The 2010s oil bust could tell us AI's future

• Of course, none of them are really perfect comparisons, but that doesn't stop people from trying.

Case in point: In recent essays, two industry observers — Carlyle analyst Jeff Currie and Henry Gladwyn of early stage tech investor OMERS Ventures — sought to compare what's happening in AI now and what happened in oil exploration 10-15 years ago.

The big picture: In the mid-2010s, the shale boom — named for the supplies trapped in rock formations unlocked by fracking — turned the U.S. into the world's largest oil and gas producer, adding new geopolitical leverage along the way.

• But it was a painful road for industry and investors because they invested heavily on growth — and got hammered when Saudi-led OPEC looked to reclaim market share in late 2014 and prices collapsed.
• Some think there's a parallel, and that AI could be following the same path.
• The connective tissue between AI and energy isn't just metaphorical: Natural gas is emerging as a winner, slated to supply at least a big tranche of the additional power needed for huge AI data centers.

Zoom in: In Currie's latest piece, he explores the what happens when companies built on ideas suddenly have lots of assets, like data centers and power plants. Instead of being "asset light," they're "investing like old economy energy."

• To his way of thinking, there's a historic back-and-forth between what's more important: the bits (think: computing) and the atoms (think: molecules of energy).
• "[T]he 2020s are shaping up to be a decade where the bits converge with the atoms, creating new 'bit-atom' commodities like cryptocurrencies and AI compute," he writes.

How it works: The output of data centers is so power-intensive that their computing is measured in dollars per hour, the same way energy is priced by the megawatt-hour or barrel. That carries risk.

• "Big Tech AI is now producing a physical commodity with a supply and demand balance just like an energy company," Currie notes, drawing the comparison to the shale oil bust of a decade ago.
• "If, analogously, we replace low-cost Saudi Arabian oil supplies with low-cost Chinese AI compute technologies combined with cheaper foreign providers then the narrative could look eerily similar."

Of note: Gladwyn's AI-shale comparison also makes the China-as-AI's-OPEC analogy.

• His piece explores how the U.S. and allies can succeed, and which industry players will thrive or suffer inside this western "technodollar envelope."
• "Washington cares most about security and scale. Europe and Canada insist on carbon intensity and climate alignment. Japan and Korea emphasize supply chain resilience and domestic champions," writes Gladwyn, a managing partner at OMERS.
• "The technodollar's rules of entry are shaped by all these priorities. Security, procurement, and carbon standards are the glue that binds the Western bloc."

Reality check: Carlyle's Currie does see differences between then and now.

• One of them: "No one would ever have mentioned monopoly in the way that the term is thrown around today in the context of generative AI."
• The analogy has plenty of limits, like oil companies serving a market where demand is largely known and growing incrementally, not exponentially.

The bottom line: "The shale revolution did not fail. It succeeded so completely that it reshaped the global energy order," Gladwyn writes. "Yet many investors in shale were ruined."

• "The paradox was that shale's abundance triumphed geopolitically but starved capital of returns. AI is tracing the same arc of abundance."