Orbital Estimate

About Orbital Estimate

A publication for the geopolitics of space and space accessories. orbitest.substack.com (https://orbitest.substack.com?utm_medium=podcast)

Publisher

Gene Botkin

Category

news · news

Language

en

Explicit

No

First episode

5 May 2026

Latest episode

20 Oct 2026

Latest episodes

22 episodes in the feed.

1. 

   20 Oct 2026

   The Moon as Military Terrain

   The Moon will become military terrain before it becomes a conventional battlefield. Communications relays will determine whether distant spacecraft can receive commands. Navigation services will shape how safely they move. Surveillance systems will decide whether unusual behavior is detected early enough to interpret it. The supporting architecture is already taking form. NASA’s LunaNet Interoperability Specification (https://www.nasa.gov/directorates/somd/space-communications-navigation-program/lunanet-interoperability-specification/) defines common interfaces for communications and navigation services around the Moon. China’s Queqiao-2 relay satellite (https://www.cnsa.gov.cn/english/n6465652/n6465653/c10489434/content.html) supports far-side exploration and planned activity near the lunar south pole. The United States Space Force is developing the Oracle-M pathfinder (https://www.ssc.spaceforce.mil/Newsroom/Article/4176371/oracle-m-hot-fire-test-a-major-milestone-in-cislunar-space-situational-awarenes) to improve tracking across cislunar space. None of these programs constitutes a lunar weapons base. Their military importance arises from the access they provide and the dependencies they create. A state that supplies reliable communications or trusted position data can shape how other actors operate without claiming territory on the lunar surface. Washington should preserve an open lunar architecture while identifying defense missions that require independent support. It should also establish strong security requirements for commercial providers. The first meaningful military boundary near the Moon may never be visible from its surface. It may be encoded inside the receiver that decides which network a spacecraft trusts. Cislunar Space as Strategic Geography Cislunar space is often described as the region between Earth and the Moon. That definition is serviceable, but it hides the character of the operating environment. The region is governed by the gravitational relationship between two moving bodies. Spacecraft follow curved paths shaped by both. Terrestrial military language must therefore be used with care. An army can occupy a pass or fortify a hill. A spacecraft cannot hold an orbital point in the same manner because orbital motion is continuous. Even a vehicle operating near a useful gravitational region must manage its trajectory. The Earth-Moon Lagrange regions still possess strategic value. They are locations where the gravitational relationship between Earth and the Moon permits useful orbital patterns. The L2 region beyond the lunar far side is especially important for communications. A relay placed in a suitable orbit near that region can maintain contact with far-side missions while retaining a path back toward Earth. The relay does not own the far side. It can still become indispensable to activity there. The Moon blocks a direct radio path between Earth and a far-side lander. Without a relay, the lander may continue to function internally while losing its connection with human operators. This is strategic geography created through relationships rather than borders. A useful orbit resembles a sea lane more than a plot of land. A relay resembles a port whose value comes from the traffic it supports. The Space Force has already acknowledged that traditional surveillance methods face new problems beyond geosynchronous orbit. Guardians assigned to the 19th Space Defense Squadron (https://www.petersonschriever.spaceforce.mil/Newsroom/News/Display/Article/3510548/19th-space-defense-squadron-talks-astronomy-space-surveillance-with-naval-postg/) have discussed the difficulty of tracking objects in this extended region. Cislunar geography is vast. The Moon can obstruct observation, while changing illumination complicates optical tracking. The actor that understands this geometry best will enjoy more than scientific prestige. It will gain time to recognize unusual movement and more confidence when interpreting another spacecraft’s intent. The Infrastructure That Creates Access Sustained lunar activity requires dependable communications. It also requires accurate position data. An individual mission can carry specialized equipment for both tasks, but shared services reduce the burden placed on each spacecraft. NASA describes LunaNet as a framework rather than a single government-owned constellation. Under its published specification, public agencies or commercial companies could provide compatible services. A user would connect through common interfaces rather than designing a private communications system for every mission. NASA’s Lunar Communications Relay and Navigation Systems program (https://www.nasa.gov/goddard/esc/lcrns/) applies this model to future lunar operations. Interoperability allows users to receive service from more than one provider. In principle, this can improve resilience because the failure of one node need not isolate a mission. Shared infrastructure also produces dependence. Early missions may adopt LunaNet-compatible services because they reduce cost. Later spacecraft will be designed around the available network. Once the receiver and mission software assume that service exists, losing it can cripple operations even when the vehicle remains physically intact. This is how technical architecture acquires political weight. The choice of communications protocol appears narrow when engineers make it. Years later, that choice may determine which coalition can support the spacecraft during a crisis. Standards can divide cislunar space before formal boundaries emerge. A vehicle designed around an American-led framework will enter one network of providers. A spacecraft designed around Chinese services may enter another. Some users may operate across both systems, but compatibility will depend upon political decisions as much as engineering. The resulting division will be difficult to see. A border drawn through software has no fence, yet it can determine who receives the signal. China’s Developing Lunar Architecture China’s Queqiao program demonstrates how support architecture can extend a nation’s reach. Queqiao-2 was launched in 2024 to relay communications for missions on the lunar far side. It also supports China’s wider plans near the south pole. The system serves legitimate exploration goals. Far-side operations require a relay because the Moon blocks direct communication with Earth. Still, the same capability establishes experience that can support later activity. China is also organizing the International Lunar Research Station with Russia and additional partners. The China National Space Administration’s description (https://www.cnsa.gov.cn/english/n6465652/n6465653/c10670178/content.html) includes Earth-Moon transportation and energy supply. It also includes communication and navigation under a centralized control structure. These features do not prove that the station conceals a military purpose. A large scientific program requires coordination. The strategic issue is who supplies that coordination and which partners become dependent upon it. A smaller space power may lack the funds to build an independent lunar relay. Joining the Chinese framework offers access at a lower cost. Once its spacecraft is designed around Chinese services, withdrawal becomes expensive. That dependence may later influence diplomacy. A partner whose scientific program relies upon Beijing may hesitate to support measures that place its access at risk. China would not need to issue an explicit threat. The possibility of losing service can discipline behavior by itself. The Department of Defense’s 2025 report on Chinese military power (https://media.defense.gov/2025/Dec/23/2003849070/-1/-1/1/ANNUAL-REPORT-TO-CONGRESS-MILITARY-AND-SECURITY-DEVELOPMENTS-INVOLVING-THE-PEOPLES-REPUBLIC-OF-CHINA-2025.PDF) places these programs within Beijing’s wider cislunar ambitions. China’s success in returning far-side lunar samples shows that it can execute complex operations across the Earth-Moon system. The competition will therefore concern membership as much as hardware. The system that offers dependable service at a tolerable cost will attract users. Speeches about leadership will matter less than whether a relay works when a lander descends behind a ridge. Space Domain Awareness Beyond GEO Military activity near the Moon begins with awareness. A state cannot protect its spacecraft if it cannot track nearby objects. It cannot attribute interference if it lacks a record of ordinary activity. Surveillance systems designed for Earth orbit are poorly suited to the full cislunar region. The volume is immense. Objects appear faint at great distance. Earth-based telescopes also face periods when geometry or lighting prevents useful observation. Oracle-M is intended to develop a better picture. Space Systems Command describes the mission as a pathfinder for persistent situational awareness. It will test tracking and trajectory estimation for cislunar objects while using commercial ground support. The Space Force began developing relevant experience during Artemis I. Space Delta 2 used the mission to test cislunar tracking tactics (https://www.spaceforce.mil/News/Article-Display/Article/3244437/delta-2-leverages-space-domain-awareness-in-support-of-artemis-i/) while NASA’s Orion spacecraft traveled around the Moon. Such awareness creates decision time. Suppose an unfamiliar spacecraft begins moving toward an American relay. The movement might reflect a benign mission plan. It could also signal preparation for interference. Reliable tracking allows operators to reconstruct the approach before choosing a response. Surveillance also supports diplomacy. A government accusing another state of threatening a lunar asset will need evidence. A reconstructed trajectory gives the claim more weight than an assertion issued after contact is lost. Intent will remain difficult to determine. Spacecraft may approach the same region because their missions require similar orbits. Close movement alone is not aggression. Sparse observations could cause routine activity to appear hostile, especially during a wider political crisis. Rules of behavior could reduce this risk, but experience must come first. States need to learn what ordinary cislunar operations look like before they can define a dangerous departure from them. Dual-Use Networks and Cyber Vulnerability Lunar infrastructure will often be dual-use. A commercial relay may serve a scientific rover during ordinary operations and support a defense spacecraft during crisis. Its legal ownership will not erase its military value. The most accessible attack surface may be on Earth. A relay depends upon ground software and administrative accounts. An intruder who compromises the ground segment could interfere with scheduling or alter navigation data without physically attacking the spacecraft. Subtle disruption may offer greater strategic value than destruction. A short outage during a landing could be blamed on equipment failure. Corrupted timing data might push a vehicle away from its planned path while leaving no dramatic debris field. This ambiguity complicates deterrence. The victim may suspect hostile action without possessing enough evidence for attribution. By the time investigators determine what occurred, the operational opportunity may have passed. The Space Force’s Space Doctrine Note on operations (https://media.defense.gov/2022/Feb/02/2002931717/-1/-1/0/SDN%20OPERATIONS%2025%20JANUARY%202022.PDF) emphasizes preserving freedom of action in space. Extending that objective to cislunar space requires protection across the full service chain. Authentication is central. A lunar user must know that a navigation message came from a trusted source. It also needs an independent means to recognize corrupted position data. Redundant access offers little protection if every provider depends upon the same compromised ground service. Commercial contracts should address security before defense users become dependent upon the network. Requirements must cover service during geopolitical crisis. A provider that can disconnect at the first diplomatic squall offers convenience rather than military reliability. Competing Systems of Lunar Order LunaNet and the International Lunar Research Station represent competing foundations for future lunar participation. They should not be treated as clean territorial blocs. Some countries may seek access to both. The competition will still shape political order. China’s 2021 space white paper (https://www.cnsa.gov.cn/english/n6465684/n6760328/n6760329/c6813160/content.html) presents the International Lunar Research Station as a focus for cooperation. NASA describes LunaNet as an open cooperative architecture (https://www.nasa.gov/centers-and-facilities/goddard/lunanet-empowering-artemis-with-communications-and-navigation-interoperability/) built around common standards. Each model offers an entry point for states that cannot fund complete lunar systems. Participation creates relationships among space agencies and contractors. Those relationships may later support the exchange of tracking data during crisis. Dependable service will decide much of the competition. Smaller powers will choose the framework that gives their missions the best chance of success. Reliability can convert a technical standard into political influence without fanfare. Infrastructure becomes powerful when users stop noticing it. Lunar order will therefore emerge through operating practice before law settles every dispute. A coalition that has used a region for years will resist exclusion. Routine presence creates expectations about access, even when no state claims sovereignty. Policy and Defense Recommendations The United States should identify which lunar services could become indispensable to defense missions before spacecraft are designed around them. Changing providers after launch may be impossible if the receiver lacks compatible hardware. Washington does not need a separate military copy of every civil service. That approach would waste money while reducing the commercial demand needed to sustain the wider network. Lower-risk users can accept civil support when backup service exists. Consequential defense missions should retain independent command access. Commercial providers serving lunar missions need common security requirements. Ground systems deserve the same attention as spacecraft. Contracts should define expected service during crisis and establish how government users receive warning of a compromise. Cislunar tracking should draw upon distributed sensors. Government observations can be checked against commercial data when the two sources remain genuinely independent. Multiple perspectives also reduce the periods when geometry hides an object. Policy leaders should seek early protection for emergency communications and crew-safety services. Interfering with those signals should be treated as dangerous behavior regardless of who owns the transmitter. This narrow principle is more credible than pretending every dual-use network can remain outside military competition. The American-led framework must also be attractive. Open standards create an opportunity, but service quality secures adoption. A coalition will form around the network that works on an inconvenient Tuesday. Flags still cannot route packets. The Boundary Inside the Receiver The Moon’s near-term military value lies in supporting architecture rather than bombardment. Communications systems will sustain presence. Navigation signals will govern movement. Surveillance will determine whether unusual behavior can be understood before it causes escalation. The central contest concerns access. A state that supplies trusted services can shape the behavior of dependent users without claiming lunar territory. Its influence will grow through ordinary mission design rather than conquest. The United States should preserve an open cislunar framework while protecting the defense functions that cannot tolerate interruption. China will continue building its own network around proven relay capability. Smaller space powers will judge both systems by performance. The first meaningful military boundary near the Moon may never appear across its surface. It may be encoded inside the receiver that decides which network a spacecraft trusts. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit orbitest.substack.com (https://orbitest.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

2. 

   13 Oct 2026

   Poisoning the Machine That Guards the Network

   Cybersecurity systems increasingly rely on machine learning to distinguish suspicious activity from ordinary behavior. These systems develop behavioral baselines from historical observations and adjust their judgments as conditions change. That capacity helps security teams monitor environments too large for manual review. It also creates an opening for adversaries capable of manipulating what a model learns. Data poisoning occurs when an attacker influences the information used to train or update an algorithm. Instead of directly disabling a defensive system, the attacker alters its understanding of acceptable behavior. A compromised account can gradually become associated with unusual activity until genuinely hostile actions no longer generate meaningful concern. The risk extends beyond individual networks. Security vendors may depend on shared telemetry when refining detection models, creating potential supply-chain exposure. Organizations that treat algorithmic judgments as authoritative may therefore inherit vulnerabilities that remain hidden behind reassuring dashboards. The strategic issue is straightforward: a defensive system cannot reliably identify malicious behavior when an adversary has influenced its definition of normal. The Security Problem Inside Adaptive Detection Traditional detection rules identify conditions specified in advance. A rule might flag an employee account that authenticates from geographically distant locations within an impossible travel window. The logic is visible and relatively easy to inspect. Behavioral models operate differently. They study historical activity and estimate whether a new event resembles established patterns. An unexpected login might receive a higher risk score because it departs from an account’s previous behavior. That approach helps identify attacks that do not match a known signature. A criminal using stolen credentials may appear legitimate to a password-based control while still behaving differently from the actual account owner. The weakness emerges when the baseline itself becomes an attack surface. The National Institute of Standards and Technology’s adversarial machine learning taxonomy (https://csrc.nist.gov/pubs/ai/100/2/e2025/final) identifies poisoning as a distinct category of attack against learning systems. Its 2025 guidance explains that adversarial activity must be assessed according to the stage of the system’s life cycle and the attacker’s practical access. An attacker does not always need direct control over a model. In some environments, influence over the observations reaching that model may be sufficient. Consider an employee who normally accesses internal resources during business hours. An attacker using the employee’s credentials begins authenticating occasionally at unusual times. Each event appears questionable but remains explainable. If the system adapts to repeated observations, the account’s established pattern may slowly expand. Activity previously considered abnormal becomes familiar. Whether this occurs depends on the product’s design. Some systems retrain periodically. Others update scoring thresholds or incorporate analyst feedback. The vulnerability is conditional rather than universal, but the underlying concern remains consistent: an adaptive model can inherit manipulated assumptions from its environment. From Direct Poisoning to Hidden Backdoors The most direct poisoning attack targets training data before deployment. Suppose a security vendor builds a classifier using examples of legitimate network traffic and known malicious activity. If an attacker can alter the labels attached to selected samples, the model may learn that particular hostile behaviors belong in the harmless category. A successful attack does not require the entire dataset to become unreliable. Carefully chosen samples may influence the model’s treatment of a narrow behavior while leaving its overall performance largely intact. That selective outcome is strategically attractive. A detection system that fails constantly attracts investigation. A system that misses one carefully prepared intrusion can continue appearing effective. The United Kingdom’s National Cyber Security Centre guidance on machine learning supply chains (https://www.ncsc.gov.uk/collection/machine-learning-principles/secure-development/secure-supply-chain) explains that poisoned datasets can degrade model performance or introduce targeted backdoors. A backdoor operates through a hidden trigger. During training, the attacker conditions the model to associate a particular feature with a benign outcome. Later, that feature appears inside otherwise suspicious activity. The model responds as trained. A malicious script might contain a characteristic that repeatedly appeared in harmless training examples. If the model assigns excessive importance to that characteristic, the script could receive a favorable classification despite performing hostile actions. The dangerous behavior may remain invisible during standard testing. A model can score well against conventional benchmarks while failing when the attacker introduces the specific trigger. This creates a difficult procurement problem. A customer reviewing published accuracy figures may learn little about whether the model contains narrow weaknesses deliberately embedded during development. Behavioral Baselines as a Target Postdeployment poisoning can be more difficult to recognize because it occurs within ordinary operations. Imagine a defense contractor using behavioral analytics to monitor access to sensitive engineering documents. An employee normally works within one project environment. An attacker compromises the employee’s credentials but avoids immediately reaching for protected material. Instead, the account begins accessing a small number of files associated with an adjacent project. The activity produces an alert. A security analyst finds a plausible business explanation and closes the case. Several weeks later, the account accesses additional material. Again, nothing appears immediately damaging. If the product incorporates recent behavior into its baseline, the account gradually becomes associated with broader access. By the time the attacker approaches genuinely sensitive documents, the activity may appear less unusual than it would have at the beginning. This scenario is illustrative. It does not establish that every commercial behavioral analytics product updates its models in this manner. The practical risk depends on how a particular system weighs historical activity and whether analyst decisions influence future classifications. The distinction is important because legitimate adaptation is necessary. An employee’s duties can change. A security tool that never adjusts would generate unnecessary warnings whenever normal work patterns evolve. The problem arises when adaptation happens without adequate historical context. A model that evaluates recent activity in isolation may mistake a carefully managed intrusion for ordinary professional development. The attacker exploits the system’s willingness to believe that repeated behavior deserves acceptance. Poisoning the Human Feedback Loop The learning system is only one part of the defensive structure. Human analysts also develop expectations about which alerts deserve attention. An attacker can exploit those expectations even without directly modifying training data. Suppose an account repeatedly triggers warnings through unusual but ultimately harmless activity. Each investigation consumes time. After enough repetitions, analysts may begin treating similar alerts as routine. If the organization reduces the alert’s severity, an attacker benefits from the adjustment. Where analyst decisions feed back into automated scoring, the effect may become more serious. Repeated dismissals can reinforce the conclusion that similar activity is acceptable. The NCSC’s secure AI development guidance (https://www.ncsc.gov.uk/collection/guidelines-secure-ai-system-development/introduction) explicitly recognizes that attackers may corrupt training data or user feedback. This matters because feedback represents institutional judgment translated into machine-readable form. A sophisticated adversary can attempt to shape that judgment gradually. The problem becomes especially pronounced when different analysts handle related alerts across separate shifts. Each person may review a single event without seeing the larger pattern. The immediate explanation appears reasonable. The combined history tells another story. A security team that fails to connect those cases effectively allows the attacker to distribute one operation across multiple isolated investigations. Operational Consequences Beyond the Network The consequences of poisoned detection can extend into physical operations. Consider a water treatment facility using anomaly detection to monitor control commands. Under ordinary conditions, equipment operates within an established range. An attacker seeking to alter chemical treatment could introduce small adjustments that remain technically permissible. If the monitoring system learns from recent behavior without adequate safeguards, its understanding of normal operating conditions may shift. A later adjustment could appear less suspicious because the model has already accepted the direction of change. This example describes a plausible mechanism rather than a documented case attributed to a specific facility. Its significance lies in the connection between algorithmic judgment and physical effects. Independent engineering safeguards should prevent dangerous commands from succeeding even if a monitoring model misclassifies them. Where those safeguards are absent or poorly maintained, the consequences could become more serious. Defense environments face a similar concern when automated monitoring protects information that shapes operational decisions. If an attacker compromises a maintenance system after gradually conditioning its behavioral controls, the eventual harm may appear outside the network. Personnel could act on altered technical information while believing the underlying system remains trustworthy. The cybersecurity incident then becomes an operational problem. The danger comes from misplaced confidence rather than visible system failure. Security Vendors and the Model Supply Chain Most organizations purchase machine learning capabilities through commercial products. Customers may understand what a platform claims to detect without knowing how its models are trained. That creates uncertainty around provenance. A vendor might improve detection by analyzing telemetry collected across customer environments. If customer data influences shared models, a compromised environment could theoretically become a route for introducing misleading examples into a broader system. Whether that risk exists depends on the vendor’s architecture. Some providers isolate customer-specific learning. Others aggregate observations while applying safeguards intended to reject unreliable inputs. Customers cannot evaluate the exposure without understanding those design choices. The NCSC’s 2026 assessment of adversarial attacks against machine learning (https://www.ncsc.gov.uk/paper/understanding-adversarial-attacks-against-machine-learning-and-ai) places poisoning within a broader threat model for deployed systems. Its analysis emphasizes that organizations must consider how model vulnerabilities affect the surrounding operational environment. This principle matters for procurement. A security platform may contain no conventional software backdoor while still behaving dangerously because its training history was corrupted. Ordinary software assurance does not necessarily reveal that problem. A model’s learned behavior represents another form of supply-chain dependency. The customer inherits decisions made during data collection and subsequent retraining, even when those decisions remain invisible inside the product. Defensive Architecture and Data Provenance Training data should be treated as a protected security asset. If unauthorized personnel can modify a dataset used to classify malicious activity, they may influence future detection outcomes without changing application code. Organizations should maintain records showing where training examples originated and who assigned their classifications. Changes to sensitive datasets should receive the same scrutiny given to modifications of security configurations. Suppose a repository contains samples previously identified as malicious. If those samples are suddenly relabeled harmless, investigators should determine whether the change reflects new evidence or unauthorized interference. Model updates also require review. A new version that stops flagging behavior identified by its predecessor may have a legitimate explanation. It may also reflect compromised training data. The NIST guidance on adversarial machine learning (https://www.nist.gov/news-events/news/2024/01/nist-identifies-types-cyberattacks-manipulate-behavior-ai-systems) warns that no universally reliable defense exists against every manipulation technique. That limitation argues for layered controls rather than confidence in a single protective method. A behavioral score should not determine access independently. If an employee lacks formal authorization to retrieve protected engineering files, the request should fail even when the model considers the activity familiar. A machine can estimate whether an action appears suspicious. It should not decide whether policy permits the action. Testing for Slow Manipulation Conventional security testing often asks whether a system detects a known malicious sample. That question matters, but it does not address whether the model can be conditioned beforehand. An adversarial assessment should examine what happens when suspicious behavior changes gradually. A test account might slowly expand its access across a simulated historical dataset. The central issue is whether the system identifies the cumulative change or simply updates its baseline until the final action appears ordinary. Historical comparisons help answer that question. A model evaluating recent activity should retain visibility into older behavior. If an account maintained one stable pattern for two years before drifting steadily over several weeks, that departure deserves attention even when individual events appear minor. Organizations should also examine whether small input changes produce unexpectedly large changes in classification. A suspicious script that becomes harmless after a trivial modification may expose an unstable model decision. Investigators should determine whether the behavior reflects an innocent training artifact or a hidden weakness. MITRE ATLAS (https://atlas.mitre.org/) provides a framework for organizing threats against artificial intelligence systems. Security teams can use that framework to build threat scenarios around model-specific attacks rather than treating the model as an untouchable feature inside a larger product. Strategic Assessment Data poisoning changes the central question of cybersecurity monitoring. The traditional concern is whether a defensive system can recognize hostile behavior. The emerging concern is whether an attacker can influence what the system recognizes as hostile in the first place. That distinction has consequences for organizational trust. A quiet dashboard may reflect genuine security. It may also reflect a model that learned the wrong lesson. Defense organizations should examine how automated judgments shape operational decisions. Commercial buyers should demand greater clarity about model retraining and data provenance. Security teams should preserve historical context when evaluating behavior that changes gradually. None of these measures eliminates the threat entirely. They reduce the chance that a manipulated algorithm becomes the sole authority over whether suspicious activity deserves investigation. The machine guarding the network is useful because it learns. Its greatest vulnerability begins when someone else decides what it learns. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit orbitest.substack.com (https://orbitest.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

3. 

   6 Oct 2026

   America’s AI Supremacy Has an Electricity Bill Coming Due

   The next contest for computing power may be decided by who can connect a data center to the grid before its processors grow old. An AI company can order chips, sign a lease, and announce a campus. None of those acts delivers a dependable megawatt to a particular parcel of land. That requires generation, transmission capacity, transformers, substations, cooling equipment, and a contract that settles who pays when the grid must be rebuilt. The physical work is stubbornly indifferent to the speed of a software release. My estimate is that electricity will increasingly determine where American AI capacity is built and who can afford to build it. It does not follow that the United States is running out of power. The problem is matching large, concentrated, nearly continuous loads to power that is available at the right location and on the right schedule. That distinction will decide whether today’s data center announcements become operating machines. A national forecast meets a local socket The scale is real, even if the precise destination is uncertain. Lawrence Berkeley National Laboratory’s 2024 data center study (https://eta.lbl.gov/publications/2024-lbnl-data-center-energy-usage-report) estimated that U.S. facilities consumed 58 terawatt-hours of electricity in 2014 and 176 terawatt-hours in 2023. Its 2025 update, published in June 2026 (https://eta.lbl.gov/publications/united-states-data-center-energy-2025), places 2030 demand at 649 terawatt-hours in its reference case, with a broad compounded uncertainty range of 521 to 843 terawatt-hours. That reference case would account for 11.8 percent of U.S. electricity use, according to the laboratory. These are estimates and forecasts for all data centers, not a meter reading for AI alone. The multiplication deserves attention. Relative to the 2023 estimate, the 2030 reference case is about 3.7 times as large. A terawatt-hour is a billion kilowatt-hours; an annual total gives a sense of the fuel and generation required. It does not say whether a proposed campus can be served on a hot afternoon in northern Virginia, or whether a transmission line can deliver power to an Ohio substation without an upgrade. Electricity is delivered through a network with addresses, limits, and neighbors. Berkeley Lab builds its forecast from expected equipment shipments, the power each device draws, cooling performance, and the types and locations of facilities. Its range reflects consequential uncertainty. The report’s sensitivity cases include different assumptions about graphics-chip shipments, chip lifetimes, and the amount of electricity AI servers draw while idle or in use. A forecast this wide should discipline anyone announcing an inevitable national shortage. It should also discipline anyone treating the lower bound as comfortable spare capacity. For a developer, the first question is not whether the nation produces enough electricity over a year. It is whether a utility can deliver the requested load to the site at the promised date, including during a regional peak or an equipment failure. A large campus can need new transformers and high-voltage work. New generation may have to enter the system. The developer’s timetable, the utility’s construction schedule, and the grid operator’s reliability test have to meet on the same calendar. They frequently do not. Even the word power hides several separate jobs. Generation makes electricity. Transmission carries large quantities over long distances. Substations change voltage and direct the flow. Distribution and site equipment deliver it to the building. Backup systems and cooling keep a brief fault from becoming a failed service. A new gas plant can address one problem and leave a transmission constraint untouched. A transmission line can reach a region while the last substation serving a chosen parcel remains full. A data center is therefore built twice: once as a computing facility, and again as a claim on the electrical system around it. The queue contains a political decision PJM, the grid operator serving much of the Mid-Atlantic and Midwest, offers a useful test. Its January 2026 long-term forecast (https://insidelines.pjm.com/pjms-updated-20-year-forecast-continues-to-see-significant-long-term-load-growth/) projects a summer peak of 160,451 megawatts in 2027 and 191,017 megawatts in 2031. Those figures cover the entire PJM system, including homes, businesses, and industry. They must not be described as data center demand. PJM does identify data centers among the major sources of expected growth, while acknowledging uncertainty about which proposed projects will materialize. PJM has already revised its near-term outlook downward from the prior year’s forecast after changing its treatment of electric vehicles, economic growth, and large-load requests. Its new process demands firmer commitments for nearby years and discounts less certain projects farther out. That is a healthy correction. A company can ask several utilities about several sites while intending to build only one. If each inquiry becomes an assumed future load, the grid may plan around a ghost population of server halls. If planners discount serious projects too aggressively, they can fail to build capacity that customers really need. Either error costs money. This is where an engineering problem becomes an argument about authority. Who gets a firm reservation on scarce capacity? What proof of construction should be required? When a new customer triggers a substation or transmission upgrade, how much should that customer pay, and how much should be charged to existing households and businesses? Those choices determine who enters the computing market. A large firm can hold land, fund studies, sign long contracts, and consider building its own power supply. A smaller competitor has a shorter financial runway. Federal regulators have begun to confront the issue directly. In December 2025 (https://www.ferc.gov/news-events/news/fact-sheet-ferc-directs-nations-largest-grid-operator-create-new-rules-embrace), the Federal Energy Regulatory Commission directed PJM to clarify its rules for data centers and other large customers located alongside power plants. In June 2026 (https://www.ferc.gov/news-events/news/ferc-launches-aggressive-targeted-action-speed-large-load-integration), FERC told six regional grid operators to justify or revise rules governing how large users connect. Its stated concerns include the speed of studies, the allocation of transmission costs, power plants built near large customers, and service for customers able to reduce their grid draw. The question of a data center beside a power plant sounds simple until the grid needs that plant on a difficult day. Does the new facility take output that would otherwise reach other customers? Does it buy backup service from the network? What happens when its private generator trips? A campus can be physically close to generation yet remain electrically and financially connected to everyone else. FERC’s intervention is evidence that the bottleneck includes rules about reliability and payment, not merely the ability to pour concrete. The strongest objection is also a plan There is a serious case against treating electricity as a lasting brake on American AI. High prices attract investment in generation and grid equipment. Developers can choose sites with available capacity, contract for power, phase a campus’s opening, or build generation nearby. Better chips and software may deliver more computation for each unit of electricity. Some computing tasks can move in time or place. America’s capital markets and large technology firms can finance projects that few rivals could attempt. I take that objection seriously. In fact, it helps explain why the American advantage is substantial. The United States has companies prepared to buy extraordinary amounts of equipment and pay for the physical plant around it. But the objection describes a construction program, not an automatic market adjustment. A more capable chip can lower the energy required for a given task while making still larger and more profitable tasks possible. Power saved per operation does not guarantee lower total consumption. A generator built near a campus also needs fuel, cooling, permits, maintenance, and a clear agreement about grid support. Nor should every kilowatt-hour be treated as interchangeable. Annual production from a new plant can look ample on paper while its output arrives at the wrong hour or on the wrong side of a constrained line. A data center may tolerate interruptions for some work, such as a training run that can pause and resume. Other work, including services used by customers continuously, calls for steadier supply. The mix matters. So does the ability to move workloads among sites when one region is under strain. These are design choices with costs, and they should be compared with the expense of building firm electrical capacity. The strategic implication is narrower, and stronger, than a slogan about an AI power crisis. Access to processors remains important. Yet a nation can own impressive chips and fail to put them to work if power delivery falls behind. Within the United States, regions that can connect substantial new loads without shifting undue costs onto existing users should attract operating facilities sooner. Firms able to secure sites, equipment, and credible power contracts will gain time. Time matters when expensive processors lose commercial value while sitting idle. The national government has a role, though no federal order can make a local transformer appear overnight. It can demand honest forecasts, support transmission construction where warranted, and insist that connection rules state plainly who pays for what. States and localities control many of the siting decisions. Utilities must procure and install equipment. Private developers have to reveal enough about their projects for the public system to distinguish an actual campus from a speculative reservation. American AI capacity will be assembled by all of them, one electrical connection at a time. The evidence that would weaken my judgment is straightforward: major data center regions repeatedly connecting new facilities on schedule, without significant grid upgrades, rising reliability concerns, or costs shifted to other customers. Evidence that would strengthen it includes delayed openings attributed to power delivery, continued upward revisions to firm load forecasts, and regulators repeatedly rewriting service rules to accommodate large customers. My estimate for the rest of this decade is that the United States will keep adding substantial AI computing capacity, but the location and pace of that buildout will increasingly follow available electricity and the ability to connect it. The question is whether public authorities can make those connections fast enough while retaining the confidence of everyone already paying for the grid. If an AI campus requires a new substation, who should bear its cost? The Estimate Through 2030, I expect power delivery and connection terms to remain a material constraint on the timing and location of large U.S. AI campuses. Confidence: moderate. This assumes continued demand for large computing facilities, no abrupt collapse in equipment orders, and no sudden, broadly available reduction in total electricity requirements. A sustained pattern of on-time connections in the busiest regions, with stable reliability and transparent customer costs, would disprove the claim. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit orbitest.substack.com (https://orbitest.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

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