If one form of energy becomes the cheapest, most secure, and cleanest, you’d probably adjust your economy to use more of it, right?

That’s one of the ideas underpinning a report the International Energy Agency issued last week about the global move toward electrification. Fatih Birol, head of the IEA, discussed some of the concepts from the report the following day at the United Nations.

“Many, many years in the energy world, we had three choices in front of us. Shall I choose the most secure energy option? Shall I choose the most economic energy option? Or shall I choose the cleanest energy option?” he asked. “We have to make choices, we have to make trade-offs but when we look at the world today, for the first time in years, all of these three objectives are getting aligned.”

Birol’s referring to the “energy trilemma,” a concept in energy and economics research about the need to balance cost, security and environmental impact of sources and systems. He said those priorities are increasingly aligning in part because of the Iran war, which has driven up fuel prices and exacerbated concerns about energy security.

It’s notable that the leader of the world’s leading energy research organization is so optimistic about electrification. I reached out to energy researchers this week to get their reaction.

But first, some numbers:

With $100 worth of gasoline, an internal-combustion-engine car would go 862 miles; with $100 worth of electricity, an electric vehicle would go 2,310 miles—nearly a threefold advantage for the EV.

For home heating, $100 would buy enough fuel for a gas boiler to provide 30 days of heating; it would also fund enough electricity for a heat pump to provide 42 days of heating—a 40 percent edge for the heat pump.

The figures are 2025 global averages from the IEA report, and the gaps are probably larger today with gasoline and natural gas experiencing price spikes that exceed rising electricity costs.

Global electrification trends also tell the story. The world used 16.7 percent of its final energy—meaning the energy consumed by the end user—from electricity in 2000, and that share rose to 23.4 percent in 2025, according to the IEA.

China has had the largest change, and North America was among the smallest in that period, a difference largely attributable to levels of renewable energy use and EV adoption.

But this doesn’t mean electrification is inevitable, according to energy-systems researchers.

“I see today’s energy system more as a Rorschach test,” said David Victor, a professor of innovation and public policy at the University of California, San Diego.

By that, he means different people see different things. He acknowledged it’s possible to view the trilemma as becoming aligned, but he still sees many obstacles.

For example, he doesn’t expect to see a rapid shift to electrification for heavy trucks, maritime transport and aircraft. He also thinks trade barriers may hinder electrification, with countries seeing benefits of producing goods at home rather than relying on imports such as Chinese solar panels.

“The trade-offs are inescapable,” he said.

Some of the trade-offs are political, with elected officials and entrenched industries seeking to slow electrification, said Emily Grubert, an energy systems researcher at the University of Notre Dame.

“I think [Birol is] basically correct, but the reason we’re not seeing transition happening rapidly and naturally is that the ‘we’ he’s referring to is not the ‘we’ that actually makes the decisions,” she said in an email.

Instead, some decision-makers are prioritizing the profits of certain industries, which often means favoring fuels such as coal, natural gas and oil, and slowing the transition to cleaner sources.

Grubert could be describing many countries in which fossil fuel industries hold political sway, including the United States.

Kenneth Medlock III, senior director for the Center for Energy Studies at Rice University’s Baker Institute for Public Policy, said it’s vital to fully evaluate the costs and benefits of electrification.

“In truth, the lesson from the current moment is to diversify energy choice and the supply chains of specific energy types, but at the lowest possible cost,” he said.

Medlock thinks arguments in favor of electrification don’t account enough for the high upfront costs of building the systems. A good example is how wind and solar have low operating costs but are expensive to build. Fossil-fuel power plants also are expensive to build, but his larger point is that the advantages of any option come with disadvantages

Outside the wealthiest economies, the case for electrification is tougher to make, said Chuks Okereke, professor of global climate governance and public policy at the University of Bristol in the United Kingdom.

“Sometimes in the bid for optimism, we oversimplify and ignore important contexts,” he said.

Okereke was lead author of a paper published in June in the journal Energy Economics that shows how fossil fuels and highly polluting development make short-term economic sense in Nigeria compared to cleaner alternatives. One reason is the high upfront cost of infrastructure.

For the energy transition to be a net-positive for the Nigerian economy, the world’s wealthy countries and international organizations would need to provide assistance.

Birol closed his UN speech by discussing the goal of getting to 35 percent electrification of final energy by 2035. The goal was part of discussions of climate policymakers in June in Bonn, Germany, and will be on the agenda at COP31 in November in Antalya, Turkey.

If the target is agreed to at the climate change conference, it will “give a very strong and unmistakable signal” to the world about the path forward, he said.

Birol isn’t saying the world has solved the problems of the energy transition. He’s saying this immense challenge becomes a bit easier when some of the competing priorities begin to align more closely than they did before.

This matters because electrification and decarbonization go hand in hand. It means shifting nearly everything that runs on coal, oil, and gas to electricity, while simultaneously transforming the grid to run on carbon-free sources. None of this will be easy.

This article originally appeared on Inside Climate News, a nonprofit, non-partisan news organization that covers climate, energy, and the environment. Sign up for their newsletter here.

Read full article

Comments

"Rendering Doom in a database is obviously a bad idea," Lukas Vogel writes in a lengthy blog post explaining how exactly he managed to render Doom using an SQL database.

OK, that's not entirely accurate. The SQLDoom project uses a small Python client to handle input and output, drive the game's timing, and display each frame to the screen. Behind that, a series of CedarDB tables tracks the game geometry and state, while about 1,300 lines of SQL queries spread across 89 common table expressions implement the game logic and generate 35 bitmap framebuffers per second.

In this, SQLDoom is a major improvement over Vogel's previous DoomQL project, which last year set out to build "a multiplayer Doom-like shooter entirely in SQL." Unfortunately, that effort ended up with raycasting-based, grayscale ASCII graphics that were more akin to the simplistic 90-degree-angled maps of Wolfenstein 3D. The newer SQLDoom, on the other hand, generates full-color 640x480 frames that look like they could have come from the original Doom executable.

It's all just data, man

Converting Doom's classic WAD files to a relational database was relatively simple and straightforward, Vogel writes, because of the way the original game broke levels down into vertices, lines, sectors, and so on. Even Doom's famous binary-space partition trees can be broken down into SQL using a sort_key for objects that's pre-computed for each position at load time. With this set in your table, a simple "ORDER BY" statement can determine every frame which parts of walls to display and which to ignore, vastly improving performance.

A visualization of the sorting/culling algorithm used in the SQLDoom project Credit: Lukas Vogel

There were a few complications in going from SQL table to rendered first-person frames, however. Chief among these was the algorithm needed to render floors and ceilings, which can't really make use of the elegant "visplanes" and state mutations that handle this rendering on a column-by-column basis in the original game. For SQLDoom, Vogel uses what he calls a "pretty hacky" replacement involving iterating over an ordered list of panels.

Despite the substantial additional overhead of reading and writing to SQL tables for everything, Vogel said he was able to get DoomSQL running at about 60 fps on a Ryzen 7-powered laptop, with occasional dips down to 35 fps for busy scenes. And despite the hassles of translating Doom to SQL, Vogel points out that the database's steady "reference snapshot" of the game state, along with inherent concurrency and access handling features, offers significant benefits for running a multiplayer server. With a database, there's "no partially applied updates, physics bugs, or disagreements over whether the rocket actually hit," Vogel says.

If you want to run a Doomtabase on your own local machine, you can do so simply with the GitHub code, a copy of CedarDB, and a Doom WAD file. Or you can jump into a free online hosted demo match to test it out without all the setup (I got some pretty poor performance using this option, though). And when you're done with that, maybe peruse some of the many other unlikely Doom ports that Ars has covered over the years.

Read full article

Comments

Russian society is becoming increasingly militaristic since the war in Ukraine began. These soldiers marched in Moscow in a parade in 2023.

The Trump administration has taken government equity stakes in more than three dozen private companies.

(Image credit: Win McNamee)

Deep Blue took down Garry Kasparov at chess in 1997, AlphaGo beat Lee Sedol at Go in 2016, and poker bots have been beating professionals for years. But one classic game called Stratego held out. Even DeepMind, with its exceptional budget, couldn't build a machine that reliably beat the best human players.

Now, a team of researchers from Carnegie Mellon, MIT, New York University, and Stanford University has done it. Their AI, called Ataraxos, beat Pim Niemeijer, arguably the best Stratego player of all time, 15 games to one, with four draws. And it took just 16 GPUs and a few thousand dollars to train it.

Hidden armies

In Stratego, each player gets 40 pieces representing military ranks, from a marshal down to a spy, plus bombs and a flag. You win by capturing the opponent's flag. Your opponent knows where your pieces are, but not what they are. Identities are revealed only when two pieces collide in battle—the weaker one is removed, and the identity of the winner is revealed. That makes Stratego an imperfect-information game, just like poker, which computers cracked years ago. “There's something super distinctive about Stratego, which is that it is a massive amount of hidden information that unfolds over a very long time scale,” said Eugene Vinitsky, a researcher at NYU and co-author of the study.

In some forms of poker, the hidden information is tiny. In Texas Hold’em, “You only have two hidden cards,” said Gabriele Farina, an MIT computer scientist and another co-author. That leaves just 1,326 possible hands, few enough for a machine to weigh them all. “In Stratego, there's 40 pieces on the board that could be in any order,” Farina said. That's more than a decillion possible setups. Then there's the game's length.

“In chess, usually the game lasts 40 moves, but in Stratego, a game can easily last 2,000 moves,” Farina said. On top of that, Stratego is a game of bluffing. Sometimes you move a weak piece as if it were a marshal, just to scare the opponent off. When players bluff too often, their threats mean nothing; when they never bluff, they become predictable. That balancing act, the team explains, is what stumped earlier AIs like DeepMind’s DeepNash, introduced in 2022.

Learning to guess

Just like DeepNash, Ataraxos learned by playing against itself—163 million games in total. In these self-play sessions, moves that led to wins were reinforced and played more often in future matches, while moves that led to losses were played less, which was the same simple training idea. The difference was in how much Ataraxos adjusted after each game, because hidden information tends to send self-play learning algorithms around in circles. The team addressed this by making big, bold changes in strategy early in training and small, careful ones later.

The even bigger innovation was something DeepNash never had: thinking ahead before each move. AIs like AlphaGo refine their general strategy with a search just before acting. DeepMind couldn't make that work in Stratego because the search space was too large, leaving it an open question whether it was worth trying.

“This is one of the things that we did figure out how to do,” Farina said. The solution was a second neural network, a belief model, trained to guess the opponent's hidden pieces based on how they had been moving. This way, instead of iterating through every possible arrangement, Ataraxos samples plausible ones, plays out candidate moves in each, and picks based on how they turned out.

And it shows in its playstyle.

Calm and unbothered

The name Ataraxos comes from the ancient Greek word for a state of calm. “It means somebody that's calm and unbothered,” Farina explained. He suggests the structure of the AI and its lack of human emotions ensure it doesn’t react impulsively, “even in situations where a human would be losing their mind.” While the human might try big gambles to come back from a significant deficit, Ataraxos would work its way back into the game slowly and methodically.

The strategy it developed also avoids drawing attention to any problems it faces. When Ataraxos estimates its opponent has no reason to suspect a weak spot, it leaves that spot alone, even if it might look like a disaster waiting to happen to anyone who can see both sides of the board.

“For humans, it's very hard when you know a secret to make decisions ignoring the fact that you know that secret,” Farina said. “For machines, it's easy.” This, the team says, leads machines to make moves a human would only do while bluffing—and follow up on them much better than humans. “We would watch the bot ‘bluff’ its way back from like a two percent victory probability, very, very casually,” Vinitsky added.

Niemeijer, the human player Ataraxos pulled these miraculous comebacks against, has four world championships and more than 600 weeks as the world's top-ranked player.

The match

Over three weeks, Niemeijer played 20 online games against Ataraxos, earning $100 for each win. He knew the AI would not adapt to him, which gave him time to hunt for weaknesses. He managed to win just once.

That loss, researchers claim, wasn't really a flaw. Playing Stratego well requires randomizing the arrangement of your pieces, so luck always plays a role. “Even a perfect strategy, sometimes it will just lose,” Farina said.

The human champion apparently got lucky, but it went both ways. “Sometimes we got lucky,” Vinitsky admitted.

At the 2025 Stratego World Championship, attendees who challenged Ataraxos fared even worse. The AI won 38 of 40 games. In the process, it also changed how people play. “I think this bot has kind of skewed the metagame a little bit,” Farina said.

Players were surprised, for example, by how often it tucked its flag into a corner behind just two bombs, a rarely played setup.

But Ataraxos's best trick was arguably its price tag.

The price to pay

DeepNash was trained for two to three months on 1,024 of Google's specialized chips, a run the Ataraxos team estimates would cost $3 million to $4.5 million at 2025 prices. Ataraxos, in contrast, needed 16 GPUs for a week, plus an additional four GPUs for four days to train the belief model.

Farina and lead author Samuel Sokota achieved this efficiency by writing a simulator that runs millions of moves per second on graphics cards. “At the scale that we are in academia, we don't really have access to an entire field of GPUs,” Farina said.

The algorithm also learned far faster—it played about 34 times fewer games than DeepNash, and still ended up much stronger.

The Ataraxos architecture also worked in learning other games. The same approach beat three world champions at Barrage Stratego, a faster eight-piece variant of Stratego, mastered the cooperative card game Hanabi, and beat the best bots at the Chinese card game dou dizhu. But the team has its sights set on scenarios far more complex than board or card games.

Beyond the board

Board games have fixed rules and clear winners, while real-world problems like negotiations, financial markets, or military conflicts usually don't. But the Ataraxos team argues the gap is smaller than it looks, since tackling any real problem starts with building a simplified model of it.

“War gaming is a common thing that people do,” Vinitsky said. “You can use the techniques that were derived here to play it forward and see how a strong opponent might respond to what you do.”

Farina and his colleagues are now interested in making their AI more understandable, since Ataraxos, in its current state, can't explain why it makes the moves it makes. “We work on machines that produce strong but also interpretable and explainable strategies. I think we're not quite there yet," Farina said.

Nature, 2026. DOI: 10.1038/s41586-026-11036-y

Read full article

Comments

Researchers looked back at the baby-boom era and found that federal mortgage programs played a part in the country’s population upswing after World War II.