Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy
[00:00:00] S0: Our guest today is Matthew Smith. He's been on the show before many years ago, and I always love talking to him about energy markets, where he's worked for 20 years. He's the founder and CIO of Chronometer Partners, which invests in energy, industrials, materials, power and utilities, and related infrastructure. He and his team have modeled nearly every natural gas well, pipeline, and processing asset in the United States. He's reached a conclusion that most of the market does not share. Starting in 2028, AI data centers and LNG exports will need more gas than the country can produce and deliver. By his math, the US could exhaust its working natural gas storage by 2030, which could lead to a true energy crisis. In his words, the upside risk to prices becomes unbounded and convex. We talk about why this was set in motion long before AI arrived, why the US can't just turn off exports, who wins and loses among producers, nuclear, solar, and hyperscalers, and what he sees as the only long-term solution. Please enjoy my great conversation with Matthew Smith. So, Matt, the last time we did this was, I think, during COVID. Kind of crazy that it's been six years. I've always loved talking to you about energy markets. You've been working in this space for 20 years. You're about as encyclopedic on this stuff as anyone I've ever met, but you've also been acutely studying the current energy situation in the US, rebuilding, in a way that you'll describe, from the well level up a picture of what's happening — especially as AI is creating all this new demand through data centers, et cetera — of what is going on over the last 18 months of concerted effort. You've reached a fascinating and somewhat scary conclusion. I'd love you to start with the conclusion, and then we're going to talk through how you came to this conclusion, who the winners might be, who the losers might be, and what's to be done about it. But before we get deep into all the component parts, just tell us what you found after 18 months of study.
· Matthew Smith, Patrick O'Shaughnessy
[00:00:00] S1: We are headed into a place where we see an historic deficit in natural gas supply available in the United States, which does portend some pretty serious consequences. Natural gas, which is over 40% of US power generation, is imminently going to become the most important fuel in the country. It's overtaking petroleum, given the amount that we use now for generation. Our work suggests that in '26 and '27, natural gas is appropriately supplied. But as we get into '28, and you plug in this compute, and you assign gas to very specific assets as they're plugged in as well, and you continue to export LNG, as we're planning to do with known projects, we start to eat into our working gas storage, which is the nexus of supply and demand in the country. I think we will come to the conclusion that the upside risk to the price of natural gas is both unbounded and convex. And so where you will feel it the most acutely will be electricity prices in '28, '29, and '30, based on our work. [00:00:00] S0: How much of this is just attributable to data centers? Purely, we're building a lot more data centers, that's just for AI. Is it that simple, or is there something else going on as well?
· Matthew Smith
[00:00:00] S1: The die was cast long before AI compute came to the scene. If I may set the stage a little bit, US gas was plentiful starting in about 2010, when shale started to really come to the scene and change things. We had been importing natural gas to satisfy consumption on top of what we produced domestically. Shale started to be very productive, surprised to the upside, and became this abundant force. And as natural gas became more abundant, we started to export it, starting with Cheniere. We've gone from that early Cheniere exporting to today, where we're exporting about 15 Bcf a day of nameplate US export capacity. Now that 15 billion cubic feet is on a base of about 110 to 112 Bcf a day of natural gas production in the US. So if you think about it, it's become about 12 to 15% of the US daily ability to supply the market that we're exporting. As this abundance continued, more and more facilities and projects have been announced. So as of today, we're scheduled to export up to 35 Bcf a day by the end of 2030. And in that case, the die has been mostly cast. To build an LNG project, you need various approvals. They're project financed. You site and permit many years in advance. Most of these projects that get you from 15 or 16 Bcf/day today of nameplate to 35 are well on their way. And so that's the primary incremental demand driver in the country over the last 10 years, and will be at least for the next 5. We had moderate population growth during the tens and teens into the 2020s. We went through a period of stagnating electricity demand. You had energy efficiency and some other things driving down electricity demand. You had more demand for gas-driven generation, but it's really been in the recent past where compute has started to pull incrementally. But before that, you had LNG as the main driver of demand. Now let's put those together. I just shared that we're going to go from about 15 to 35 Bcf a day of incremental LNG exports. And after evaluating every producing gas well and the entire pipeline, processing, and gathering system, we have the capacity to add about 20 Bcf a day of gas production. Even without AI compute, we had sources and uses matched between our ability to deliver new natural gas from Appalachia, Haynesville, Permian, and that which is supposed to leave the door through LNG. Now enter AI compute.
· Matthew Smith
There are so many different power-generating ideas in order to power compute — time to power that folks talk about so much. It sort of goes from the large-scale, most efficient assets, which are GE Vernova combined cycle, all the way down through the distributed generation assets, which we'll call fuel cells. We will add Wärtsilä Oyj or Caterpillar solar turbines. There are various local field-level behind-the-meter assets. Those assets are also relevant. We have had to assign, with an outside partner, probabilities to all of this stuff. And so what we've gone about doing is we will start with our base case, which is, we'll call it P50. Everything with a probability of 50% or more — 50% being they have some approvals, they have usually a PPA, someone planning to buy power from them under contract. They usually have some sort of interconnection agreement, or they're in process with the interconnection agreement. Those are the assets we've taken seriously in our base case. And so we'll call that the P50 level. And when you do that, it's about 5 Bcf a day, we think, of very credible, incremental natural gas demand associated with mostly AI compute. Now importantly, there are multiples of what we are considering seriously in our base case that have been proposed that will consume natural gas. Every solution today — a 6-series Bloom Energy latest-gen fuel cell will take 150 million cubic feet a day of gas per gigawatt. The market has been assigning a high probability on them attaining 2 gigawatts a year of productivity, or of manufacturing capacity, and that's likely to ramp to 5 gigawatts. There isn't the gas for that unless you take it from something else.
· Patrick O'Shaughnessy, Matthew Smith
[00:00:00] S0: In the extreme case, how high does that number get? [00:00:00] S1: If you start to move it down to say P30 or P0, that number can more than double and be 12 to 15 Bcf a day by the early 2030s if unmitigated. [00:00:00] S0: One naive way to approach this is to say, this doesn't sound like that big of a deal. Twelve new in the extreme case, just shut off the exports — who cares? We didn't export natural gas for a long time. People domestically are not going to tolerate skyrocketing energy prices, especially when they think the simple solution to this is just to stop shipping it out of the country and use it for ourselves. Why is the solution not just to shut off exports? [00:00:00] S1: It's more complicated than that. Contract law. There are rules. There are really good reasons why we're exporting, and there's tens of billions of project financing and contracts attached to or associated with these LNG projects. And as the US will be about a third of global gas supply in several years, our allies and other FTA and increasingly non-FTA countries are reliant on US gas. [00:00:00] S0: What's FTA? [00:00:00] S1: Free Trade Agreement. [00:00:00] S0: So the answer is that it's both — because it's a third of the global supply, that's really important for the rest of the world. And domestically, there's just contracts and investments, and it could be stopped, but it'd be very complicated.
· Patrick O'Shaughnessy, Matthew Smith
[00:00:00] S0: So the answer is that it's both — because it's a third of the global supply, that's really important for the rest of the world. And domestically, there's just contracts and investments, and it could be stopped, but it'd be very complicated. [00:00:00] S1: Yeah, let's step back for a moment. You have to have a starting place for a base case, which typically starts with signed contracts — what do the words on the page say? What's allowed? What's not allowed? When we set out to build the firm, we've had about 16+ months to start to model almost every asset at the atomic level. Along the way, there are numerous constraints. And rules and regulations and contracts. When we set out to build this, it was about acknowledging those constraints for what they are, assuming that contract law would be followed. And then as we go through and build all of this, we can flex up and down based on the choice to send less LNG out of our terminals, for instance, or slow AI compute growth, which is one other solution we're not really willing to propose because we know that there's insatiable demand. It's not popular to say slow AI compute growth. But to the extent that would happen, that would be another lever to reduce the pull or strain we expect in the system as the decade goes along.