The Musk–Altman Fight, the AI Memory Squeeze, and the Power Wall That Will Define the Next 25 Months
The fight between Elon Musk and Sam Altman is being covered as a founder feud. It is louder than that, and smaller than that, at the same time.
The fight is loud because two of the most visible figures in artificial intelligence are now in court over the future of OpenAI, and it is smaller because the personalities are not the real story. The larger story is that artificial intelligence has crossed out of the software era and entered the infrastructure era.
We now live in a world where the future of AI is no longer decided by model releases alone. Decisions will be made by law and government, availability of memory, and access to power sources. The courtroom ultimately will decide who controls the mission. Memory decides who can scale the model. Power decides who gets to run it.
That is the real landscape for AI over the next 25 months.
On April 28, 2026, Elon Musk took the stand in federal court in Oakland, California, in a trial against OpenAI, Sam Altman, Greg Brockman, and Microsoft. Musk alleges that OpenAI abandoned its original public-benefit mission by transforming from a nonprofit research effort into a profit-seeking enterprise. OpenAI denies wrongdoing and argues that Musk wanted control, not simply AI safety. Reuters reported that Musk is seeking $150 billion in damages for OpenAI’s charitable arm, the restoration of OpenAI’s nonprofit status, and the removal of Altman and Brockman from leadership roles. (Reuters)
The legal claims have narrowed. Ahead of trial, Musk dropped fraud claims, leaving the dispute focused on breach of charitable trust and unjust enrichment. That narrowing matters. This is no longer simply a fight over whether one founder misled another. It is a test of whether the founding mission of an AI organization can continue to bind that organization once it becomes a capital-intensive infrastructure company. (Fortune)
The Musk–Altman fight is not just about who founded OpenAI. It is about whether a founding mission can survive the economics of industrial-scale AI.
The First Leg: Law
OpenAI began as a nonprofit in 2015. Its stated mission was to ensure that artificial general intelligence benefited humanity. In 2019, OpenAI created a for-profit subsidiary to help finance the computing power, research scale, and talent base required to compete at the frontier. In October 2025, OpenAI announced an updated structure: the nonprofit became the OpenAI Foundation, and the for-profit became OpenAI Group PBC, a public benefit corporation. OpenAI says the Foundation continues to control the Group, while the Foundation holds a 26% equity stake worth about $130 billion. Microsoft holds roughly 27%, with the remaining 47% held by current and former employees and investors. (OpenAI)
That structure is the legal heart of the case.
A public benefit corporation is not a conventional corporation, at least in theory. It is designed to pursue both commercial success and a stated public benefit. But the OpenAI case raises a harder question: when the underlying technology becomes expensive enough to require hyperscale capital, does mission governance remain meaningful, or does it become the wrapper around a commercial engine?
Musk’s argument, in broad terms, is that OpenAI’s original mission was charitable and that its later restructuring violated that mission. OpenAI’s answer is that scaling frontier AI requires a commercial structure because compute, talent, infrastructure, and deployment are too expensive for a conventional nonprofit model. OpenAI’s lawyer argued in court that the creation of the for-profit entity was critical to buying compute and competing with Google DeepMind. (Reuters)
That is why the legal meaning reaches beyond OpenAI.
If Musk wins meaningful remedies, mission language across the AI industry may become more than reputational language. Founding charters, nonprofit promises, public-benefit claims, safety commitments, and investor documents could become litigation assets. Boards and investors would need to treat mission language as operating risk.
If OpenAI wins, the market may interpret the result differently: that mission-controlled commercial structures can survive if the nonprofit retains formal control and the organization can argue that commercialization is necessary to advance the mission.
Either outcome changes the legal map.
The case is therefore not only about OpenAI. It is about the legal architecture of frontier AI.
The Second Leg: Memory
The second constraint is not in court. It is in the supply chain. For the last two years, the public AI story has been dominated by GPUs. That is understandable. GPUs became the visible symbol of the AI boom. But GPUs do not work alone. They require high-bandwidth memory, DRAM, NAND, SSDs, advanced packaging, networking, power, cooling, and data-center capacity.
AI is not only compute. AI is memory moving at scale.
IDC reported in December 2025 that the global semiconductor ecosystem was experiencing an “unprecedented” memory shortage, with effects that could persist into 2027. IDC attributed the squeeze to AI data-center demand, which is pulling manufacturing capacity away from conventional DRAM and NAND used in consumer electronics and toward high-bandwidth memory and high-capacity DDR5 used in AI servers. IDC expects 2026 DRAM and NAND supply growth to remain below historical norms, at 16% and 17% year over year, respectively. (IDC)
This changes the AI economy.
The old cycle was simple enough: memory prices rose, manufacturers added capacity, supply normalized, and pricing pressure eventually eased. The AI cycle is different because hyperscalers are not merely buying more memory. They are changing the allocation of memory manufacturing itself.
The smartphone, PC, networking, and enterprise hardware markets are now downstream of the AI buildout. Every wafer pushed toward HBM for AI accelerators is capacity that cannot be used somewhere else. Every AI server order competes indirectly with consumer electronics, enterprise refresh cycles, SSD availability, and edge infrastructure.
Reuters Breakingviews reported that the AI data-center boom has transformed the memory sector, with facilities packed with processors that require ultra-fast high-bandwidth memory and large volumes of storage to feed AI models. It also noted that data centers require traditional solid-state drives to hold the large datasets used to train and serve AI models. (Reuters)
This is where the Musk–Altman legal fight connects to the memory shortage.
OpenAI’s shift toward a capital-seeking structure did not happen in a vacuum. It happened because AI stopped being a research project and became an infrastructure race. Once AI required billions of dollars in compute, massive data-center commitments, supply-chain priority, and cloud partnerships, the nonprofit ideal collided with physical scarcity.
The legal case asks whether OpenAI stayed faithful to its founding mission. The memory market asks whether anyone can physically keep building.
The future of AI may be argued in court, but it will be rationed in memory.
The Third Leg: Power
The third constraint is power. AI companies can raise capital. They can fight lawsuits. They can secure GPUs. They can sign cloud agreements. They can win enterprise customers, but none of it matters if they cannot secure electricity.
Reuters Events reported that U.S. annual power demand is forecast to rise by 1.2% in 2026 and 3.3% in 2027 as data-center deployments surge, citing the U.S. Energy Information Administration. Texas is expected to see especially large demand growth, while power developers are struggling to keep pace with AI-driven demand. Reuters also reported that residential electricity prices are forecast to rise by 5.1% in 2026 and 2.4% in 2027 before inflation. (Reuters)
This turns AI into a geography problem.
The next AI winners will not only be the companies with the best models. They will be the companies with access to power, grid interconnection, long-term energy contracts, land, cooling, fiber, and local political support for data-center expansion. That means the AI map is changing. The traditional software geography of Silicon Valley is giving way to an infrastructure geography built around energy corridors, transmission capacity, fiber density, tax policy, and time to power.
This is where the AI buildout starts to resemble telecom, energy, and industrial infrastructure more than software. A model can be copied. A benchmark can be surpassed. A user interface can be replicated. However a large energy campus cannot be improvised. Grid connections take time. Transformers take time. Cooling systems take time. Permits take time. Transmission takes time. Local opposition can slow or stop projects. Energy availability becomes a competitive moat.
AI is turning into a power and infrastructure business.
The Microsoft Layer
The OpenAI–Microsoft relationship is also evolving.
On April 26, 2026, OpenAI announced the next phase of its Microsoft partnership. Microsoft remains OpenAI’s primary cloud partner, and OpenAI products will continue to ship first on Azure unless Microsoft cannot or chooses not to support the required capabilities. Microsoft will continue to hold a license to OpenAI IP for models and products through 2032, but that license is now non-exclusive. OpenAI can also serve its products to customers across any cloud provider. (OpenAI)
This is important because it signals a broader shift for the next phase of AI; it will not be single-cloud, single-model, single-provider, or single-supply-chain. It will be multi-cloud, memory-constrained, power-constrained, legally contested, and geographically distributed.
OpenAI needs flexibility because AI infrastructure cannot be guaranteed through one pipe. Microsoft needs exposure because OpenAI remains one of the most important companies in AI. The market needs clarity because enterprise customers do not want to build long-term AI strategies on unclear governance or constrained supply.
The partnership update fits the larger thesis: AI is moving from product competition to infrastructure competition.
The Next 25 Months
While this is purely my opinion I do believe the next 25 months from April 2026 to May 2028, that the AI landscape will dramaticlly change. I’ve boiled it down to five likely phases.
Phase One: Spring 2026 — The Governance Shock
The Musk–Altman trial places OpenAI’s founding mission, nonprofit history, Microsoft relationship, and public-benefit structure under public examination. Reuters reported that the trial could affect OpenAI’s IPO plans and public perception of AI, with a verdict possible by mid-May. (Reuters)
This phase forces every major AI company to revisit its governance language. Mission statements, safety promises, public-benefit claims, charitable origins, and investor rights become more than communications material. They become potential legal exposure.
Phase Two: Summer to Fall 2026 — The Infrastructure Separation
By late 2026, AI companies with secured memory, cloud access, and power commitments will begin separating from companies that only have models.
The difference will become visible in enterprise delivery. Some vendors will be able to serve inference reliably at scale. Others will face capacity constraints, higher prices, slower rollout schedules, or dependence on third-party infrastructure.
The market will begin asking a new question: not “how smart is the model?” but “can the provider deliver it reliably, affordably, and securely?”
Phase Three: Late 2026 to Early 2027 — The Cost Pass-Through
The memory shortage will begin showing up more clearly in hardware prices, enterprise refresh cycles, AI servers, PCs, smartphones, SSDs, and networking equipment. IDC has already warned that rising DRAM and NAND costs may force device makers to raise prices, reduce specifications, or both. (IDC)
This will affect AI adoption.
Large hyperscalers will keep building because they can secure supply early and absorb cost. Mid-market enterprises may delay deployments, reduce scope, or prioritize practical automation over experimental AI. Startups will be forced to optimize for smaller models, lower memory footprints, better retrieval, compression, quantization, caching, and domain-specific performance.
The winner in this phase may not be the biggest model. It will be the most efficient model.
Phase Four: 2027 — The Power Wall
By 2027, power becomes the constraint that customers can see. We all see it looming on the horizon. Data-center projects begin competing more visibly with residential demand, industrial demand, electrification, and grid modernization. Energy affordability becomes political. On-site generation becomes more common. Locations with available power and transmission capacity become strategic assets.
This is where AI infrastructure stops being an abstract technology story and becomes a municipal, regional, and national planning issue.
The question will not be whether AI can advance. The question will be where it can advance.
Phase Five: Early 2028 — The Split Between AI Owners and AI Claimants
By May 2028, the AI market may split into two classes. The first class will be AI infrastructure owners: companies with durable access to models, chips, memory, cloud regions, energy, customers, and distribution. The second class will be AI claimants: companies with strong demos but weak infrastructure control.
That does not mean smaller AI companies disappear. It means they specialize. The opportunity shifts toward vertical AI, private enterprise AI, workflow automation, AI security, agentic orchestration, domain-specific models, and systems that use memory and power more efficiently.
The AI market matures from a race to show intelligence into a race to deliver governed, reliable, affordable intelligence at scale.
The Billions of Bits Read
The Musk–Altman trial is not a sideshow. It is the first major courtroom battle over the institutional shape of frontier AI. But the trial is only one leg. The second leg is memory: HBM, DRAM, NAND, SSDs, and wafer allocation. The third leg is power: electricity, transmission, cooling, grid interconnection, and time to power.
Together, these three constraints define the next phase of artificial intelligence.
Law determines who is allowed to control the mission.
Memory determines who can scale the system.
Power determines who can keep it running.
The AI economy is now measured in billions of dollars, billions of parameters, billions of tokens, and billions of watts. But underneath all of it are the bits themselves: stored, moved, cached, retrieved, trained, inferred, compressed, and served. The question over the next 25 months is not whether AI advances.
It will.
The question is who gets to advance it — and under what constraints. The answer will not come from one courtroom, one model release, one GPU shipment, or one cloud contract. It will come from the convergence of law, memory, and power.
That is the new AI landscape. Not just who controls the model, but who controls the billions of bits.
