift. The potential for trillion-dollar IPOs from OpenAI and SpaceX underscores the immense investor interest in AI, but also highlights the risks and uncertainties associated with these high-valuation companies. Successful companies will be those that can effectively leverage their data, build robust infrastructure, and deliver innovative AI solutions that address real-world business challenges.