Talking about Games

Stay Ahead, Stay ONMINE

Talking about Games

Game theory is a field of research that is quite prominent in Economics but rather unpopular in other scientific disciplines. However, the concepts used in game theory can be of interest to a wider audience, including data scientists, statisticians, computer scientists or psychologists, to name just a few. This article is the opener to a […]

In this article, I will explain the kinds of problems Game Theory deals with and introduce the main terms and concepts used to describe a game. We will see some examples of games that are typically analysed within game theory and lay the foundation for deeper insights into the capabilities of game theory in the later chapters. But before we go into the details, I want to introduce you to some applications of game theory, that show the multitude of areas game-theoretic concepts can be applied to.

Applications of game theory

Even french fries can be an application of game theory. Photo by engin akyurt on Unsplash

Does it make sense to vote for a small party in an election if this party may not have a chance to win anyway? Is it worth starting a price war with your competitor who offers the same goods as you? Do you gain anything if you reduce your catch rate of overfished areas if your competitors simply carry on as before? Should you take out insurance if you believe that the government will pay for the reconstruction after the next hurricane anyway? And how should you behave in the next auction where you are about to bid on your favourite Picasso painting?

All these questions (and many more) live within the area of applications that can be modelled with game theory. Whenever a situation includes strategic decisions in interaction with others, game-theoretic concepts can be applied to describe this situation formally and search for decisions that are not made intuitively but that are backed by a notion of rationality. Key to all the situations above is that your decisions depend on other people’s behaviour. If everybody agrees to conserve the overfished areas, you want to play along to preserve nature, but if you think that everybody else will continue fishing, why should you be the only one to stop? Likewise, your voting behaviour in an election might heavily depend on your assumptions about other people’s votes. If nobody votes for that candidate, your vote will be wasted, but if everybody thinks so, the candidate doesn’t have a chance at all. Maybe there are many people who say “I would vote for him if others vote for him too”.

Similar situations can happen in very different situations. Have you ever thought about having food delivered and everybody said “You don’t have to order anything because of me, but if you order anyway, I’d take some french fries”? All these examples can be applications of game theory, so let’s start understanding what game theory is all about.

Understanding the game

Before playing, you need to understand the components of the game. Photo by Laine Cooper on Unsplash

When you hear the word game, you might think of video games such as Minecraft, board games such as Monopoly, or card games such as poker. There are some common principles to all these games: We always have some players who are allowed to do certain things determined by the game’s rules. For example, in poker, you can raise, check or give up. In Monopoly, you can buy a property you land on or don’t buy it. What we also have is some notion of how to win the game. In poker, you have to get the best hand to win and in Monopoly, you have to be the last person standing after everybody went bankrupt. That also means that some actions are better than others in some scenarios. If you have two aces on the hand, staying in the game is better than giving up.

When we look at games from the perspective of game theory, we use the same concepts, just more formally.

A game in game theory consists of n players, where each player has a strategy set and a utility function.

A game consists of a set of players I = {1, .., n}, where each player has a set of strategies S and a utility function ui(s1, s2, … sn). The set of strategies is determined by the rules of the games. For example, it could be S = {check, raise, give-up} and the player would have to decide which of these actions they want to use. The utility function u (also called reward) describes how valuable a certain action of a player would be, given the actions of the other players. Every player wants to maximize their utility, but now comes the tricky part: The utility of an action of yours depends on the other players’ actions. But for them, the same applies: Their actions’ utilities depend on the actions of the other players (including yours).

Let’s consider a well-known game to illustrate this point. In rock-paper-scissors, we have n=2 players and each player can choose between three actions, hence the strategy set is S={rock, paper, scissors} for each player. But the utility of an action depends on what the other player does. If our opponent chooses rock, the utility of paper is high (1), because paper beats rock. But if your opponent chooses scissors, the utility of paper is low (-1), because you would lose. Finally, if your opponent chooses paper as well, you reach a draw and the utility is 0.

Utility values for player one choosing paper for three choices of the opponents strategy.

Instead of writing down the utility function for each case individually, it is common to display games in a matrix like this:

The first player decides for the row of the matrix by selecting his action and the second player decides for the column. For example, if player 1 chooses paper and player 2 chooses scissors, we end up in the cell in the third column and second row. The value in this cell is the utility for both players, where the first value corresponds to player 1 and the second value corresponds to player 2. (-1,1) means that player 1 has a utility of -1 and player 2 has a utility of 1. Scissors beat paper.

Some more details

Now we have understood the main components of a game in game theory. Let me add a few more hints on what game theory is about and what assumptions it uses to describe its scenarios.

We often assume that the players select their actions at the same time (like in rock-paper-scissors). We call such games static games. There are also dynamic games in which players take turns deciding on their actions (like in chess). We will consider these cases in a later chapter of this tutorial.
In game theory, it is typically assumed that the players can not communicate with each other so they can’t come to an agreement before deciding on their actions. In rock-paper-scissors, you wouldn’t want to do that anyway, but there are other games where communication would make it easier to choose an action. However, we will always assume that communication is not possible.
Game theory is considered a normative theory, not a descriptive one. That means we will analyse games concerning the question “What would be the rational solution?” This may not always be what people do in a likewise situation in reality. Such descriptions of real human behaviour are part of the research field of behavioural economics, which is located on the border between Psychology and economics.

The prisoner’s dilemma

Let us become more familiar with the main concepts of game theory by looking at some typical games that are often analyzed. Often, such games are derived from are story or scenario that may happen in the real world and require people to decide between some actions. One such story could be as follows:

Say we have two criminals who are suspected of having committed a crime. The police have some circumstantial evidence, but no actual proof for their guilt. Hence they question the two criminals, who now have to decide if they want to confess or deny the crime. If you are in the situation of one of the criminals, you might think that denying is always better than confessing, but now comes the tricky part: The police propose a deal to you. If you confess while your partner denies, you are considered a crown witness and will not be punished. In this case, you are free to go but your partner will go to jail for six years. Sounds like a good deal, but be aware, that the outcome also depends on your partner’s action. If you both confess, there is no crown witness anymore and you both go to jail for three years. If you both deny, the police can only use circumstantial evidence against you, which will lead to one year in prison for both you and your partner. But be aware, that your partner is offered the same deal. If you deny and he confesses, he is the crown witness and you go to jail for six years. How do you decide?

The game derived from this story is called the prisoner’s dilemma and is a typical example of a game in game theory. We can visualize it as a matrix just like we did with rock-paper-scissors before and in this matrix, we easily see the dilemma the players are in. If both deny, they receive a rather low punishment. But if you assume that your partner denies, you might be tempted to confess, which would prevent you from going to jail. But your partner might think the same, and if you both confess, you both go to jail for longer. Such a game can easily make you go round in circles. We will talk about solutions to this problem in the next chapter of this tutorial. First, let’s consider some more examples.

Bach vs. Stravinsky

Who do you prefer, Bach or Stravinsky? Photo by Sigmund on Unsplash

You and your friend want to go to a concert together. You are a fan of Bach’s music but your friend favors the Russian 20th. century composer Igor Stravinsky. However, you both want to avoid being alone in any concert. Although you prefer Bach over Stravinsky, you would rather go to the Stravinsky concert with your friend than go to the Bach concert alone. We can create a matrix for this game:

You decide for the row by going to the Bach or Stravinsky concert and your friend decides for the column by going to one of the concerts as well. For you, it would be best if you both chose Bach. Your reward would be 2 and your friend would get a reward of 1, which is still better for him than being in the Stravinsky concert all by himself. However, he would be even happier, if you were in the Stravinsky concert together.

Do you remember, that we said players are not allowed to communicate before making their decision? This example illustrates why. If you could just call each other and decide where to go, this would not be a game to investigate with game theory anymore. But you can’t call each other so you just have to go to any of the concerts and hope you will meet your friend there. What do you do?

Arm or disarm?

Make love, not war. Photo by Artem Beliaikin on Unsplash

A third example brings us to the realm of international politics. The world would be a much happier place with fewer firearms, wouldn’t it? However, if nations think about disarmament, they also have to consider the choices other nations make. If the USA disarms, the Soviet Union might want to rearm, to be able to attack the USA — that was the thinking during the Cold War, at least. Such a scenario could be described with the following matrix:

The matrix for the disarm vs. upgrade game.

As you see, when both nations disarm, they get the highest reward (3 each), because there are fewer firearms in the world and the risk of war is minimized. However, if you disarm, while the opponent upgrades, your opponent is in the better position and gets a reward of 2, while you only get 0. Then again, it might have been better to upgrade yourself, which gives a reward of 1 for both players. That is better than being the only one who disarms, but not as good as it would get if both nations disarmed.

The solution?

All these examples have one thing in common: There is no single option that is always the best. Instead, the utility of an action for one player always depends on the other player’s action, which, in turn, depends on the first player’s action and so on. Game theory is now interested in finding the optimal solution and deciding what would be the rational action; that is, the action that maximizes the expected reward. Different ideas on how exactly such a solution looks like will be part of the next chapter in this series.

Summary

Learning about game theory is as much fun as playing a game, don’t you think? Photo by Christopher Paul High on Unsplash

Before continuing with finding solutions in the next chapter, let us recap what we have learned so far.

A game consists of players, that decide for actions, which have a utility or reward.
The utility/reward of an action depends on the other players’ actions.
In static games, players decide for their actions simultaneously. In dynamic games, they take turns.
The prisoner’s dilemma is a very popular example of a game in game theory.
Games become increasingly interesting if there is no single action that is better than any other.

Now that you are familiar with how games are described in game theory, you can check out the next chapter to learn how to find solutions for games in game theory.

References

The topics introduced here are typically covered in standard textbooks on game theory. I mainly used this one, which is written in German though:

Bartholomae, F., & Wiens, M. (2016). Spieltheorie. Ein anwendungsorientiertes Lehrbuch. Wiesbaden: Springer Fachmedien Wiesbaden.

An alternative in English language could be this one:

Espinola-Arredondo, A., & Muñoz-Garcia, F. (2023). Game Theory: An Introduction with Step-by-step Examples. Springer Nature.

Game theory is a rather young field of research, with the first main textbook being this one:

Von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior.

Like this article? Follow me to be notified of my future posts.

Stay Ahead

Explore More Insights

Stay ahead with more perspectives on cutting-edge power, infrastructure, energy, bitcoin and AI solutions. Explore these articles to uncover strategies and insights shaping the future of industries.

F5 to acquire CalypsoAI for advanced AI security capabilities

CalypsoAI’s platform creates what the company calls an Inference Perimeter that protects across models, vendors, and environments. The offers several products including Inference Red Team, Inference Defend, and Inference Observe, which deliver adversarial testing, threat detection and prevention, and enterprise oversight, respectively, among other capabilities. CalypsoAI says its platform proactively

HomeLM: A foundation model for ambient AI

Capabilities of a HomeLM What makes a foundation model like HomeLM powerful is its ability to learn generalizable representations of sensor streams, allowing them to be reused, recombined and adapted across diverse tasks. This fundamentally differs from traditional signal processing and machine learning pipelines in RF sensing, which are typically

Google enlists new ‘free’ data transfer service as the EU Data Act deadline approaches

Google beyond the basic requirement When asked about the rationale behind the introduction of the new service, a Google spokesperson said that the hyperscaler was going “above and beyond what the data act requires by eliminating the cost of in-parallel processing fees for customers in the EU and UK.” In

Cisco’s Splunk embeds agentic AI into security and observability products

AI-powered observability enhancements Cisco also announced it has updated Splunk Observability to use Cisco AgenticOps, which deploys AI agents to automate telemetry collection, detect issues, identify root causes, and apply fixes. The agentic AI updates help enterprise customers automate incident detection, root-cause analysis, and routine fixes. “We are making sure

Strathcona Files Amended Offer to Acquire MEG Energy

Strathcona Resources Ltd. said Thursday it filed an amended offer to acquire all of the outstanding shares of MEG Energy Corp. for 0.80 Strathcona shares per MEG share, in its latest move to win the takeover battle with Cenovus Energy Inc. Cenovus on Aug. 22 said it entered into a definitive arrangement agreement to acquire MEG in a cash-and-stock transaction valued at $7.9 billion, inclusive of assumed debt. Since then, Strathcona said it has been approached by “a substantial portion of the MEG shareholder base” giving feedback that the deal “meaningfully undervalues MEG and allows Cenovus to keep the vast majority of future upside for itself due to its cash-heavy consideration structure”. Strathcona’s amended offer reflects a 10 percent increase to its original offer and equates to an 11 percent premium to the Cenovus-MEG agreement, the company said, adding that feedback on it from MEG shareholders has been “overwhelmingly positive” to date. Strathcona said it “remains ready and willing to engage constructively and in good faith with the MEG board of directors to enter into a supported transaction”. Strathcona also reaffirmed Waterous Energy Fund’s (WEF) willingness to enter into a mutually acceptable lock-up agreement to “assuage any unfounded concerns around future selling of Strathcona shares by WEF,” according to the statement. Under the terms of the Cenovus-MEG agreement, Cenovus will acquire all of the issued and outstanding common shares of MEG for $27.25 per share, which will be paid 75 percent in cash and 25 percent in Cenovus common shares. The transaction is expected to close in the fourth quarter. Last week, Strathcona confirmed its purchase of around 6 million MEG shares for approximately $172.7 million, bringing its total ownership to 14.2% of the issued and outstanding shares. Strathcona said it intends to vote against the resolution to approve the

Oil Holds Steady Amid Russia Sanction Uncertainty

Oil held steady as US threats to sanction Russia’s crude failed to materialize, even as mounting Ukrainian drone attacks increased the prospect that the country’s flows would be disrupted. West Texas Intermediate edged up 0.5% to settle below $63 a barrel, after earlier climbing as much as 2.6%. Ukrainian strikes temporarily suspended operations at Primorsk, the main oil-loading port in the region, and targeted three pumping stations pushing crude to the Ust-Luga hub, a person familiar with the situation said. Those are Russia’s two most important crude-exporting hubs on the Baltic coast. “We expect that Ukraine will step up their attacks on Russian energy infrastructure as a ceasefire is still unlikely, and support prices through destruction of oil supply,” Joe DeLaura and Florence Schmit, energy strategists at Rabobank, said in a note, forecasting WTI at $63 a barrel in late 2025. Adding to the focus on Moscow’s exports, the US will urge its allies in the Group of Seven nations to impose tariffs as high as 100% on China and India for their purchases of Russian oil. Canada, which holds the presidency of the G-7, convened a meeting of the group’s finance ministers on Friday to “discuss further measures to increase pressure on Russia and limit their war machinery,” according to a statement. A lack of developments on the sanctions front led traders to unwind bullish positioning going into the weekend amid speculation of whether Trump would follow through on the tariff threats. Further limiting the rally, the International Energy Agency projected a record oil supply surplus next year. A more pessimistic report from the agency on Thursday followed a decision by the OPEC+ producer group to keep returning idled barrels to the market in October, albeit at a lower rate than previous hikes. With WTI buffeted by the competing

Trafigura Oil Head Says Assets Support Profit

Trafigura Group’s recent investments in refineries and distribution assets have helped support its oil business, even as rivals saw their profitability slide, according to the trader’s head of oil, Ben Luckock. Investments in distribution business Greenergy, the Fos-sur-Mer refinery in France, and several new vessels have “quickly made a material difference to our business performance and knowledge base,” Luckock said in an interview with Bloomberg News in Singapore on Wednesday. His comments come as some of the world’s biggest energy firms from Gunvor Group to Shell Plc bemoan headline-driven price moves that have dented earnings from trading oil after a period of record profits. “It’s been a difficult nine months for parts of the trading community. Many have struggled with the unpredictable nature of policy-driven volatility,” he said. Meanwhile, margins for refining oil have risen amid robust demand for fuels, while product supplies have been crimped by Ukrainian attacks on Russian production facilities. Trafigura moves enough oil daily to supply France three times over and is the world’s biggest metals trader. The company reshuffled its crude and oil products teams last year, with Dan Woodbridge now running crude and Tom Farrant taking on the global gasoline book. Both have had “a strong last few months in the market,” said Luckock. Although assets can provide trading houses with advantageous market positions, the industry has had a mixed record of managing and integrating upstream, processing and distribution businesses. Trafigura, which has previously taken big write-downs on some of its zinc and oil distribution investments, has established a new division to manage all its assets. “The creation of the Asset Division is the single biggest change we’ve made in recent years. It has already had a very positive impact,” Luckock said. The company is under some pressure to keep profitability high to ensure

USA Urges G7 Sanctions on Russian Oil

The US will urge its allies in the Group of Seven to impose tariffs as high as 100% on China and India for their purchases of Russian oil in an effort to convince President Vladimir Putin to end his war in Ukraine. President Donald Trump said on Friday that his patience with Putin was “running out fast” and threatened new economic sanctions. “It’ll be hitting very hard on with sanctions to banks and having to do with oil and tariffs also,” he said in an interview on Fox News. The US will also tell the G-7 countries they should create a legal pathway to seize immobilized sovereign Russian assets and consider seizing or using the principle of those assets to fund Ukraine’s defense, according to a US proposal seen by Bloomberg. The vast majority of the about $300 billion of Moscow’s immobilized assets are in Europe. Brent crude futures extended gains following the report, briefly touching a session high. The euro fell to the day’s low and last traded around the $1.1703 mark early in the New York session. A spokesperson from the White House didn’t immediately respond to a request for comment on the proposals. Separately, senior US officials have floated with European counterparts the idea of gradually seizing those frozen Russia assets to increase the pressure on Moscow to enter into negotiations, according to people familiar with the matter who spoke on the condition of anonymity. Profits generated by the assets are currently being used to provide loans to Ukraine. Canada, which holds the presidency of the G-7, convened a meeting of the group’s finance ministers on Friday to “discuss further measures to increase pressure on Russia and limit their war machinery,” according to a statement. The US proposal calls for 50% to 100% secondary tariffs on China

DOE’s emergency orders create a moral hazard

Jennifer Danis is the federal energy policy director at the Institute for Policy Integrity at New York University School of Law. The U.S. electric grid is a shared, interconnected system. When it fails, it fails for everyone. To prevent this, regulators like the Federal Energy Regulatory Commission and regional grid operators like the Midcontinent Independent System Operator are, among other things, entrusted with a complex, round-the-clock task: ensuring reliability. They administer non-discriminatory markets that serve two-thirds of the U.S. electricity demand, promoting efficiency and reliability by allowing investors to make decisions based on market signals, including higher profits during periods of tighter reliability margins. However, the U.S. Department of Energy is now invoking its emergency authority under Section 202(c) of the Federal Power Act to intervene in these markets. While this power is intended to allow DOE to quickly respond to short-term crises — authorizing orders for up to 90 days — this DOE has demonstrated a willingness to subsequently extend these orders without any reliability entity requesting them or logical terminus, as it did with a Michigan coal power plant that was scheduled to retire in May 2025. This practice raises concerns about the use of an emergency mechanism for what could become a de facto long-term policy. If there were a true energy emergency, the last thing a rational regulator would do is halt energy projects about to supply the grid with low-cost power (like Revolution Wind) and announce policies adding extra-statutory layers of review to other kinds of shovel-ready energy resources simply because they are not this administration’s preferred technologies. While vaguely couched in terms of preventing future outages, DOE’s centralized approach erodes trust in the market system and interferes with the good planning practices that are essential for long-term grid reliability. A departure from historical use

Texas regulators approve 2 Entergy gas plants with $2.4B ‘hard cap’

The Public Utility Commission of Texas on Thursday approved a pair of new gas plants proposed by Entergy, but imposed a “hard cap” on costs of $2.4 billion to protect ratepayers following regulator concerns that the utility did not take steps to ensure the projects were cost effective. The 754-MW Legend Power Station and 453-MW Lone Star Power Station plants are “critical to serve the significant growth in southeast Texas,” Entergy Texas CEO Eliecer Viamontes said in a video statement. The utility expects summer coincident peak load to increase by almost 20% by 2028, driven by new customer loads and industrial expansion. Entergy Texas serves about 524,000 customers in 27 counties, and is connected to the Midcontinent Independent System Operator grid. “Entergy has shown a need for this. I think we want them to proceed,” PUCT Chair Thomas Gleeson said at the open meeting. “But given, kind of, the issues with the process … I think it’s appropriate to apply a hard cost cap consistent with the costs that they anticipate and estimate.” Both facilities are planned to be in service by mid-2028, Entergy Texas said. Entergy Texas did not conduct a request for proposals to determine which projects to pursue, drawing concern from the Texas Industrial Energy Consumers group, the Office of Public Utility Counsel and PUCT staff. Texas law does not require an RFP process for generation projects to receive a certificate of convenience and necessity. Entergy says the cost of the gas plants is about 25% below current market prices, and the utility expects they will produce more than $280 million in net benefits. The PUCT approved the projects consistent with a cost cap described in a memo Gleeson filed Wednesday. “The capital costs for the dispatchable portfolio, inclusive of allowance for funds used during construction, on which

There are 121 AI processor companies. How many will succeed?

The US currently leads in AI hardware and software, but China’s DeepSeek and Huawei continue to push advanced chips, India has announced an indigenous GPU program targeting production by 2029, and policy shifts in Washington are reshaping the playing field. In Q2, the rollback of export restrictions allowed US companies like Nvidia and AMD to strike multibillion-dollar deals in Saudi Arabia. JPR categorizes vendors into five segments: IoT (ultra-low-power inference in microcontrollers or small SoCs); Edge (on-device or near-device inference in 1–100W range, used outside data centers); Automotive (distinct enough to break out from Edge); data center training; and data center inference. There is some overlap between segments as many vendors play in multiple segments. Of the five categories, inference has the most startups with 90. Peddie says the inference application list is “humongous,” with everything from wearable health monitors to smart vehicle sensor arrays, to personal items in the home, and every imaginable machine in every imaginable manufacturing and production line, plus robotic box movers and surgeons. Inference also offers the most versatility. “Smart devices” in the past, like washing machines or coffee makers, could do basically one thing and couldn’t adapt to any changes. “Inference-based systems will be able to duck and weave, adjust in real time, and find alternative solutions, quickly,” said Peddie. Peddie said despite his apparent cynicism, this is an exciting time. “There are really novel ideas being tried like analog neuron processors, and in-memory processors,” he said.

Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist (and coming soon free Data Center Intern listing). Data Center Critical Facility Manager Impact, TX There position is also available in: Cheyenne, WY; Ashburn, VA or Manassas, VA. This opportunity is working directly with a leading mission-critical data center developer / wholesaler / colo provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations (enterprise and hyperscale customers). This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer New Albany, OH This traveling position is also available in: Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; Cedar Rapids, IA; Phoenix, AZ; Dallas, TX or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs. *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Data Center Engineering Design ManagerAshburn, VA This opportunity is working directly with a leading mission-critical data center developer /

Modernizing Legacy Data Centers for the AI Revolution with Schneider Electric’s Steven Carlini

As artificial intelligence workloads drive unprecedented compute density, the U.S. data center industry faces a formidable challenge: modernizing aging facilities that were never designed to support today’s high-density AI servers. In a recent Data Center Frontier podcast, Steven Carlini, Vice President of Innovation and Data Centers at Schneider Electric, shared his insights on how operators are confronting these transformative pressures. “Many of these data centers were built with the expectation they would go through three, four, five IT refresh cycles,” Carlini explains. “Back then, growth in rack density was moderate. Facilities were designed for 10, 12 kilowatts per rack. Now with systems like Nvidia’s Blackwell, we’re seeing 132 kilowatts per rack, and each rack can weigh 5,000 pounds.” The implications are seismic. Legacy racks, floor layouts, power distribution systems, and cooling infrastructure were simply not engineered for such extreme densities. “With densification, a lot of the power distribution, cooling systems, even the rack systems — the new servers don’t fit in those racks. You need more room behind the racks for power and cooling. Almost everything needs to be changed,” Carlini notes. For operators, the first questions are inevitably about power availability. At 132 kilowatts per rack, even a single cluster can challenge the limits of older infrastructure. Many facilities are conducting rigorous evaluations to decide whether retrofitting is feasible or whether building new sites is the more practical solution. Carlini adds, “You may have transformers spaced every hundred yards, twenty of them. Now, one larger transformer can replace that footprint, and power distribution units feed busways that supply each accelerated compute rack. The scale and complexity are unlike anything we’ve seen before.” Safety considerations also intensify with these densifications. “At 132 kilowatts, maintenance is still feasible,” Carlini says, “but as voltages rise, data centers are moving toward environments where

Google Backs Advanced Nuclear at TVA’s Clinch River as ORNL Pushes Quantum Frontiers

Inside the Hermes Reactor Design Kairos Power’s Hermes reactor is based on its KP-FHR architecture — short for fluoride salt–cooled, high-temperature reactor. Unlike conventional water-cooled reactors, Hermes uses a molten salt mixture called FLiBe (lithium fluoride and beryllium fluoride) as a coolant. Because FLiBe operates at atmospheric pressure, the design eliminates the risk of high-pressure ruptures and allows for inherently safer operation. Fuel for Hermes comes in the form of TRISO particles rather than traditional enriched uranium fuel rods. Each TRISO particle is encapsulated within ceramic layers that function like miniature containment vessels. These particles can withstand temperatures above 1,600 °C — far beyond the reactor’s normal operating range of about 700 °C. In combination with the salt coolant, Hermes achieves outlet temperatures between 650–750 °C, enabling efficient power generation and potential industrial applications such as hydrogen production. Because the salt coolant is chemically stable and requires no pressurization, the reactor can shut down and dissipate heat passively, without external power or operator intervention. This passive safety profile differentiates Hermes from traditional light-water reactors and reflects the Generation IV industry focus on safer, modular designs. From Hermes-1 to Hermes-2: Iterative Nuclear Development The first step in Kairos’ roadmap is Hermes-1, a 35 MW thermal demonstration reactor now under construction at TVA’s Clinch River site under a 2023 NRC license. Hermes-1 is not designed to generate electricity but will validate reactor physics, fuel handling, licensing strategies, and construction techniques. Building on that experience, Hermes-2 will be a 50 MW electric reactor connected to TVA’s grid, with operations targeted for 2030. Under the agreement, TVA will purchase electricity from Hermes-2 and supply it to Google’s data centers in Tennessee and Alabama. Kairos describes its development philosophy as “iterative,” scaling incrementally rather than attempting to deploy large fleets of units at once. By

NVIDIA Forecasts $3–$4 Trillion AI Market, Driving Next Wave of Infrastructure

Whenever behemoth chipmaker NVIDIA announces its quarterly earnings, those results can have a massive influence on the stock market and its position as a key indicator for the AI industry. After all, NVIDIA is the most valuable publicly traded company in the world, valued at $4.24 trillion—ahead of Microsoft ($3.74 trillion), Apple ($3.41 trillion), Alphabet, the parent company of Google ($2.57 trillion), and Amazon ($2.44 trillion). Due to its explosive growth in recent years, a single NVIDIA earnings report can move the entire market. So, when NVIDIA leaders announced during their August 27 earnings call that Q2 2026 sales surged 56% to $46.74 billion, it was a record-setting performance for the company—and investors took notice. Executive VP & CFO Colette M. Kress said the revenue exceeded leadership’s outlook as the company grew sequentially across all market platforms. She outlined a path toward substantial growth driven by AI infrastructure. Foreseeing significant long-term growth opportunities in agentic AI and considering the scale of opportunity, CEO Jensen Huang said, “Over the next 5 years, we’re going to scale into it with Blackwell [architecture for GenAI], with Rubin [successor to Blackwell], and follow-ons to scale into effectively a $3 trillion to $4 trillion AI infrastructure opportunity.” The chipmaker’s Q2 2026 earnings fell short of Wall Street’s lofty expectations, but they did demonstrate that its sales are still rising faster than those of most other tech companies. NVIDIA is expected to post revenue growth of at least 42% over the next four quarters, compared with an average of about 10% for firms in the technology-heavy Nasdaq 100 Index, according to data compiled by Bloomberg Intelligence. On August 29, two days after announcing their earnings, NVIDIA stocks slid 3% and other chip stocks also declined. This came amid a broader sell-off after server-maker Dell, a customer of those chipmakers,

Cologix and Lambda Debut NVIDIA HGX B200 AI Clusters in Columbus, Ohio

In our latest episode of the Data Center Frontier Show, we explore how powerhouse AI infrastructure is moving inland—anchored by the first NVIDIA HGX B200 cluster deployment in Columbus, Ohio. Cologix, Lambda, and Supermicro have partnered on the project, which combines Lambda’s 1-Click Clusters™, Supermicro’s energy-efficient hardware, and Cologix’s carrier-dense Scalelogix℠ COL4 facility. It’s a milestone that speaks to the rapid decentralization of AI workloads and the emergence of the Midwest as a serious player in the AI economy. Joining me for the conversation were Bill Bentley, VP Hyperscale and Cloud Sales at Cologix, and Ken Patchett, VP Data Center Infrastructure at Lambda. Why Columbus, Why Now? Asked about the significance of launching in Columbus, Patchett framed the move in terms of the coming era of “superintelligence.” “The shift to superintelligence is happening now—systems that can reason, adapt, and accelerate human progress,” Patchett said. “That requires an entirely new type of infrastructure, which means capital, vision, and the right partners. Columbus with Cologix made sense because beyond being centrally located, they’re highly connected, cost-efficient, and built to scale. We’re not chasing trends. We’re laying the groundwork for a future where intelligence infrastructure is as ubiquitous as electricity.” Bentley pointed to the city’s underlying strengths in connectivity, incentives, and utility economics. “Columbus is uniquely situated at the intersection of long-haul fiber,” Bentley said. “You’ve got state tax incentives, low-cost utilities, and a growing concentration of hyperscalers and local enterprises. The ecosystem is ripe for growth. It’s a natural geography for AI workloads that need geographic diversity without sacrificing performance.” Shifting—or Expanding—the Map for AI The guests agreed that deployments like this don’t represent a wholesale shift away from coastal hyperscale markets, but rather the expansion of AI’s footprint across multiple geographies. “I like to think of Lambda as an AI hyperscaler,”

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle