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At Christie’s Art Tech Summit 2026, the State of A.I., New Wealth and the Art Market’s Next Chapter

From Benedict Evans on the economics of generative A.I. to Max Carter on SpaceX billionaires buying art in anticipation of liquidity, the day's conversations mapped a market at the intersection of extraordinary new wealth and enduring human taste.

The annual Christie's Art Tech Summit has always taken on the year’s hottest topic. In 2026, the event could hardly avoid the elephant in the room that is artificial intelligence. A.I. was not only the opening theme but the focus of the entire morning, with tech founders, analysts and researchers offering both theoretical and critical perspectives on the increasingly machine-driven reality we are beginning to inhabit.

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This year’s speaker list did not necessarily feature the most innovative voices working at the intersection of art and technology—figures Christie’s could presumably have accessed fairly easily, including artists such as Trevor Paglen, Zero10 curator Eli Schein, Toledo Museum of Art director Adam Levine or the sundry innovators we have covered this week. Instead, the program leaned heavily toward the U.S. technology and investment worlds, with speakers including the “Einstein of Wall Street,” Peter Tuchman; Rich Barton, co-founder of Zillow and Glassdoor; and Robinhood co-founder Baiju Bhatt. Art Tech is now organized through Christie’s Ventures and Innovation Labs, which invests in companies working across digital assets, A.I., fintech and hardware. The summit is, first and foremost, a business-development opportunity—a way for Christie’s to build bridges across art, technology and investment.

From 10 a.m. to noon, the discussion moved rapidly from Benedict Evans’s excursus into the evolution of A.I. technology and, more importantly, the relationship between capital and innovation—and therefore its market—to the more philosophical question of whether A.I. is thinking and what still makes human thought and creativity unique. From there came the inevitable questions about its disruptive power in the job market, but also about the opportunities for knowledge sharing. Described in the panel simply as an “analyst,” Evans is one of the most influential independent technology thinkers writing today, particularly on the long-term impact of A.I., mobile computing and digital platforms, known for his ability to synthesize technological trends into clear strategic frameworks for investors, executives and policymakers.

He bluntly framed generative A.I. as just the latest tech in a succession of computing-platform shifts that began with mainframes and moved through PCs, the web and smartphones—a line of reasoning other speakers would return to throughout the morning session. Each technology initially appears limited, then becomes the center of excitement, hence investment and company creation, before settling into the background as mature infrastructure. Generative A.I., he argued, is still at the start of that cycle: “Each technological wave arrives faster because it builds on the infrastructure of the previous one… ChatGPT launched into a world with billions of smartphones. But it still takes much longer to change organizational structures, processes, deployment models and consumer habits.”

For technology companies, it is better to invest too early than arrive too late. That explains why Google, Meta, Microsoft and Amazon view overinvestment as less dangerous than missing the transition entirely. Evans divided the resulting platform war into four areas: chips, data-center capacity and power, model development and deployment—who uses A.I., for what, at what price and with what economic return.

The most visible result is an extraordinary increase in capital expenditure on building this new technological infrastructure. The largest cloud companies, which until recently spent roughly $100 billion a year, are now guiding toward several times that amount. Evans estimated that annual A.I.-related infrastructure spending could approach $800-900 billion.

The scale is not unprecedented compared with global oil, gas or telecom investment, but it is unprecedented for the technology industry, he noted. A large share of that spending is flowing into Nvidia and the broader semiconductor industry, as chips, data centers and power are seen as the necessary elements needed to build all the capacity the industry is aiming for. OpenAI has announced enormous infrastructure ambitions without yet having the cash flow required to finance them directly, Evans argued. Much of the proposed spending, therefore, depends on partners’ balance sheets and on forms of vendor financing, more politely described as circular revenue.

Hence, the real question is who will capture the value and where the profits will accumulate. Sam Altman, Evans said, has compared intelligence to water or electricity, but utilities are not known for high margins. His more cautionary analogy was mobile telecommunications: smartphone adoption and mobile data usage transformed the global economy, yet telecom stocks largely stagnated because much of the value was captured by services operating above the network—Amazon, social platforms, e-commerce companies and other digital businesses.

OpenAI and Anthropic may survive as global-scale infrastructure providers, much like utilities, Evans argued, but the uncertain challenge is whether they can also own the emotional and experiential relationship with consumers. This was a point that Abhay Parasnis of Typeface also raised in the subsequent panel on Funding Innovation with Kevin Hartz of A*. Both also emphasized that standard model capabilities may become commodities, leaving differentiated experiences, trusted services and specialized knowledge as the real sources of value.

Evans also pointed out that chat itself is still much too poor an interface: users often do not know what to ask, and complex workflows are difficult to describe. The technology, therefore, needs to be embedded in specialized software, applications and tools. All the speakers agreed that the user experience is not yet there to make the potential of those tools truly accessible at scale. In the closing panel, Replit’s Business Development VP Jeff Burke also predicted that by 2030, A.I. would move beyond the prompt box and keyboard into more intuitive, personalized interfaces. Emerging formats could include voice systems, spatial A.I. and smart glasses, with one company likely to develop a breakthrough experience within the next several years. As computing, code generation and customization improve, those capabilities should become available across multiple devices and forms of interaction.

Improving user experience will help broaden adoption, which Evans pointed out remains limited rather than something used daily by most people. Enterprise adoption follows a similar pattern. The easiest applications—analytics,........

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