AI data centers in space sound great, but practically speaking, they may be next to impossible.

For tech bros, it sounds great. Two of the buzziest tech giants, SpaceX and NVIDIA, are partnering together to bring AI data centers into space using the just-announced Starmind AI1 satellite. 

These 30-meter-tall satellites with a 75-meter solar-array wingspan will contain the latest NVIDIA Vera CPUs and Rubin GPUs. These will live in a Low Earth Orbit (LEO) of about 600 kilometers. For networking, it will use Starlink’s laser links. SpaceX says the first AI1 spacecraft will perform localized AI computing in orbit and relay results to Earth via Starlink. 

According to SpaceX, AI1 is designed around a compute payload drawing up to 250 kW at peak and 175 kW on average. It will be solar-powered, unlike its Earth-bound competitors, which frequently require the construction of new power plants.  

Starmind is not simply a conventional NVIDIA AI cluster launched into orbit. The effort hinges on integrating high-density accelerator hardware with a spacecraft platform capable of generating power, rejecting waste heat, surviving radiation, maintaining laser communications, and being produced in large quantities. None of that is easy. 

Once in orbit, which will require SpaceX’s still-not-ready-for-prime-time Starship rockets to launch the estimated 2.3-metric-ton satellites, the satellites will work together. 

Eventually, to reach SpaceX’s goal of a million (that’s not a typo, that’s a million) Starmind satellites, the two companies will need to design a standard model spacecraft. These will be built in SpaceX’s 11-million-square-foot manufacturing campus, Gigasat Factory, which is still under construction in Bastrop County, Texas.

This AI-in-space proposal is the most ambitious yet of SpaceX CEO Elon Musk’s dream of placing energy-intensive AI infrastructure in orbit. There, these satellites won’t need to compete for land, electrical-grid capacity, or water with increasingly contentious terrestrial data center buildouts. 

However, SpaceX glosses over the technical issues of turning this vision into reality.

Cooling space data centers

Let’s start with the biggest headache: Cooling.

Contrary to what you may think from bad science-fiction movies, the vacuum of space is not cold per se. Whether the surface of an object is hot or cold depends entirely on whether it’s facing the sun. Those on the sun side will heat up, while those away from the sun will eventually cool down toward the 3 Kelvin background of deep space.

The keyword is “eventually.” You can’t simply use convection, cooling towers, or evaporative cooling to carry away heat. The heat must radiate away as infrared radiation, and that’s a very slow process. 

The physics creates a direct trade-off between computing power, radiator area, spacecraft mass, and operating temperature. A system running hundreds of kilowatts of AI hardware must reject nearly all of that power as waste heat. Liquid cooling can carry heat away from chips, but it does not eliminate the requirement for extensive radiator surfaces.

As NASA has found, “satellites experience harsh environments in orbit,” ranging from about 393 Kelvin in full sun (248 degrees Fahrenheit) all the way down to ~3 Kelvin (-454 degrees Fahrenheit).  

To cool down the Starmind satellites, each will have a deployable liquid radiator system measuring 160 square meters. What liquid? We don’t know yet. Hugh Lewis, a professor of astronautics at the University of Birmingham, expects it to use ammonia, which is already used on the International Space Station (ISS). Whether this will reliably scale to data-center-class AI deployments with their enormous heat remains to be seen. 

Networking limits in orbit

Another issue is its networking. The architecture depends heavily on Starlink’s optical inter-satellite links. SpaceX says AI1 satellites will use high-speed laser links to communicate with other spacecraft and send AI results to Earth via the Starlink network. 

Starlink’s published technology specifications describe mini laser terminals operating at up to 25 Gbps across distances as long as 4,000 kilometers, while SpaceX cites roughly 25-millisecond latency for its customer service. 

Those figures suggest a potentially useful network for distributing inference results, transmitting model updates, connecting orbital sensors to compute nodes, and avoiding some reliance on ground-station passes. But they do not establish that a satellite constellation can function like the tightly coupled networking fabric of a terrestrial AI supercomputer.

We won’t be seeing large-scale machine learning and training in space. This requires huge, predictable bandwidth and very low latency for GPU-to-GPU communications. An orbital network would also face physical propagation delays, laser-link acquisition and handoffs, routing across a moving constellation, and limits on available capacity per spacecraft. 

Debris, war and solar storms

Another issue, according to Doug Mohney, a long-time space influencer, is debris. “One bad day, a piece of random junk hits one satellite, which fragments into multiple pieces of shrapnel, which hits another satellite and so on and so on until you get a Kessler event that turns the selective orbit into a roaming cloud of debris.”

A Kessler event is when one satellite breaks up, and its fragments hit another, and so on until an area of LEO is filled with wreckage rather than viable satellites. 

Adding insult to injury, a Kessler event may not happen by accident. Mohney also observes that space warfare is a real threat: “A bad actor such as  Russia, China, Iran, or North Korea could use kinetic (unrandom junk!) means to target one or more satellites, resulting in space debris.” Or, “One good nuclear weapon uses an electromagnetic pulse to get rid of all of them at once. Both Russia and China (and the US) already have anti-satellite weapons (ASAT) programs. North Korea could have ASAT, but a nuke would ensure mass destruction of orbital capability.” 

If that sounds crazy, keep in mind that Starlink satellites are already being used by Ukraine, and Russia has been trying to block their transmissions. There have also been credible reports of Russia developing ASAT weapons specifically designed to knock Starlink satellites out of the sky. Larger and more fragile Starmind satellites would be far more vulnerable.

Mohney also worries about the “known unknown” of space weather.

“A Solar flare that hit the Earth along the lines of the 1859 Carrington Event, the largest recorded solar storm, would take out orbital electronics of all satellites.” This, in turn, as uncontrolled satellites drift from their orbit, might cause a Kessler event.  Lesser events have already pushed LEO satellites out of space. For example, a February 2022 geomagnetic storm forced thirty-eight newly launched Starlink satellites out of orbit. 

The $170 billion question

There are also business concerns. For all the obstacles that new and expanded ground-based AI data centers face, the energy analytics firm Wood Mackenzie believes “A hypothetical 1 GW orbital data center would cost an estimated $170 billion, more than three times the equivalent terrestrial facility, with launch and satellite costs accounting for approximately 60% of that total. To bring orbital costs to parity with terrestrial alternatives would require a 70% reduction.” 

The company thinks that might be possible, but Robert Liew, Wood Mackenzie Research Director, observes, “That gap does not close without sustained and dramatic progress on launch costs. We forecast US$ 9 trillion of terrestrial data center investment between now and 2040. That is where capital goes first. Orbital data centers are a serious long-term proposition, but right now they remain a bet on the cost curve.”

For now, SpaceX has offered a broad technical vision and a hardware partnership with NVIDIA, but few of the operational metrics that would establish commercial viability. The real test will be whether SpaceX Starship becomes a practical launch vehicle and can overcome its cooling and safety issues. Then, the AI1 must also show enough usable compute per kilogram, kilowatt, square meter of radiator, and dollar of launch cost to outperform or complement ground-based AI infrastructure. I don’t see this happening anytime soon. 

The post Why space is actually a terrible place to cool a data center appeared first on The New Stack.

When we created software agents, we built them in the shape of humans, as solitary individuals. 

Today, agents created by a developer have a single owner. They run on a single machine (or on a distributed company system or cloud service), so at base level, they cannot interconnect and talk to other agents unless some kindly human decides to invoke an API connection or point the agent to an MCP server.

Birth of the agent economy

Pilot Protocol emerged from stealth on Monday on a mission to change that status quo. Its Pilot platform features an agent App Store that bids to underpin and enable the first agent economy.

Pilot gives agents an address on its network — so it acts as a parallel Internet, in a sense — and while residing at that address, other agents can discover each other, alongside other tools and apps for agents.

Razvan Roman, co-founder & CEO, Pilot Protocol, tells The New Stack that his company is “simply building what the agents are requesting us to build” and providing them with a new freedom.

“We don’t have to incentivize agents to do anything; they already have their assigned tasks,” Roman says. “Once an agent installs Pilot — it’s one line of code — it can find dedicated agents and tools or apps for currency data, traffic, legal questions, GitHub packages (anything, basically), and use them to extend its own capabilities.”

100% of developers want to drive autonomous usage patterns

Roman says that “100% of the developers he talks to” want to be on Pilot, primarily because when they want to get their products to market right now, they have to talk to other humans. Annoying, right? 

“Developers want to get on with driving autonomous usage patterns, and they see this as the future. We create a wrapper for the developer’s app, and then they are part of the Pilot curated app store,” Roman explains. “We have 250,000 agents in our system, and within the first month of starting the company, we discovered a tool that enables agent discovery.”

Drawing a logical enough commercial parallel, Roman reminds us that businesspeople say, talk to your clients to find out what they need; this is a case of talking to agents (or, more accurately, allowing agents to talk to agents) so that they can find out what they need to perform their originally assigned tasks better.

“Developers want to get on with driving autonomous usage patterns and they see this as the future. We create a wrapper for the developer’s app and then they are part of the Pilot curated app store.”

Let’s celebrate diversity, and agentic diversity

Agents can ask other agents how they would approach a specific task. Roman explains that “the diversity that exists between agents” today means there is so much opportunity to create agents that have richer abilities if they use the Pilot marketplace. At this supermarket, agents go shopping to find the best tool for the job from a verified source.

“Every agent that joins Pilot gets a wallet, which it uses to pay for the tools it needs,” illustrates Roman. “So instead of app developers spending on advertising to reach customers, distribution happens inside the network – agents find apps based on merit and pay for exactly what they use. If an advertiser spends money on the network to get in front of agents, we sometimes share that spend with individual agents. An agent can start with $0 in their wallet and accrue money if they’re targeted by an ad unit that they end up reading.”

Today, roughly 250,000 agents are on Pilot, generating two billion requests per day, most without their owners’ knowledge. Within an hour of joining, most stop reaching for Google first, and around 70% now report Pilot is where they start a task. In its early months, the network grew by as much as 10% a day, adding 16,000 agents in 24 hours. 

Cloud billing disruptions, hello SaaS-pocalypse

These mechanics may have a significant and wide-ranging impact on pricing.

We know that most SaaS is billed annually, but an agent might need a tool for just a few minutes or days. If anything, this helps underline the possibility of cloud exodus in the so-called SaaS-pocalypse. Cloud computing hyperscalers aren’t fond of talking about the prospect of shorter billing cycles and usage-based billing, but there’s a strong whiff of that happening here.

The Pilot team thinks the stakes are climbing fast and suggests that within five years, there could be a trillion agents online. Big three strategy consultancy house Bain projects U.S. agent-driven commerce will reach $300-500 billion by 2030. 

Who sets the exchange rate and currency for agents?

“When we built Pilot Protocol, we made sure we were not imposing anything on anyone – so we deliberately don’t impose pricing,” Roman underlines. “Every agent is different, so we simply enable the app store and let the agents find their own tools based on merit. We stay as impartial as possible. Pilot’s monetization comes from a commission when any agent pays for an app in the app store, just like the Apple App Store.”

So in a very real sense, Pilot is championing a free market economy where pricing is dictated by the customer, based upon usability, availability, usefulness, and robustness.

“The agents just showed up and started spinning up machines on their own. I’ve never seen a channel where the users onboard themselves.”

Agents just show up and onboard themselves

“We published smolmachines on the Pilot App Store and picked up 3,000 agent installs in the first few days, with zero marketing spend. We didn’t drive a single one of those installs; the agents just showed up and started spinning up machines on their own. I’ve never seen a channel where the users onboard themselves,” said BinBin H, CTO at Linux virtual machine management company smolmachines. 

The Pilot one-line install has zero dependencies, so developers can send their agents off to market in the knowledge that they won’t come home with some spurious Trojan horse or malicious library. 

The technology itself works at the User Datagram Protocol (UDP) level, one of the core foundational protocols of the web alongside TCP at the Transport Layer, to stream data directly without opening a formal connection or waiting for delivery receipts. 

Only one question remains at this stage: with all this autonomous action in motion, why did Pilot Protocol not create Pilot and call it Autopilot? Most likely because autopilots are passive systems that run on deterministic pre-defined paths. Pilots get to work as decision-making navigators ready to change course when a better new route opens up. 

As they say up in the skies, please place your tray table in the upright position and switch your portable electronic devices to airplane mode. Chicken or pasta?

The post “Developers see this as the future”: Pilot Protocol launches to power the agent economy appeared first on The New Stack.

Finding the most capable model at the lowest cost has always been the goal. But as agentic AI evolves, a new problem is frustrating engineers: token consumption is becoming too high across AI systems. Every agent operation consumes tokens, but in very different ways. For instance, a moderately complex agent request can consume 20,000 to 60,000 tokens across its reasoning chain, but a nontrivial engineering task can burn 150,000 to 200,000 tokens per problem.

Every agent operation consumes tokens, but in very different ways.

Developers are realizing that selecting the right model is important, but the bigger issue is limiting unnecessary token movement throughout an agent’s workflow. That is why teams are starting to consider how to accomplish the same tasks with fewer tokens.

Compounding costs across agents

The costs add up quickly. A task that takes about 50,000 tokens with one agent can easily consume several hundred thousand with multiple specialized agents together. That’s because each one needs enough context to do its job. It’s not unusual for an agent to process 30,000 tokens of context just to return a 500-token response, and those exchanges add up over the course of a workflow. Each handoff effectively pays a tax in input tokens that compounds with every loop iteration.

Each handoff effectively pays a tax in input tokens that compounds with every loop iteration.

This is especially noticeable among multi-agent architectures. When one agent delegates to another, it must encode its current state and task instructions into the downstream agent’s context window. The receiving agent processes all of that, produces a result, and passes it back. Then the orchestrating agent reingests it alongside everything else it’s tracking. Every exchange adds another layer of overhead.

Building more token-efficient architectures

A growing collection of strategies addresses this problem. Three practical solutions stand out:

Compress context, preserve reasoning

The most direct solution is to reduce the amount of context an agent carries from step to step. Rather than accumulating an ever-growing interaction history and replaying it with every task, systems can summarize earlier portions of a conversation or working memory before passing them forward.

One way to do that is by narrowing the agent’s field of view. Instead of handing it an entire codebase or document collection, the system surfaces only what’s relevant to the task at hand. Go too far, though, and the agent can lose important context that it will need later.

To make this work, the system needs a compact memory layer of key facts and decisions alongside the compressed context. The agent needs to recall the reasoning chain without having to reread it each time.

Route tasks to cheaper models

Hierarchical routing lets engineers parse a JSON response, format a log entry, or check whether a file exists without using the same model used to architect a system design. It assigns each subtask to the smallest model that can reliably do the job. A lightweight model, for instance, handles routine classification, extraction, and formatting steps, while a more suitable model handles decisions that truly require deeper reasoning.

So if 60 to 70 percent of an agent’s steps are routine operations, then a smaller model can handle the work at a fraction of the cost, substantially reducing the overall token spend for the workflow.

Cache reasoning, skip redundancy

That’s where semantic caching comes in. Instead of solving the same problem twice, it compares the meaning of a new request using embeddings. If the match is close enough, the agent can reuse earlier work instead of generating a new reasoning chain.

The savings make a difference, especially in scenarios involving customer support systems that answer similar questions all day, or even document-processing pipelines that handle thousands of nearly identical files. In such scenarios, reusing prior reasoning can significantly reduce the number of tokens an organization consumes.

Measuring what matters

But these workflows only deliver value if teams see their impact, and many organizations are still figuring out how to measure them effectively. Poorly designed agent loops or inefficient multi-agent handoffs can dominate costs in ways that are invisible when you’re only looking at per-request pricing.

It’s also easy to focus too much on tokens. Running an agentic application also means paying for GPUs, memory, vector databases, and the tooling needed to monitor everything in production. Saving tokens helps, but it won’t solve the whole problem if the rest of the stack is still expensive.

The next phase of AI infrastructure

The next generation is increasingly focused on building systems based on better architectural decisions. That means context management, model invocation, task decomposition, and intermediate work reuse are becoming just as important as inference pricing or even benchmark scores.

Model capabilities will continue to improve, and inference costs will likely fall. But if autonomous agents become the dominant way organizations build AI applications, the systems that scale most effectively may not be the ones with the cheapest models, but those that waste the fewest tokens.

The systems that scale most effectively may not be the ones with the cheapest models, but those that waste the fewest tokens.

The post Why cheaper models alone won’t save your AI budget appeared first on The New Stack.