Category: Report

Chips must usually be kept below 27ºC, hence 10-20% of both the capex and energy consumption of a typical data-center is cooling, as explored in this 14-page report. How much does climate matter? What changes lie ahead? And which companies sell into this soon-to-double market for data-center cooling equipment?

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Our base case outlook for AI considers 150GW of AI data-centers globally by 2030, underpinning 1,000 TWH pa of new electricity demand. However, at $30,000 per GPU, it is not advisable to cook your chips. 150GW-e of AI data-centers requires 150GW-th of data-center cooling. Hence the data-center cooling market is summarized on page 2.

The commercial cooling industry hinges on industrial HVAC, across heat exchangers, water evaporator units and industrial chillers, and explained from first principles on pages 3-4.

An underlying observation is that increasing demand for chilling capacity pulls on many capital goods categories such as compressors, heat-exchangers, pumps, fans and blowers, storage tanks, piping, VFDs, switchgear, grid connections and engineering and construction. All of the capex ultimately goes somewhere.

The economics of commercial cooling are broken down across capex, electricity, maintenance, utilization and operating decisions on page 4-5.

Another feature of our model is that we can stress-test PUEs and capex costs according to different inputs and outputs, for example, to control for water use (currently up to 10-30ml per GPT query), different climates, or tolerating higher temperatures at the chip-level.

Specifically for data-centers, the market is unusual in that it tolerates higher temperatures than other cooling sub-segments (which typically chill water to 7ºC), but also higher cooling density in kW/rack (pages 6-7).

Location matters. For example, how are the PUEs and capex costs of data-centers different in cool locations such as Norway and Calgary, versus hot, arid locations such as West Texas and the Middle East? Answers for core cooling and overall data-centers are on pages 8-9.

Immersion cooling may offer advantages over direct-to-chip cooling, and thus gain market share, for reasons outlined on pages 10-11.

Ten companies control 60% of the $15bn pa data-center cooling market, including two Western leaders. Best-known is Vertiv. #2 is a global capital goods giant. Key conclusions from our company screen are on pages 12-14.

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Rising energy demands of AI are now the biggest uncertainty in all of global energy. To understand why, this 17-page note gives an overview of AI computing from first principles, across transistors, DRAM, GPUs and deep learning. GPU efficiency will inevitably increase, but compute increases faster. AI most likely uses 300-2,500 TWH in 2030, with a base case of 1,000 TWH.

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The energy demands of AI are the fastest growing component of total global energy demand, which will transform the trajectory of gas and power and even regulated gas pipelines, as recapped on pages 2-3.

These numbers are so material that they deserve some deeper consideration. Hence this 17-page note is an overview of AI computation.

Of course, in 17-pages, we can only really scratch the surface, but we do think the report illustrates why computing efficiency will improve by 2-50x by 2030, and total compute will increase 3-5x faster. Thus a range of forecasts is more sensible than a single point estimate.

Transistors made of semiconductor materials, underpin all modern computing by allowing one circuit to control another. The basic working principles of MOSFETs are explained briefly on page 4.

All computers also contain a clock which is an oscillator circuit, generating pulses at a precise frequency. A faster clock accelerates computing, but also amplifies switching losses in transistors, per page 5.

Combinations of transistors can enact logical and arithmetic functions, from simple AND, OR and NAND gates, to matrix multiplications in the tensor cores of GPUs, as shown on page 6.

Transistors and capacitors can be arranged into DRAM cells, the basis of fast-acting computer memory. But DRAM also has a continued energy draw to refresh leakage currents, as quantified on page 7.

GPUs are fundamentally different from CPUs, as they carve up workloads into thousands (sometimes millions) of parallel processing threads, implemented by built-in cores, each integrated with nearby DRAM, and as illustatrated for NVIDIA’s A100 GPU on page 8.

An AI model is just a GPU simulating a neural network. Hence we outline a simple, understandable neural network, training via back-propagation of errors, and the model’s inherent ‘generativity’ on pages 9-10.

A key challenge for energy analysts is bridging between theoretical peak performance at the GPU level and actual performance of AI computing systems. The gap is wide. The shortfall is quantified on page 11.

Our favorite analogy for explaining the shortfall is via the energy consumption of planes, which can in principle reach 80 passenger miles per gallon. Jet engines provide a lot of thrust. But you also need to get the plane into the air (like pulling information from memory), keep it in the air (refreshing data in DRAM) and fuel consumption per passenger falls off a cliff if there are very few passengers (memory bandwidth constraints, underutilization of GFLOPS). See page 12.

If you understand the analogies above, then it is going to be trivial to improve the energy consumption of AI, simply by building larger and more actively used neural network models that crunch more data, and utilize more of the chronically underutilized compute power in GPUs. Other avenues to improve GPU efficiency are on page 13.

The energy consumption of AI is strongly reminiscent of the Jevons effect. Increasing the energy efficiency of GPUs goes hand in hand with increasing the total compute of these models, which will itself rise 3-5x faster, as evidenced by data and case studies on pages 14-15.

Forecasting the future energy demands of AI therefore involves several exponentially increasing variables, which are all inherently uncertain, and then multiplying these numbers together. This produces a wide confidence interval of possible outcomes, around our base case forecast of 1,000 TWH pa. Implications are on pages 16-17.

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This note may also be read alongside our overview of the gas and power market implication of AI, as shown below.

Energy and AI: the power and the glory?  

FERC regulations are surprisingly interesting!! In theory, gas pipelines are not allowed to have market power. But increasingly, they do have it: gas use is rising, on grid bottlenecks, volatile renewables and AI; while new pipeline investments are being hindered. So who benefits here? Answers are explored in this 13-page report.

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There are three major trends underway for gas pipelines in the energy transition. Demand is rising to backstop renewables and power AI data-centers. Pipeline capacity growth is stagnating due to various roadblocks. And yet gas prices are becoming increasingly volatile. These effects are all discussed on pages 2-3.

In any other industry, these conditions — demand surprising to the upside, supply stagnating, and increasing arbitrage — would be a kingmaker. Perfect conditions for incumbents to generate excess returns.

The peculiarity of the US gas pipeline industry is that the companies within this industry are regulated by FERC. Pipeline companies are not allowed to earn excess returns. They must not exercise pricing power, even when they obviously do have it.

Hence the purpose of this note is to explore FERC regulations, to assess what changes in industry conditions might mean for gas pipelines, or conversely, whether these changes will benefit others elsewhere?

A concise overview of regulated gas markets — covering FERC, recourse rates, long-term contracts, open season, firm customers, NPV prioritization, Section 4 and Section 5, capacity scheduling, nominations and capacity release markets — are distilled on pages 5-6.

Ensuring utilization is the most important dimension dictating the economics of pipelines and pipeline companies, as discussed on page 7.

Gas marketers may be the primary beneficiary of evolving market dynamics, for the reasons discussed on page 8.

But can the increasing value of pipelines trickle back to pipeline operators, and boost their returns in ways that are nevertheless compatible with FERC regulations? Our answers to this question are on pages 9-10.

Leading companies in US gas marketing and pipelines are compiled in our screen of US midstream gas, and discussed on pages 11-12.

Implications extend into power markets as well. Increasing market volatility is actually needed as a catalyst to expand energy storage. And similar issues will arise due to power grid bottlenecks. Closing observations are on page 13.

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The power demands of AI will contribute to the largest growth of new generation capacity in history. This 18-page note evaluates the power implications of AI data-centers. Reliability is crucial. Gas demand grows. Annual sales of CCGTs and back-up gensets in the US both rise by 2.5x?

This is the most detailed report we have written to-date on the implications of AI, for power markets, gas and across capital goods.

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We started by asking ChatGPT for examples where AI data-centers had installed their own power generation equipment. We received a very detailed list. All erroneous. All hallucinations. Hence there is still a role for a human energy analyst to address these important questions.

Forecasts for the energy demands of AI are broken down from first principles, within the energy demands of the internet, on pages 3-4.

Economics of AI data-centers are also broken down from first principles, across capex, opex, and per EFLOP of compute, on pages 5-7.

Data-centers primarily pull upon gas value chains, as uptime and reliability are crucial to economics, whereas only 6% of a data-center’s energy needs could be self-powered via on-site solar, per pages 8-9.

Combined cycle gas turbines are predicted to emerge as a leading energy source to meet the power demands of AI data-centers, and relative economics are quantified on pages 10-11.

The need for newbuild power equipment also hinges on maximizing uptime and utilization, and avoiding power grid bottlenecks, as outlined on pages 12-13.

Power grids: the biggest bottleneck in the world?

To contextualize the growth that lies ahead, we have compiled data on US power generation installations, year by year, technology by technology, running back to 1950, including implications for turbine manufacturers, on pages 14-16.

The impacts of AI on US gas and power markets sharply accelerate US electricity demand, upgrade our US shale forecasts, especially in the Marcellus, and sustain the growth of US gas demand through 2035. Charts and numbers are on pages 17-18.

We look forward to discussing and debating these conclusions with TSE subscription clients.

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This 11-page note summarizes the key conclusions from our energy transition research in 1Q24 and across 1,400 companies that have crossed our screens since 2019. Volatility is rising. Power grids are bottlenecked. Hence what stands out in capital goods, clean-tech, solar, gas value chains and materials? And what is most overlooked?

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1,400 companies have been mentioned 3,000 times in our research since 2019, and our energy transition research now includes over 1,300 research notes, data-files and models.

Hence we want to do a better job of summarizing key conclusions, for busy decision-makers, in a regular and concise format (see pages 2-3).

The two key themes from our energy transition research in 1Q24 are rising volatility in global energy markets and rising bottlenecks in the power grid. The implications are summarized on pages 4-5.

The biggest focuses in our energy transition research in 1Q24 have been across the solar supply chain and gas value chains, and where could consensus be wrong? (pages 6-7)

The most overlooked theme in the energy transition is discussed on page 8, and centers on materials value chains.

Specific companies where we have reviewed product offerings or patent libraries in 1Q24 are reviewed on page 9.

The most mentioned companies in our research in 1Q24, and from 2019-2024 more broadly, are discussed (including some specific profiles) on pages 10-11.

The downside of a concise, 11-page report, is that it cannot possibly do justice to the depth and complexity of these topics. A TSE subscription covers access to all of the underlying research and data.

We are also delighted to elaborate on our energy transition conclusions from 1Q24, and discuss them with TSE clients, either over email or over a call.

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Power grids: the biggest bottleneck in the world?

Energy market volatility: climate change?

What if large quantities of power could be transmitted via the 2-6 GHz microwave spectrum, rather than across bottlenecked cables and wires? This 12-page note explores power beaming technology, advantages, opportunities, challenges, efficiencies and costs. We still fear power grid bottlenecks.

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Power grids are shaping up to be the biggest bottleneck in the energy transition, with implications across every sector (page 2).

Hence what opportunity exists to bypass bottlenecked cables and wires, by transmitting electricity as electromagnetic waves? Far field radiative power transmission, aka power beaming, was championed a century ago, by Nikola Tesla.

Electromagnetic radiation is simply the synchronized, energy-carrying oscillation of electric and magnetic fields which moves through a vacuum at 300,000 km per second (aka the Speed of Light, c). Relevant physics are re-capped on pages 3-4.

Magnetrons are the devices needed to convert electricity into microwave energy for power-beaming. Their functioning and efficiency are on page 5.

Rectennas are the devices needed to convert microwaves back into DC electricity. Their functioning and efficiency are on page 6.

Advantages of microwave power transmission are long ranges, minimal permitting requirements and flexibility in directing and re-directing loads.

Additional opportunities for power beaming go beyond the resolution of power grid bottlenecks. Without wishing to channel something out of Dune II, they could be used to power flying aircraft or to beam solar power from space (pages 7-8).

The efficiency of beaming power, from generation sources to end consumer, is modeled across a simplified value chain on page 9.

Costs and challenges of beaming power are explored on page 10. At long distances, we do think microwave transmission could cost less than HVDCs and other transmission lines.

Companies involved in power transmission via the microwave spectrum, are noted on page 11. One notable private company is EMROD. We also found a notable public company specializing in smaller-scale devices.

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What if the world is entering an era of persistent power grid bottlenecks, with long delays to interconnect new loads? Everything changes. This 16-page report looks across the energy and industrial landscape, to rank the implications of grid bottlenecks across different sectors and companies.

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Our growing fear is that power grids are shaping up to be the biggest bottleneck in the energy transition. Hence what if the developed world is entering a strange, new era, where grid tie-ins cost an order of magnitude more, and take an order of magnitude longer?

The implications of grid bottlenecks extend across every sector that consumes electricity and every energy source that produces electricity (ranked by size on page 3).

The biggest impacts are seen for the rise of the internet, manufacturing, and consumer prices, leading to inflation (pages 4-5).

Most impacted are new energies categories that rely upon timely electricity supplies from the grid, although our outlook differs for green hydrogen, heat pumps and electric vehicles (pages 6-7).

Most controversial are the outlooks for power electronics, power cables and underlying materials such as copper (pages 7-8).

Renewables growth is also impacted, but again, our outlook differs for wind versus solar, and along their supply chains (pages 9-10).

The first half of the report focuses on challenges in these different industries, so that we can understand the best way of resolving them.

The second half of the report assesses the outlook for other generation sources, across natural gas, smaller-scale gas, diesel generation, batteries, coal and nuclear (pages 12-13).

A growing number of facilities need ratable, round-the-clock power, yet are struggling to secure this from the grid. The best metric is the Levelized Cost of Total Electricity (page 11).

Renewable-heavy grids: dividing the pie?

Energy savings behind the meter also become more valuable, especially where they can reduce the peak sizes of grid connections. Our favorite ideas range from growth-stage niches to mega-caps (pages 14-16).

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Power grids will be the biggest bottleneck in the energy transition, according to this 18-page report. Tensions have been building for a decade. They are invisible unless you are looking. And power grid bottlenecks could last a decade. Further acceleration of renewables may be thwarted. And we are re-thinking grid back-ups.

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Power grid bottlenecks remind us weirdly of the US housing market in 2007-08. Capable of world-changing economic upheaval. Imbalances have been building for a decade. But they are invisible unless you are looking. So where should we be looking and what will we find?

In the most likely route to net zero by 2050, global electricity demand would grow by 2.5x, transmission and distribution miles grow by 3-5x and global grid capex rises by 5x. Charts and numbers are on pages 2-4. But will this actually happen?

The functioning of low-voltage distribution network is discussed on pages 5-6. We predict what might indicate distribution network bottlenecks. And these indicators are starting to look worrying.

The capacities of individual grid nodes are modelled on pages 7-8, using a modified Maxwell-Boltzmann distribution. We predict what would be the most likely indicator for grid node bottlenecks. These indicators are increasing, but not exponentially.

The functioning of the high-voltage transmission network is discussed on pages 9-12. The crucial conclusion is that interconnecting large new loads — wind, solar, data-centers, electric vehicles, etc — pulls on the entire network, not just the point of interconnection.

Indicators for transmission network bottlenecks include renewables curtailment rates (page 13), interconnection costs (page 14), interconnection timelines (page 15) and attempts to circumvent network costs by co-developing renewables plus batteries (page 16).

The evidence in this note suggests bottlenecks have slowly been building up over the past ten years. Some indicators of grid bottlenecks are ‘going exponential’ in 2024. Implications for batteries, load-shifting, natural gas and even diesel gensets are on pages 17-18.

Global solar: absorption spectrum?

Smooth operators: who benefits from volatile power grids?

Some topics in our research have tended to seem so large and far-reaching that we have struggled to capture all of the implications in a concise 18-page note. If you are a TSE client and would like to discuss this report, then please do contact us.

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Energy transition is the largest construction project in human history. But building in boom times is associated with 2-3x cost inflation. This 10-page note reviews five case studies of prior capex booms, and argues for accelerating FIDs, even in 2024. The outlook for project developers depends on their timing? And who benefits across the supply chain?

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A key challenge in our roadmap to net zero is the sheer amount of investment needed, stepping up from $3trn per year to $9trn per year. Where we see the biggest boom times ahead is summarized on page 2.

The thing about all of these booms is that no one seems to be in a rush. Especially amidst the political and geopolitical limbo of 2024.

Mega-projects have a checkered reputation among investors. Some commentators seem to despise them! But frankly, there is no other way to build world-changing infrastructure at a reasonable overall cost. We illustrate the economies of scale on pages 3-4.

In this note, we challenge the notion that it is better to wait. Building in boom times almost always rewards being early or counter-cyclical.

Our first case study for building in boom times is the Australian LNG industry, where projects later in the queue cost 2x more (page 5).

Our second case study comes from the costs of developing 130 oil and gas fields, developed offshore Norway in 1975-2024 (page 6).

Other case studies include Energy Majors’ development capex (page 7), UK offshore wind projects (page 8) and recent green hydrogen projects (page 9). Peak costs are 2-3x trough costs.

For project developers, the key conclusion is to target the front of the queue. Early projects are likely to achieve materially better outcomes than later projects. Conclusions for investors, the energy service supply chain and policymakers are on page 10.

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Solar semiconductors have changed the world, converting light into clean electricity. Hence can thermoelectric semiconductors follow the same path, converting heat into electricity with no moving parts? This 14-page report reviews the opportunity for thermoelectric generation in the energy transition, challenges, efficiency, costs and companies.

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Semiconductors have already changed the entire global energy industry by converting light into clean electricity (i.e., photovoltaic solar) and efficiently converting electricity back into light (i.e., LEDs).

But semiconductors can also convert temperature differences into electricity (the Seebeck Effect) and convert electricity into temperature differences, or in other words, very localized cooling (the Peltier Effect).

Perspectives on the rise of semiconductors, and the potential for thermoelectric generation to follow the path of PV solar generation, are discussed on pages 2-4.

The opportunity is vast. In our breakdown of global energy, 60% of primary energy is effectively wasted as heat. We discuss where thermoelectrics could unlock the largest opportunities on pages 5-6.

Thermoelectric devices are already commercial today, in a c$500M pa niche, ranging from NASA space probes, to remote power generation, to stove fans (available to purchase on Amazon for $20-60 apiece). Today’s commercial applications of thermoelectric generators and cooling devices are discussed on pages 7-8.

The efficiency of thermoelectric generation hinges on its Figure of Merit, aka ZT score, which we have modeled from first principles, and explained on pages 9-10.

Improved thermoelectric materials are needed for this market to accelerate. Today’s devices are 2-10% efficient, whereas photovoltaic solar really took off once it had become 15-20% efficient. Avenues to improve thermoelectric figures of merit are discussed on pages 11-12.

Our company screen covers twenty leading companies in thermoelectrics, from incumbent semiconductor manufacturing companies, to remote power generation specialists, to companies developing more novel technologies, as summarized on page 13.

What conclusions for decision-makers in the energy transition? For example, what would happen to fuel cells in a world that unlocked 40-50% efficient thermoelectric generators, with no moving parts and no decline rates? Closing observations are on page 14.

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