Highlights from the listing: 70 million shares at $27, upsized from 55.5 million, popped 33% on the open. Third largest US energy IPO in 15 years, behind Kinder Morgan in 2011 and Plains GP in 2013, both midstream MLPs. The recent energy reference list is otherwise LNG infrastructure and state-owned privatizations. Fervo’s $10 billion debut as a venture-backed producer is more noteworthy in that company.

Some napkin math on the price tag. Mark the 500 MW under construction at $2 million per MW, roughly where CEG and VST trade. Mark 3 GW of advanced development at $0.5 to $1 million per MW. That covers $2.5 to $4 billion. The market ascribes the remaining $6 to $7.5 billion to 38 GW of early-stage portfolio, around $150 to $200 per kW.

E&Ps pay for undeveloped sticks. Why not for EGS sticks? Depends on whether they are scarce. For EGS, that is a question of land, technology diffusion, and substitution.

BLM geothermal bids are up tenfold since 2023, with top bids hitting $412 per acre in Nevada’s record 2025 sale. Fervo built its 595,900-acre position at roughly $4 per acre between 2019 and 2021. ORA and Buffalo River Minerals are the new public bidders. Our Geothermal Acreage piece ran the numbers.

Geothermal acreage well sited for the grid is a lot like tier one shale acreage. Early grabs are the advantage, if you got the right rock. More than a few E&Ps missed the core (anyone remember Talisman Energy?). Did Fervo get it right?

Technology diffusion will come. Fervo has an exploitation answer key that is working today, with a learning curve compressing LCOEs. That is an advantage for now. But the dependence on drilling and completion service providers means, like with shale, those advantages will not stay Fervo’s forever.

Power markets are not precious about generation. An electron is an electron. The substitute to watch is SMRs, not solar plus storage. X-energy raised $1 billion last month on the same thesis. Fervo has 658 MW of 15-year PPAs signed across SCE, Shell, Google, and two California CCAs. $7.2 billion of revenue backlog, implied near $93 per MWh. SMRs are still chasing site approvals. That is a wide gap.

Most booms echo the past. With EGS, I hear the echoes of shale: the land capture, the rapid improvements (and falling breakevens), and technology diffusion. If those echoes foreshadow another energy renaissance, last week’s IPO is its first signpost. Congratulations to the Fervo team.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!

Morning Energy is a syndicated note published through Enverus Intelligence. My contributions will also be distributed here. Please note that links frequently lead to content available only to subscribers of Enverus solutions. Please reach out if you have any questions. Thanks! - Ian.

Consider power and energy transition, where I spend most of my time. Project economics are now hyper-location specific. Load, land, interconnection, deliverability, fuel, policy, saturation, buyer behavior. Get the combination right and the asset prints. Miss one variable and the site goes nowhere.

The harder problem is the rate of change, and you would be forgiven if it leaves you feeling a little off balance. Environmental attributes are trading more like derivatives of the political environment than the output of supply and demand. IPP valuations have rerated in recent months on the appetite of hyperscalers to sign PPAs against a subset of their assets: see our take on NRG and CEG. Data centers are moving behind the meter, but for how long? The pieces are too interconnected, excuse the pun. Power markets now need scenario-based models that clear energy, capacity, attributes and project economics together.

Zoom out and the old pattern is visible. Value accrues to whatever is scarce, deliverable and contractable. Everything abundant gets discounted to the floor.

Which brings me to the AI sessions, and the perspective that reasoning itself is becoming abundant. Every company will soon have access to capable models trained on public data, which means the model will not be the moat. One speaker pushed it further: AI is an amplifier. It does not change what you are, it amplifies what you already are. Leaders pull further ahead, laggards fall further behind, the middle gets squeezed.

It sounds hyperbolic, but the undertone was there in the coffee chats and dinners. Folks are excited. But there was also quiet conviction that everybody is behind and the hard questions are just beginning. For some, enterprise restrictions limit which models get deployed. For others it was apprenticeship loss. How does a 25-year-old analyst get the reps to know what good looks like when the first draft is always waiting, or when the MD prompts the model instead of asking the analyst?

For everyone, whatever they are doing, it is not enough.

I feel it too. But that may be the point. If reasoning is abundant and AI is an amplifier, advantage moves underneath the model or above it. Underneath, sits proprietary data, controls and tech. Above, sits the judgment to make decisions before the model has the answer. Both are hard, but the kind of hard AI will reward.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!

Morning Energy is a syndicated note published through Enverus Intelligence. My contributions will also be distributed here. Please note that links frequently lead to content available only to subscribers of Enverus solutions. Please reach out if you have any questions. Thanks! - Ian.

I have been going down the rabbit hole of AI tools, fully convinced of their power and the probability they will be transformative for the world of work I engage in. On the surface it feels like software, where I engage with agents in natural language and the agents are the enabler of my work. This is incomplete. As I engage further with this technology I can feel my own role shifting, and with it, my relationship with AI.

A core element of my role is to harness my team, a mountain of data, and our capacity to produce ideas and insights and make them consumable and actionable for our clients. This is an intensely human experience. So many decisions are dictated by the comfort and conviction of human beings, based on the trust and credibility of other human beings. Decisions of significance invariably happen in person, where the whites of one’s eyes matter as much as the quality of the case being presented.

What changes as I and our team adopt AI is that my team now includes an army of agents and rapidly growing capacity to generate ideas and content. Set the question of quality aside for a moment. Extrapolate forward, where the models and agents become increasingly competent, and I can imagine the army of agents becoming the dominant source of ideas and insights. Those ideas need a curator and an evangelist in the rooms where they can influence real people making real decisions. I am still needed, if only as an interface for my AI.

That is an oversimplification, but carries several implications relevant now.

Implication 1: The bottleneck is my ability to consume ideas.

For most of my career the scarce resource was production. Finding the data, doing the analysis, writing it up cleanly. AI has flattened that curve. I dictated the first version of this post at about 150 words per minute, 4-5x faster than I could type it, and Wispr Flow ensured the first draft already absorbed my verbal corrections and removed the filler words. Claude turned that draft into something readable and helped refine it into something serviceable before my coffee got cold. It is not perfect, but then, this is also the worst the AI will ever be.

Consider what just happened. My bandwidth from mind to paper expanded with AI-assisted dictation. Those ideas were refined and expanded with at least the same acceleration versus what would have happened in the recent past. Combined, that is 20x the output bandwidth.

I am not alone. Every analyst, PM, and consultant with an API key has the same superpower. The result is my overflowing inbox. The problem is that my input bandwidth has not expanded at the same rate. My ability to read, absorb, and hold something in working memory long enough to do anything with it is similar to five years ago. Call it the read-write asymmetry, where output bandwidth is expanding rapidly and input bandwidth is comparatively fixed.

Implication 2: Choose errors of exclusion over errors of inclusion.

If the flow of inputs coming at me is growing faster than my consumption bandwidth, my instinct is to become stricter about what I consume. That means avoiding errors of inclusion (losing time on the weaker content) and accepting errors of exclusion (missing great pieces to preserve the quality of the corpus I do consume). In practice that is a short list of writers, analysts, and primary documents that consistently deliver more ideas per word than anyone else, and a much harder no on everything else.

But strict filters kill the accidental find. The piece I never would have searched for that turned out to reframe a problem or create a connection I had not considered. There is serendipity in foraging that I do not want to lose. For now I am keeping fifteen or twenty percent of my read budget in deliberately adjacent territory. That allocation still needs a plan.

Implication 3: The bar for output has moved up.

When the cost of producing okay content drops toward zero, the things that make output great are exactly the things AI does not do by default: falsifiable claims, non-obvious reframes, and specific numbers anchoring an abstract argument. These tools allow the author to commit to a point of view rather than hedge, and to make a call rather than balance the debate. When supported with great arguments and analysis this is what I want to consume and I expect others do too.

Implication 4: The real work becomes developing taste.

Taste used to be built by making things. Years of writing badly, getting feedback, trying again, watching what works in front of an audience and what does not. If most of the making is now done by a system, where does that judgment come from?

I think the answer has two parts that have to be held in tension.

The first part is that you still have to do some of the work. It is inefficient, and the throughput pressure of AI-assisted speed pushes against it constantly. But there is a kind of understanding that only comes from sitting with a hard problem long enough to feel its actual shape: build the model from raw data once in a while, write the hard paragraph by hand, and run the analysis without the agent. There are lessons for evaluating work that you only earn by producing it.

The second part takes advantage of the technology and runs more cycles than were previously possible. Each attempt is a chance to make a call, see the result, ask what worked and what fell flat, and update. That also can be how taste gets built. The volume is the new training data for my judgment, if I actually use it that way. The risky default mode of AI-assisted work is to ship faster. Hit go, the output is good enough, move on. That improves throughput. Combine cycle count with reflection, grounded in the wisdom of having done related work, and you get an accelerated apprenticeship.

Where does this leave me?

I can feel my role evolving. Knowing the data and the operators and the history, the input-side expertise that used to be most of the value, has not gone to zero. But that responsibility is shifting elsewhere. The job is to shape what the system, my team of people and agents, produces into something a person can use in the room where the decision gets made. The judgment is still mine. The compression is the product. The taste that drives both is the thing I have to keep working on, using the same tools that threaten to erode it.

It feels like I am becoming the UX for my AI. The question is what it takes to be a good one.

I show up to events like this with a few themes and questions in mind. Top of mind walking in is power as the forcing function this year. Last week the four big hyperscalers told the market they will spend $650 to $725 billion on AI infrastructure in 2026, roughly double 2025. Capex now runs at 47% of sales for MSFT and 54% for META. Wild changes from just a couple years ago. Unpacking what is required to generate returns on that spend is a discussion for another day (it is starting to feel very shale circa 2014 to me). For now, it is enough to know that any returns are gated by speed and access to power.

Constraints combined with capital lead to innovation. And the latest innovation is more like circumvention. Skip the grid. There are real reasons most loads choose to interconnect, but when local pushback grows, when the queue wears you down, when you are already self-insuring reliability beyond what the grid offers, and when power is a small (but still multi-billion) share of a $50 to $100 billion plus project, the calculus shifts.

The calculus of constraints is showing up elsewhere. Energy security is visible now in a way it has not been in years. Hormuz keeps oil bid every day it stays contested. The industry is openly discussing Big-E exploration, new unconventional plays beyond North America, infills, refracs and EOR. I did not hear a consensus favorite at our EnergyEdge event in February. But that was before the Iran conflict.

As for questions, I am most curious about the AI vibe check. I believe AI is here, the hype is justified, and the gap between lab bench and scaled monetization is where the real work is. But where is the room at? Who is bought in. Who is using it. Who is creating value with it. Enverus ONE is our answer. I will be watching everyone else’s.

If you are at Evolve this week let me know. I would love to say hi.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!

Morning Energy is a syndicated note I publish through Enverus Intelligence. My contributions will also be distributed here. Please note that links frequently lead to content available only to subscribers of Enverus solutions. Please reach out if you have any questions. Thanks! - Ian.

Gas generation in 2026 is having a Ferris kind of day. GE Vernova’s gas turbine order book crossed 100 gigawatts at the end of Q1 2026, up from 83 GW just one quarter earlier. Siemens is similarly stuffed. Hyperscalers are signing direct. EIR has grid-connected gas builds averaging 10 GW per year, double the prior two-year pace. Pricing on new turbine bids is up 10% to 20% above the 4Q’25 backlog average. The day is bright.

I also remember Cameron, in the garage, listening to the odometer click. Here too, there is tension building. A tension we described two weeks ago in The Sibling Squabble: grid-connected gas burn for power stays roughly flat through 2030 even as load grows 10% and capacity grows by tens of gigawatts. Solar, wind, and batteries conspire to compress gas utilization across more hours of the year. New capacity does not translate into meaningfully more energy delivered.

The note left a few nuances understated. The demand is real, but bottlenecked. Heavy-duty turbine slots are sold into 2029. The chip supply chain (EUV throughput, memory, global allocation) is growing but still gates how fast data centers can consume the power gas is meant to serve. Supply chain is the binding constraint.

Against that constraint, gas demand can grow anyway. An increasing share of incremental data center load is being met by dedicated behind-the-meter gas generation and distributed generation hardware (aeros, recips, fuel cells) with more slack in their queue. Those molecules (around 1.8 Bcf/d by EIR’s count) show up in the gas balance, just not in grid-connected power burn.

But there are more miles still to reverse off the odometer. Coal retirements keep slipping, almost every quarter, deferring the substitution trade gas has been waiting on for a decade. SMRs and geothermal are no longer slideware. GEV expects an NRC license to construct at Clinch River as soon as 2H’26. Fervo just filed its S-1. Both arrive in the early 2030s on cost curves still grinding down, ready to compete for capacity and dispatch in that decade.

The movie ends differently for each of the players. IPPs do not want load moving off grid. Gas producers do not want more capacity that runs less often. Turbine OEMs do not want optionality on the order book to evaporate before it converts. Each of them gets a Ferris headline today and a Cameron question on the drive home.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!

Morning Energy is a syndicated note I publish through Enverus Intelligence. My contributions will also be distributed here. Please note that links frequently lead to content available only to subscribers of Enverus solutions. Please reach out if you have any questions. Thanks! - Ian.

For most of the industry’s history, load was the variable you forecast and generation was the variable you dispatched. Dial the turbines up or down to match the demand, and job done. Ok, it’s a bit harder than that… but you get the idea. Our weekly ISO lookback showed load forecasts were solid across PJM, MISO, and NYISO and beat ISO metrics in ERCOT, CAISO, and SPP. Last week load was defeated, and prices still surprised. Vanquish one challenge, two new ones emerge.

It’s shoulder season so those surprises are revealing. Spring generation in the Lower 48 runs roughly 20% below summer peak. Temperatures are mild, cooling load is minimal, and the grid should be the furthest thing from strained. It is also exactly when generators schedule maintenance, because the system has slack to absorb it. And yet, strip load out as the hard variable, and the forecasting problem does not get easier.

The modern supply curve now moves on its own clock, and the moves are large. ERCOT wind swung from roughly 12 GW at the weekly trough to nearly 28 GW at peak, often in a few hours, sometimes early, and sometimes late. SPP looked quiet all week and produced a single Friday spike when wind shape collided with a binding constraint. This week, NYISO wind is expected to drop nearly 90% from early week to Thursday or Friday. Plenty of capacity to surprise.

Then there is the head that does the most damage. Generation has not just changed type, it has changed location. Wind sits in West Texas and the Plains, solar in the desert Southwest, hydro upstream of population. The wires were built for a fleet that sat closer to load. Supply that looks abundant on a system map is frequently stranded on the way to where it is needed.

Place this against a backdrop where load is rising at the same time: data centers, electrification, and industrial reshoring are pushing the baseline higher even as the supply stack gets lumpier. When weather or congestion pulls renewables off the curve, the market climbs up the merit order, and the top of the stack is steep. A few MW of missed supply or unexpected demand can clear hundreds of dollars per MWh above where the average day settles.

This is today’s market, one that pays for reading between the lines a model cannot absorb. I am glad we have a team doing that work every day.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!

Morning Energy is a syndicated note I publish through Enverus Intelligence. My contributions will also be distributed here. Please note that links frequently lead to content available only to subscribers of Enverus solutions. Please reach out if you have any questions. Thanks! - Ian.

The oil and gas side sees a compelling bull case. Load growth is real (EIR forecasts energy consumption up 10.2% by 2030). Data centers are driving it (30 GW of new capacity over five years by our count, the ISOs think it is closer to 50 GW). Those data centers overwhelmingly prefer gas. GEV and Siemens are reporting record order books for gas turbine technology. EIR’s own capacity expansion model has gird connected new gas builds averaging 10 GW per year (double the last 2 year average).

Data centers add a second layer of conviction. EIR estimates 15 GW of behind the meter facilities with dedicated gas generation, adding 2.1 Bcf/d. LBRT and SEI are building real businesses powering them. If the fastest growing load class in America is tying itself to gas, surely gas burn is headed higher.

The power side is less convinced. In ERCOT, batteries already set the clearing price 23% of the time, commanding a $21/MWh flexibility premium over combined-cycle gas. Solar and storage are not a future scenario in Texas. They are setting the marginal price today. And EIR’s L48 gas balances reflect the implication: power generation demand sits at 35.5 Bcf/d in 2026 and 35.0 Bcf/d in 2030. Flat. Load grows 10%. Gas capacity grows by tens of gigawatts. Gas burn for power goes nowhere.

The explanation is straightforward. Data centers secure gas because they need firm, dispatchable power. But solar paired with batteries is entering the rest of the stack at zero marginal cost. Demand that was previously met by gas shifts to a grid increasingly served by renewables during daylight and batteries into the evening. Gas does not disappear. It gets pushed to the peaks and the margins.

The oil and gas sibling looks at turbine orders and data center gas contracts and sees a growth story. The power sibling looks at dispatch stacks, interconnection queues and battery economics and sees a utilization story. They are both looking at real data. We just think the power guys are right.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!

Morning Energy is a syndicated note I publish through Enverus Intelligence. My contributions will also be distributed here. Please note that links frequently lead to content available only to subscribers of Enverus solutions. Please reach out if you have any questions. Thanks! - Ian.

In Jiro Ono’s kitchen, an apprentice spends years perfecting rice before touching fish. Tamago, the simple egg, takes 200 batches to master. The repetition is not mechanical. It is refinement. The Japanese call this shokunin: the craftsman’s ethic of doing the same thing over and over, improving bit by bit. Today is edition 1,000 of Morning Energy, and like Jiro’s Tamago, we continue to chase perfection.

The first edition hit inboxes on January 4, 2017. It was a stream of short descriptive paragraphs about GOM layered maps and MLP consolidation. WTI was $53. Henry Hub was $3.30/MMBtu. U.S. oil production was 8.9 MMbbl/d, still nursing wounds from the 2015 to 2016 bust. Early Morning Energy was functional, not yet refined.

Three years later U.S. production touched 13 MMbbl/d, ahead of Russia and Saudi Arabia. And like the industry it narrates, Morning Energy evolved too. Its voice became clearer, more personal, more opinionated.

COVID brought new changes. Negative oil prices, but also a surge in momentum for low-carbon energy and, ultimately, the passing of the IRA. Power markets, which rarely made headlines in 2017, became the story after Winter Storm Uri put ERCOT on the front page. And Morning Energy moved beyond oil, gas and shale.

Today, AI is bringing new changes. Hyperscalar land positions are the new inventory, electrification is the new emerging economy, and silicon production is the new rig count. Gas, once a target for phase out, is now the essential fuel of the AI economy.

Morning Energy started as one analyst’s notes. It is now written by a team, but the ethic hasn’t changed. The Japanese have another tradition: fold 1,000 paper cranes by hand and your wish is granted. The wish? Get it right more often than not and make sure it’s worth reading.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!

Last week was Spring Break, which means extra time with the kids and a chance to see the world again with their innocence and curiosity. Our girls have more toys than they need, and the pattern is always the same. Once the excitement of receiving fades (a few minutes), they set it aside and spend the next three hours playing with the cardboard box it came in. Eventually you wonder: can I return the toy and just keep the box?

TotalEnergies just pulled that off. On March 23, Total and Interior signed settlement agreements to unwind the company’s New York Bight and Carolina Long Bay offshore wind leases. Interior will reimburse $795 million for OCS-A 0538 and $133 million for OCS-A 0545, roughly $928 million, after Total reinvests matching capital into Rio Grande LNG Trains 1 through 4 and upstream oil and gas. Total also pledged not to pursue any new U.S. offshore wind. The returned toy: about 4 GW of prospective capacity, roughly 13% of the Biden-era 30 GW target.

The box, of course, was already in hand. Total took FID on Rio Grande Train 4 in September 2025. Trains 1 through 3 were under construction. The deal reimburses Total for capital it was deploying anyway, with the bonus of exiting leases whose economics had soured. Pouyanné called it “a more efficient use of capital.” Hard to argue when the capital was already being spent.

But the language is what lingers. TotalEnergies’s press release quoted Pouyanné stating the company made this deal “considering that the development of offshore wind projects is not in the country’s interest” and that its own “studies on these leases have shown that offshore wind developments in the United States, unlike those in Europe, are costly.” But given the open opposition to offshore wind from the federal government and invocation of “national interest” in the release, this reads as much as a loyalty oath as is does a write-down announcement.

The real question is who lines up next at the returns counter. EDPR, RWE and BP had already halted or exited U.S. wind before this deal landed, and we don’t include much offshore wind in our capacity forecasts. Now that Total has established how to get a refund, it could get busy at the service counter.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!

When the Boogeyman goes to sleep every night, he checks his closet for Chuck Norris (RIP). That meme was always one of my favorites and it is a pretty good way to think about U.S. power markets this spring.

The obvious villains are still heat and gas, but our 90-day power price outlooks suggest the market is increasingly jumpy about the less obvious: which transmission element is out, how quickly Western snowmelt borrows supply from later in the season, and where the stack gets steep (where the grid moves out of the cheap, efficient part of the generation stack and into the more expensive units).

In ERCOT, it’s already been hot (Dallas temperatures reached the mid-90s in March). Projected June power prices sit near $40/MWh, which is exactly where the grid switches from running efficient gas plants to firing up older, expensive ones. Think of it as the edge of a cliff. A few degrees on the wrong day will do it and our bullish case is ~$10/MWh higher.

In PJM, the eastern epicenter of the U.S. data center buildout, the summer risk is more about transmission line outages than new demand (the market recently saw a near$3,000/MWh spike, and not from a cold snap or heat wave event). Case in point: transmission outages near Bedington, West Virginia are the primary price driver in the outlook. Data center load is coming, but this spring’s prices will be shaped more by which lines are down and when, than how many servers are plugged in.

Out west, more water (hydro) does not automatically mean lower prices. Warm weather is melting Pacific Northwest snowpack early, flooding the grid with cheap hydro in April. But that pulls the water forward, and May and June could surprise higher as reservoirs refill. In Southern California, batteries soaking up cheap midday solar inflate demand numbers without tightening the grid during peak evening hours. Load is growing, just not in the hours that matter for pricing.

The Boogeyman this spring is not weather or gas. It is a transmission outage in West Virginia, snowmelt timing in the Cascades, and the part of the ERCOT stack where prices stop being polite.

Coming up: The team is doing a full Winter Lookback on March 26, covering where forecasts tracked vs. diverged across ERCOT, PJM, NYISO, and ISO-NE. Register here.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!

That is not the consensus view. The tasks most exposed to AI adoption overlap heavily with entry-level work, and the data is starting to reflect it. A Stanford study published last month, using ADP payroll records covering millions of workers, found that employment for 22 to 25 year olds in the most AI-exposed occupations fell roughly 16% relative to less exposed roles, controlling for firm-level shocks. Employment for experienced workers in those same occupations? Stable or growing. Firms are not cutting salaries. They are simply not hiring the next generation.

The logic follows naturally. Junior employees are disproportionately assigned the codifiable, checkable tasks that LLMs handle well. A recent HBR piece by Edmondson and Chamorro-Premuzic made the same observation: AI is eroding the bottom rungs of career ladders by automating the intellectually routine work that entry-level roles were built around.

But the logic is also wrong. And it is wrong because it inverts the problem.

I have spent the past several months building AI into my own workflow. Claude Code connected to a live data warehouse. Autonomous agents handling tasks that used to take a junior analyst half a day. I have watched the pace of innovation inside our teams accelerate to a speed we once could only dream of. The productivity gains are real. And they have convinced me that the unintentional outcome of AI adoption, a shrinking base and a protected top, has it exactly backwards.

AI does not replace judgment, creativity, or the instinct to try something no one has tried before. It replaces routine. Routine may not be what young people are best at, but routine also builds taste, wisdom, and intuition. It is the unglamorous work that turns potential into expertise. The answer is not to eliminate it. It is to pair it with tools that compress the learning curve and free up time for the work that actually develops judgment.

Young workers on our teams are the ones bringing AI fluency, fresh pattern recognition, and the willingness to break workflows designed for a pre-LLM world. They are the ones who will figure out how to deploy these tools at scale. They are the ones stress-testing what is possible. You need a critical mass of them, and you need a steady inflow, or you risk losing the energy, the ideas, and the perspective that only comes from people who have never known a world without these tools.

These dynamics make exceptional leadership the scarce resource: experienced leaders who have done the reps, who can get their hands dirty, who can convey the hard-won wisdom of that experience, but who also know how to let loose the talent and energy of the team around them. Think of it like driving a dog sled. You need to know the trail. You need to read the conditions. But the team is doing the running, and your job is to point them in the right direction and not hold them back.

Left unchecked, the unintended consequence emerges. Experienced workers use AI to absorb the tasks that would otherwise have gone to junior colleagues. That reduces direct demand for those roles, which leads to less hiring at that level. Nobody decided to hollow out the pipeline. It just happens, one automated task at a time. Follow that drift forward five years and you have plenty of managers and nobody coming up behind them.

The question is not whether AI changes hiring. It is whether we are smart enough to automate the right things and invest in the right people.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!

Pain is temporary. Glory lasts forever. That was the locker room wisdom in The Replacements. It might also be the lesson of the Strait of Hormuz.

Not long ago, glory was supposed to come from the energy transition toolkit: emissions policy, renewables subsidies, carbon taxes, all designed to push consumption away from oil and toward low carbon substitutes. That era already feels distant, replaced by a focus on energy security, independence and affordability. Iran’s closure of Hormuz puts a finer point on the argument.

With higher oil and LNG prices, a familiar set of incentives has returned, just wearing different jerseys. Consider this lens, one I first encountered from our friends at the ARC Energy Institute: translate the change in commodity prices into an equivalent carbon tax. By our math, a $10/bbl increase in oil implies roughly $23/t CO2. A $1/MMBtu move in natural gas translates to about $19/t. Global oil and European gas prices have moved by ~$35/bbl and ~$6/MMBtu respectively, implying carbon taxes of ~$80/t and ~$113/t. Call it a “conflict carbon tax,” though one that is far from equitable across sources and locations (Henry Hub has been quiet in comparison).

Incentives work. Reprice oil and natural gas higher and substitutes become attractive, especially those that offer security, independence and affordability. So who are the replacements, and will they bring glory?

For oil, electrification and EVs find a new tailwind. For gas, especially in markets tied to LNG, the answer is predictable: coal. The grizzled veteran is unglamorous, dependable, and suddenly more competitive with fuels meant to replace it. Renewables offer independence where grids can absorb them. Geothermal is coming in hot. Nuclear is energized, but the mood feels more like regret for what has been lost than acceleration of what comes next.

But the Strait of Hormuz has reminded everyone that concentrated supply chains are not strategy. They are vulnerability. And that applies as much to transformers (Korea and Japan), chips (Taiwan), and critical minerals (China) as it does to crude. In energy, the winners are rarely the cleanest story on paper. They are the assets that can actually take the field when the starter goes down.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!

The action in the Middle East has the world focused on energy supply. And for good reason. Hydrocarbon based energy price prices are racing higher. Crude prices are up 50% (hello $100 oil, we missed you) and LNG is up more than 80% (losing 10 Bcf/d from Qatar will do that). Even coal is up nearly 20% . US power, in contrast, looks like a model of tranquility. Henry Hub has barely moved and the weather forecasts look benign.

But don’t let the calm deceive you.

Something is happening to U.S. electricity demand. The peaks are teasing it. The shoulders are hiding it. Welcome to power’s masquerade ball.

The EIA confirmed last week that U.S. electricity generation hit a record 4,430 TWh in 2025 (NB: the linked EIA report erroneously states 4.43 TWh), up 2.8% from 2024, which itself was a prior record. After two decades of essentially flat generation, the obvious explanation is AI: hyperscalers building at a feverish pace and a grid straining under the weight of the digital economy. Compelling. But not the whole story.

Look at the load shape rather than the annual total. Our analysis of power demand against heating and cooling degree days finds that seasonal peaks last summer came in above weather-normalized expectations, but shoulder-season demand has been running below trend for the past couple of months (Figure 3). Those two signals together tell the story.

Data centers and electrification were strong enough to push summer peaks above where weather alone would put them. But in the shoulders, where data center load should also show up given its flat average profile, the signal has vanished, masked by something bigger.

Friday’s jobs report may have spoiled the “Midnight Reveal”. Nonfarm payrolls fell by 92,000 in February, well below estimates, and the report shaved another 69,000 in downward revisions for January and December. A weakening economy may just have been the perfect mask for the emergent data center signal from the summer.

Emergent? Yes. Emergent. It is still early. Vistra’s CEO Jim Burke, whose company has contracted nearly 3.8 GW of nuclear capacity to hyperscalers and is actively building new gas capacity in West Texas, told investors last week that data centers will not meaningfully tighten supply and demand until late 2027 or early 2028. The (data center) guests are still arriving and when the economy finds its footing, the masks come off. That is when the power story will get interesting.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for reading!

Not long ago, battery storage in Texas was a license to print money. Park your asset, offer ancillary services, collect generous checks. Those days are over. ERCOT’s installed battery capacity has nearly doubled from 9.5 GW at the start of 2025 to over 16 GW today, and the market has noticed. Average battery revenues collapsed from $149/kW in 2023 to under $20/kW in 2025. That is not a typo.

The culprit? Saturation. Ancillary services used to be the golden goose, representing 84% of battery revenue in 2023. By 2025 that share dropped below 50%, and in dollar terms AS revenue cratered from roughly $125/kW to just $8/kW. Arbitrage didn’t pick up the slack either, falling from $24/kW to $9/kW. Tough neighborhood all around.

What surprised me was how frequently batteries are at the margin. Despite delivering less than 1% of total energy on the grid, batteries now set the system clearing price in 23% of all hours. When they do, prices average $56/MWh, a $21/MWh premium over gas-set hours. That is the market paying up for flexibility. And batteries are not just showing up during the predictable morning and evening peaks. They reliably appear at the margin around the clock, with operators like Key Capture Energy, Jupiter Power, and Plus Power quietly establishing price floors through opportunity-cost bidding even in the middle of the night.

What does this mean? Picking your node matters more than ever. Transmission congestion is creating massive gaps between system prices and local realities, with some legacy thermal plants are clearing above $500/MWh simply because they sit behind a bottleneck. Meanwhile, batteries are now like high heat rate gas units sitting at the top of the resource stack. With 765 kV transmission builds on the horizon, the takeaway is clear: the easy money in ERCOT batteries is long gone, but there is still plenty of hard money to be made.

Thoughts, questions or things we missed? Send me a note (or hit reply) - I would love to hear from you.

The clean fuels story of 2021 to 2025 was “build it and they will come.” From here, it’s “build it only if the math works.” Our newly released Clean Fuel Fundamentals report, “Discipline Over Hype,” captures a clear inflection point in capital discipline. Growth expectations have reset hard, from a ~9% CAGR over the prior five years to a projected ~3% through 2030 (slide 6). The investable edge has shifted from who can announce the most projects to who can navigate policy and credit regimes the fastest.

Three policy moves reshaped the playing field in 2025 (slide 10), and if you lost count of the acronyms, you are not alone (check the dictionary below). The OBBBA extended the 45Z Clean Fuel Production Tax Credit but reduced the SAF base credit and introduced North American feedstock sourcing requirements. California’s LCFS amendments tightened the carbon benchmark while capping credits on crop-based oils at 20%. And the EPA’s proposed RVOs nearly doubled biodiesel (D4) obligations year-over-year while introducing a 50% RIN haircut on imported feedstocks.

The net effect: foreign feedstock dependency is now a policy variable, not just an operational one. In our restricted case, foreign UCO becomes outright value-destructive (slide 20). Operators like Neste, World Energy, and MPC, with over 60% of their California LCFS pathways relying on foreign inputs, are most exposed (slide 21). The RIN environment, by contrast, is becoming more constructive: D4 to D6 prices are forecast to rise $0.13 to $0.17/RIN in 2026 to 2027 (slide 13), supporting 5% to 7% revenue uplifts for domestic soybean oil producers (slide 15). The catch is LCFS, where the credit bank peaking near 50 million tonnes CO2e holds prices at an average of just $46/tonne through 2030 (slide 14), pressuring manure RNG economics in particular.

The standout opportunity is ethanol plus CCS (slides 25 to 26). By stacking 45Z, 45Q, LCFS, and CDR credits, the NPV of adding CCS to an unpartnered ethanol plant can exceed the implied value of the base plant by more than 2x across 50-plus candidate facilities. GEVO (slide 27) is the clearest example of separating fuel value from carbon value across both compliance and voluntary markets.

On a lighter note, I welcomed a new family member this week. Archie lives on a Mac Mini and runs on OpenClaw (formerly known as Clawdbot and Moltbot), an autonomous AI agent I have been tinkering with. He can fetch data, run Python scripts, interpret results, and loop back for the next step. The same theme of discipline applies: longer chains drift quietly off course, and poorly scoped workflows burn through tokens fast. He is young and running lean on Gemini Flash 3, but showing promise. I’ll keep y’all updated!

45Q = CCUS Tax Credit
45Z = Clean Fuel Production Tax Credit
CDR = Carbon Dioxide Removal
LCFS = California’s Low Carbon Fuel Standards
OBBBA = One Big Beautiful Bill Act
RIN = Renewable Identification Number
RNG = Renewable Natural Gas
SAF = Sustainable Aviation Fuel

Thoughts, questions or things we missed? Send me a note (or hit reply) - I would love to hear from you.

Episode Summary: This episode explores the critical intersection of environmental regulation and emerging technology. The hosts analyze how the potential repeal of the EPA’s endangerment finding might affect the carbon capture industry and transition into a deep dive on the rapid evolution of AI agents and their impending impact on the future of employment.

Topics Covered:

• EPA Regulations and the Endangerment Finding: The implications of potential policy shifts on environmental standards.

• The Future of Carbon Capture (CCS) and 45Q Tax Credits: Navigating measurement, reporting, and verification (MRV) for sequestration projects.

• Global Carbon Accountability: How mechanisms like Europe’s carbon border adjustments are influencing global industries, including India’s steel sector.

• AI Evolution and “OpenClaw”: Staying proficient in a fast-moving landscape of new AI tools.

• AI Agents vs. Human Employment: The growing tension between hiring human interns and deploying digital entities for autonomous tasks.

Key Takeaways:

• Commitment Over Policy Noise: Long-term corporate strategies for decarbonization often remain steady despite short-term regulatory “noise” or changes in administration.

• The Power of MRV: Precise measurement and third-party verification remain the “crux” for companies looking to secure 45Q tax credits for CO2 storage.

• Global Export Pressures: International trade mechanisms are effectively forcing global jurisdictions to remain accountable for their emissions to stay competitive.

• AI Proficiency as Job Security: AI cannot take a job if the job itself is to proficiently use and manage AI tools.

• The Rise of Humanless Businesses: We are approaching a threshold where billion-dollar companies may soon be run autonomously by AI agents with minimal human equity holders.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for listening!

The Winter Olympics are a reminder that in elite competition, small errors compound quickly. The same applies to IPP investing. Strong sector performance over the past year, driven by M&A and hyperscaler offtake momentum, has given way to pressure from PJM load forecast revisions and rising behind-the-meter data center development. The pullback has created differentiated opportunities across names.

TLN bolted on 2.6 GW of gas capacity from Energy Capital Partners for $3.45 billion. We value the acquired portfolio at $4.64 billion, putting NAV at $366/share, a 6% discount to yesterday’s close. But the acquisition is highly concentrated: 96% of the value sits in just two plants (Waterford and Lawrenceburg), so any further upside depends on whether those assets can contract at attractive prices. Susquehanna anchors the story, but durability depends on PJM capacity prices staying elevated and 2 GW of disclosed PPAs actually closing.

NRG is the hardest to underwrite at today’s price, and the Kiewit-GEV venture is its chipping medal: impressive on paper, but fragile under stress. At $125/share NAV versus ~$173 market, a roughly 28% discount, the venture (5.4 GW of new CCGTs by 2032) needs to land perfectly. Our base case assumes $1.4 million/MW capital costs, competitive against the $2.0+ million/MW others are quoting, but untested at scale. At $1.76 million/MW, NAV drops nearly $8/share. Justifying today’s stock also requires LMP expansion and capacity prices at the cap.

VST is our Klæbo: dominant, efficient, and built for endurance. At $220/share NAV versus ~$174 market, it trades at a 20% discount. The META nuclear PPAs lock in 2.2 GW at ~$85/MWh, and 2026 to 2030 average EBITDA of $7.8 billion at 9.5x implies ~$74 billion EV. Less net debt and other claims gets you to our $220/share equity value. While peers grind the incline, VST’s contracted nuclear floor lets it sprint.

CEG is the franchise built to last. We value equity at $295/share, 2% below Tuesday’s close, but the base case understates the opportunity. With approximately 15 GW of uncontracted nuclear capacity, if the remaining fleet signs PPAs at $90/MWh, NAV rises 68% to ~$495/share. Hyperscaler demand for clean baseload is accelerating, the probability of additional contracts is rising (see VST’s recent META deals locking up remaining nuclear capacity), and the quality plus asymmetry of the position makes CEG a compelling hold even at current levels.

We favor VST and CEG. Nuclear optionality is real, contracting momentum is building, and these are franchise assets moving into an increasingly scarcity-priced environment.

Thoughts, questions or things we missed? Send me a note (or hit reply) - I would love to hear from you.

Episode Summary: This episode recaps a major energy investor conference in New York, exploring the persistent bullish sentiment for the oil market entering 2027. The hosts discuss the strategic challenges of depleting Tier 1 inventory and the growing industry focus on international exploration to address global reserve replacement needs.

Topics Covered:

• New York Energy Investor Conference Recap: A look at the sentiment of over 130 North American investors across power, renewables, and oil and gas.

• The Bullish Outlook for Oil: Analysis of why market optimism remains high despite a tough recent quarter.

• Tier 1 Inventory Depletion: The industry-wide challenge of shrinking high-quality inventory and the pivot to Tier 2 basins.

• International Exploration & Tech Export: Opportunities in global jurisdictions like Argentina and the Middle East through the export of shale technology.

• Technical vs. Geopolitical Risk: The shifting balance of risks when choosing between domestic and international energy plays.

Key Takeaways:

• Price-to-Gold Disconnect: While oil prices have been volatile, metrics relative to gold suggest oil is at a multi-decade low ratio, supporting a “persistent bullish view”.

• Inventory Crisis: The depletion of “Tier 1” resources is forcing investors to choose between lower-quality domestic basins or higher-risk international expansions.

• Reserve Replacement Gaps: Many major energy players are replacing reserves at a ratio well below 1.0, indicating a long-term depletion of the global resource base.

• Geopolitical Priority: Investors are increasingly weighing geopolitical uncertainty against technical drilling risks as they look toward international jurisdictions for growth.

Comments, questions or things I missed? Send me a note (or hit reply) - I would love to hear from you. Thanks for listening!

Good things happen when you put smart investors in a room and get them to talk. Last week we brought some of the best in the industry together for EnergyEdge in New York to discuss “What is the most compelling opportunity in energy for 2026?” Amidst the 79 slides, personalities, opinions, and a bit of talking their book, a few themes jumped out.

For starters the sentiment towards oil was clearly bullish (slide 34). The arguments varied: “we’re clearing the OPEC surplus”, “we’re exhausting tier 1 shale inventory” (slide 37), “the majors are failing to replace reserves”. Or maybe everyone pre-read the memo that its “black gold” and trading at a 50-year low relative to the shiny stuff. Whatever the reasoning, hope is back in the oil trade.

But how do you play it? That’s where the bets diverged. Do you take tier 2 Permian over anything else? That’s where history might guide you. But there was more on offer than that: Second tier basins, transfer shale technology to mature but thick conventional fields, export the technology to shale basins further afield (its not just the Vaca Muerta that could work), or take a bet on forgotten international assets. We heard pitches for all of it, and consensus on none.

You notice an appetite to go abroad in those ideas, and with them opposing perspectives on geopolitical risk (slide 16). Some noted increased uncertainty in the US, while others noted an underappreciation for pre-existing geopolitical risk in other parts of the world. What was consistent? The world feels less safe and there is a premium to be paid (and earned) by getting it right.

In power, the hot spot was the pace that power assets, and in particular gas-fired power assets, have repriced. “It is unlike anyone has seen in power before” one longtime investor stated. Given that these assets have tripled over the last couple years, that observation isn’t much of a surprise. But does it mean valuations have topped out?

Yes, power is the life blood of data centers (slide 28) and the recognition that gas-fired generation is key to AI puts a floor value on valuations. And yes, these offtakers will take as much power as you can give them, as soon as you can get to them (at least for now).

But that’s also what’s driving the 40+GW of bring your own power (slide 8) and the surge of distributed power solutions (slide 27). Throw in the known policy risks (or opportunities) like an “emergency” capacity auction in PJM (slide 54), and unknown risks of risks as electricity affordability grows as a political issue, and it all gets very asset specific (slide 66). With more than 20 GW of gas fire capacity likely to change hands this year (slide 67), don’t expect the market for high-quality thermal assets to slow down in 2026.

Thoughts, questions or things we missed? Send me a note (or hit reply) - I would love to hear from you.

LBRT got a 20% “power-up” last week. 3 GW of power projects by 2029 will do that for you. The move was the latest in the trend towards flexible power solutions - something we highlighted in our 2026 Global Outlook - which continues to mint new winners in the data center boom. LBRT, PUMP, BKR and AESI are all up more than 20% YTD. And don’t mention BE, up 75%. Flexible, quick to deploy power is in demand, and it is the oil field service companies getting the love of late.

GEV also reported last week and this line from the call stood out to me: “we don’t really view those smaller units to be competition”. We agree. Time is the competition. Large-frame combined-cycle turbines often require four to six years from contract to COD. For hyperscalers facing urgent capacity needs, these timelines are often incompatible with business requirements.

The logical response is substitution, even at higher cost: smaller aeroderivative turbines, simple-cycle units, on-site gas paired with batteries, modular and temporary solutions. Exactly what LBRT and their peers are offering, often with 15 year contracts. And there is lots more where that came from.

Are short-term substitutes a risk? GEV doesn’t think so, arguing “as the heavy-duty gas turbines are available, those smaller applications will become the reliability solution” (i.e. back-up power). I guess when your gas power equipment pipeline jumps from 62GW to 83GW sequentially, the wind at your back feels pretty firm.

But time waits for no one. Hyperscalers have explicit low- or zero-carbon commitments. Many view gas as a temporary reliability solution, not a long-term endpoint. While we wait for heavy-duty gas turbines to arrive, the hyperscalers continue to explore nuclear (META/VST, AMZN/TLN, MSFT/CEG), SMRs (META/Oklo, GOOGL/Kairos, AMZN/X-Energy), and geothermal (MSFT/Fervo). More competition is coming.

On a seemingly unrelated note, PJM just lowered is summer peak demand outlook by 4 GW, following tighter scrutiny of large‑load requests and updated economic inputs. That brought its forecast to 2028 more in-line with our views. Looking beyond, the outlook remains overstated relative to our forecasts, a victim of the land grab and optionality sought by data center developers trying to meet the demand for AI compute.

Downward load forecast revisions foreshadow the risk of the most optimistic outlooks for gas fired generation. 43 GW of GEV pipeline are SRAs (slot reservation agreements), primarily for those years beyond 2028 that house the most speculative of data center projects in ISO load queues. As reality bites - and the options those queue positions represent expire - how many of those SRAs fall away? How much leverage do those short time-to-power solutions gain from their incumbency?

Gas remains indispensable to the power mix. The gas-fired boom is real and it will underpin the build-out of AI data centers. But it will also show up differently than many expect. Time-to-power constraints are pushing key customers, especially hyperscalers, toward faster, smaller, and more modular solutions. And those same customers are likely to adopt zero-carbon baseload as soon as it becomes credible. Place your bets on gas, but place them carefully.