The first month of 2026 has already presented a “tale of two extremes” for the US AI industry.

At the CES show in Las Vegas, “Physical AI” moved from concept to reality, with robots and autonomous vehicles taking center stage. In Silicon Valley offices, OpenAI scrambled to fill a tens-of-billions funding gap, targeting a valuation of $830 billion. Between Capitol Hill and state legislatures, regulatory battles extended from industry into education, as Ohio became the first to impose AI “restraints” on K-12 schools.

From a third-party perspective, the underlying logic of this storm is clear: AI is shifting from a “virtual technology race” to “real-world deployment.” A triangular dynamic of capital frenzy, technological breakthroughs, and tightening regulations will define the industry’s core trajectory in 2026.

I. The Technological Inflection Point: Physical AI Erupts, AI Steps Out of the Screen into Reality

The biggest change at CES 2026 was the retreat of “parameter comparisons” and the arrival of “deployment capability.” NVIDIA CEO Jensen Huang’s keynote, mentioning “Physical AI” 17 times, signaled a new industry consensus: endowing AI with the ability to “see, think, and act” in the physical world has become a shared goal for giants and startups alike.

1. Consumer Side: Robots Become the “New Darling,” Precisely Solving Scenario Needs

At this year’s CES, robots were no longer “vases that can just move” but “scenario experts” capable of stably executing complex tasks. Yingzhi’s XBOT coffee robot crafts thousands of latte art cups daily, Dreame’s “Sweeping Robotic Arm” accurately sorts household clutter, and Hisense’s companion robot can both patrol for alerts and entertain pets. The common thread: abandoning flashy gimmicks to focus on concrete scenarios like food service, home care, and security, prioritizing long-term, stable operation.

The trend of edge AI collaborating with the cloud also became more pronounced. Terminal chips from Qualcomm, Intel, and others support AI models running locally offline, meeting real-time and privacy needs, while the cloud focuses on model training and cross-device coordination, forming a “complementary edge-cloud” architecture. This division of labor significantly enhances AI device experience—smart glasses can translate offline in real-time, and in-car AI navigates precisely even without a network.

2. Industrial Side: Google’s Full-Stack Loop Pressures, OpenAI’s Tech Edge Shrinks

At the foundational tech layer, Google is reshaping the landscape with its “chip-model-application” full-stack advantage. Its sixth-generation TPU chip boasts a single-chip peak performance of 4,614 TFLOPs, offering inference cost-performance several times that of NVIDIA GPUs. With 2026 production capacity projected at 4.3 million units and orders from giants like Anthropic and Meta, it directly challenges NVIDIA’s monopoly. The Gemini 3 Pro model, powered by TPUs, comprehensively outperformed OpenAI’s GPT-5.1 in core benchmarks like multimodal processing and ultra-long-context reasoning, forcing OpenAI to hastily release GPT-5.2 in response.

More critically, Google has deeply integrated Gemini into its Search, Workspace, Android ecosystem, reaching over 2 billion global users, creating an ecosystem moat difficult to breach. In contrast, OpenAI still relies on third-party platforms for user acquisition, and its compute costs are constrained by NVIDIA chips, with dual technological and commercial pressures becoming increasingly apparent.

II. Capital Frenzy and Crisis: OpenAI’s Trillion-Dollar Funding Gamble on the Future, Burning Cash at a Dizzying Pace

Behind the technology race lies astronomical capital consumption. In 2026, OpenAI’s “cash burn rate” shocked the industry, igniting a funding battle for survival.

According to leaked financial data, OpenAI is projected to burn through $17 billion in cash in 2026, a staggering 89% increase from $9 billion in 2025. To support future compute expansion, the company plans to add 30 gigawatts of compute capacity, corresponding to a cost as high as $1.4 trillion. To fill this funding black hole, OpenAI is advancing a new round of hundred-billion-dollar financing, targeting a valuation of $830 billion, with giants like Amazon and NVIDIA on the potential investor list—NVIDIA’s investment is reportedly contingent on the funds ultimately being used to purchase its GPUs.

While burning cash wildly, OpenAI is also desperately seeking monetization paths. Having once rejected ads, it now plans to integrate advertising into ChatGPT in 2026 and transform the chatbox into an e-commerce portal through partnerships with Etsy and Walmart. On the enterprise side, it launched the AgentKit toolkit, attempting to snatch sticky clients from Anthropic. However, industry skepticism abounds: the performance gap between top models is narrowing rapidly. When the technological edge fades, can OpenAI’s commercialization efforts sustain a trillion-dollar valuation?

Notably, capital attitudes toward the AI sector are beginning to diverge. While institutions like HSBC remain long-term bullish on mega-cap tech stocks, they also warn that “chip capacity and power supply will become industry bottlenecks.” Companies like CoreWeave, focused on AI compute, have seen downgrades, reflecting market concerns about an AI bubble.

III. Regulatory Battles Expand: From Federal to State, Education Becomes a New Front

The federal regulatory easing under the Trump administration in late 2025 did not end AI regulatory battles but instead spread the conflict to more domains. In early 2026, the tug-of-war between federal and state regulators intensified, with education emerging as a new focal point.

1. Federal Level: Suppressing State Rules, Guarding Against “Woke AI”

The executive order signed by the Trump administration in December is being implemented: the Justice Department established a task force specifically to challenge local AI regulations in states like California and Colorado; the Commerce Department uses “alignment of state rules with federal policy” as an evaluation criterion for broadband funding distribution, indirectly suppressing state regulatory autonomy. Policy documents specifically note that attempts by some states to embed “Diversity, Equity, and Inclusion” ideology into AI models may violate federal laws, leading to “Woke AI” distorting factual output.

While this framework preserves space for state legislation in areas like data centers and government procurement, its core is to strengthen federal control, making future legal clashes with states like California inevitable.

2. State-Level Breakthrough: Ohio Leads with Legislation, Mandating AI Rules for K-12 Schools

Beyond the federal level, states are taking the initiative in specific areas. Ohio became the first state to mandate that K-12 public schools establish AI usage policies, requiring all school districts to implement comprehensive regulations covering privacy, ethics, and academic integrity by July 1, 2026. State education officials stated plainly: “AI is no longer a concept; it’s a reality students will face in their future workplaces. We must establish guardrails in advance.”

Ohio’s move triggered a chain reaction: Alabama requires “AI-generated content must undergo human verification,” Georgia introduced a “traffic light” system clarifying AI use boundaries, and 26 states nationwide have issued relevant educational guidance policies. The refinement of regulation in education marks AI governance moving from the “industrial level” deeper into the “societal level.”

IV. Third-Party Perspective: 3 Core Assessments for the 2026 AI Industry

The dynamics of the opening month have already outlined a clear contour for the US AI industry in 2026, with three core assessments taking shape:

1. Technology Deployment Enters a “Physical” Phase: AI’s value will no longer depend on lab performance but on its deployment capability in the physical world. Robotics, autonomous driving, industrial control, and similar fields will become core battlegrounds for giants, with “edge-cloud synergy” and “hardware-software closed loops” becoming essential capabilities.
2. Capital Bubble and Rationality Coexist: Funding scales for leading companies will continue to expand, but capital will prioritize commercialization capability over mere technological concepts. Small and medium-sized startups unable to find a deployment path in niche scenarios will face funding shortfalls.
3. Regulation Presents a “Layered” Landscape: The federal level focuses on “unified standards and antitrust,” while the state level refines rules in specific domains like education and privacy. Compliance capability will become a core competitive edge for companies, especially in scenarios involving public services and youth.

Conclusion: The Elimination Race Accelerates – Who Will Have the Last Laugh?

The US AI industry in 2026 has moved from “wild growth” into the “refined competition” stage of an elimination race. Google’s full-stack advantage, OpenAI’s funding gamble, Meta’s open-source strategy, NVIDIA’s chip dominance, and state-level regulatory innovations are all reshaping the industry landscape.

In the coming year, can Physical AI achieve scaled deployment? Can OpenAI’s trillion-dollar valuation hold? Where will the federal-state regulatory tug-of-war lead? Follow me for continuous tracking of every critical turning point in the AI industry!