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Altman-Musk Courtroom War Exposes Power Struggle, Trust Crisis, and Trillion-Dollar Stakes At Openai

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The courtroom battle between Sam Altman and Elon Musk has evolved far beyond a dispute over corporate structure. The trial is now exposing a bitter ideological and financial conflict over who should control artificial intelligence, how its profits should be distributed, and whether one of Silicon Valley’s most influential companies abandoned its original mission in pursuit of enormous commercial gains.

Testifying in federal court in Oakland, California, Altman on Tuesday forcefully rejected Musk’s allegation that OpenAI’s leadership betrayed the organization’s founding commitment to build AI for humanity’s benefit rather than for corporate enrichment.

“It feels difficult to even wrap my head around that framing,” Altman said when asked about Musk’s accusation that he and OpenAI President Greg Brockman effectively “stole a charity.”

The high-stakes trial, now entering its third week before U.S. District Judge Yvonne Gonzalez Rogers, could reshape the future of OpenAI as the company weighs a potential initial public offering that analysts and investors believe could eventually value the firm at around $1 trillion.

At the core of the lawsuit is Musk’s claim that OpenAI abandoned its nonprofit mission after attracting billions of dollars in capital and commercial partnerships, especially from Microsoft. In his August 2024 lawsuit, Musk alleged that Altman and other OpenAI leaders persuaded him to contribute roughly $38 million to a nonprofit dedicated to safely advancing AI for humanity, only to later transform the organization into a profit-driven enterprise.

Musk is seeking about $150 billion in damages from OpenAI and Microsoft, with the money intended for the nonprofit arm of OpenAI. He is also seeking the removal of Altman and Brockman from leadership positions. The case has become one of the most consequential legal confrontations in the technology industry, arriving at a moment when generative AI systems are reshaping global business, labor markets, media, defense, and geopolitics.

Altman sought to turn Musk’s accusations back on him, portraying the billionaire entrepreneur not as a protector of OpenAI’s mission but as someone who repeatedly sought dominant control over the company.

Asked whether Musk opposed OpenAI becoming a for-profit business, Altman replied, “quite the opposite.” According to Altman, Musk at one point demanded a 90% stake in OpenAI, a proposal that left him “extremely uncomfortable” because it would have effectively concentrated control of the organization in Musk’s hands.

Altman said his concerns stemmed partly from observing power dynamics at SpaceX, where Musk consolidated substantial authority over the aerospace company.

“I had quite a lot of experience with startups, had seen a lot of control fights,” Altman testified.

The testimony cuts directly against Musk’s broader public narrative that OpenAI’s leadership corrupted the company’s mission by commercializing it. Altman’s defense instead attempts to frame Musk as a frustrated co-founder who failed to gain sufficient control over an organization that later became one of the most strategically important companies in artificial intelligence.

Altman also testified that he opposed a proposal to merge OpenAI with Tesla, arguing such a move would have compromised OpenAI’s independence.

“I don’t think we would have had the ability to ensure that mission was acted on,” Altman said. “Fundamentally, Tesla needs to serve its customers and sell cars.”

What is More to the Dispute?

The exchange underscores how the dispute is fundamentally about governance and power as much as technology. OpenAI’s transition from nonprofit research lab to one of Silicon Valley’s most valuable commercial AI firms has created tensions over whether the company can simultaneously pursue enormous profits and uphold its original safety-oriented mission.

Lawyers for Musk spent much of Tuesday attempting to undermine Altman’s credibility, portraying him as a manipulative executive whose public messaging differs sharply from internal conduct. Musk’s attorney, Steven Molo, cited testimony from former OpenAI officials and board members who allegedly questioned Altman’s honesty. One former board member described what was characterized in court as a “toxic culture of lying,” while multiple former officials reportedly testified that they did not trust him.

“Have you misled people when you do business?” Molo asked.

“I believe I am an honest and trustworthy business person,” Altman responded.

When pressed again, Altman answered: “I do not think so.”

The line of questioning revived scrutiny surrounding Altman’s dramatic temporary removal from OpenAI in late 2023, when the board abruptly ousted him over concerns related to candor and governance before reinstating him days later following internal backlash and pressure from employees and investors.

Altman testified that he briefly considered leaving for Microsoft during the crisis but ultimately decided to return because OpenAI was too important to abandon.

“I was willing to run back into a burning building to save it,” he said.

The trial is also offering rare insight into the extraordinary scale of capital flowing into artificial intelligence. Altman testified that OpenAI has raised approximately $175 billion from investors over its lifetime as the company races to secure computing infrastructure needed to train increasingly powerful AI models.

That spending race has become central to the global AI competition, with firms pouring unprecedented sums into semiconductors, data centers, and energy infrastructure. OpenAI Chairman Bret Taylor added another dramatic dimension to the proceedings on Tuesday when he testified that Musk’s AI company, xAI, led a formal takeover attempt for OpenAI’s nonprofit arm in February 2025, months after Musk initiated legal action.

“I was surprised,” Taylor testified. “This proposal was to acquire this non-profit by a group of for-profit investors, which felt contradictory to the spirit of the lawsuit.”

That revelation could become a significant element in OpenAI’s defense strategy because it potentially weakens Musk’s argument that his lawsuit is purely about preserving OpenAI’s nonprofit mission.

The proceedings have also highlighted broader tensions inside OpenAI during Musk’s early involvement with the organization. Altman testified that some employees felt relieved after Musk departed OpenAI’s board in 2018 because they believed his management style was demoralizing researchers.

“I don’t think Mr. Musk understood how to run a good research lab,” Altman said. “He had demotivated some of our most key researchers.”

The testimony paints a picture of an organization divided not only by ideology but also by conflicting visions of leadership, governance, and AI development strategy. Several influential figures from the AI industry have already testified, including former OpenAI chief scientist Ilya Sutskever, who said he spent about a year compiling evidence for OpenAI directors regarding what he described as Altman’s “consistent pattern of lying.”

Satya Nadella also testified, describing Microsoft’s massive investment in OpenAI as a “calculated risk,” highlighting how deeply intertwined the software giant has become with the future of generative AI.

The case is increasingly viewed as a referendum on the future structure of the AI industry itself. The outcome could influence how courts interpret nonprofit-to-for-profit transitions in advanced technology sectors, how founders can exert control over mission-driven organizations, and how regulators may eventually oversee artificial intelligence companies with enormous economic and societal influence.

With testimony expected to conclude this week and jurors potentially beginning deliberations by May 18, the trial has already exposed the deep fractures behind the public image of the AI revolution. What began as a partnership among Silicon Valley figures promising to develop safe AI for humanity has devolved into accusations of greed, deception, power consolidation, and betrayal.

SoftBank Delivers Record $46 Billion Vision Fund Windfall on OpenAI Bet

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SoftBank Group reported a massive $46 billion gain in its Vision Fund for the financial year ended March 31, the vast majority of which stemmed from the skyrocketing valuation of its enormous stake in OpenAI.

The performance marks one of the most dramatic single-year turnarounds for the fund and underscores Masayoshi Son’s aggressive, conviction-driven approach to investing in transformative technologies.

The Vision Fund alone generated around $20 billion in gains in the final quarter of the year, with nearly the entire increase attributable to OpenAI. While other holdings such as Coupang, DiDi Global, and Klarna dragged on returns, the OpenAI position more than compensated, delivering $45 billion in gains for the full year.

SoftBank has already deployed more than $30 billion into OpenAI and has committed to a total of over $60 billion, which would secure it roughly 13% ownership. In March, OpenAI completed a major funding round co-led by SoftBank that valued the company at an eye-watering $852 billion, even as it faces stiff competition from Google, Anthropic, xAI, and others racing to dominate generative AI.

This outcome reflects Masayoshi Son’s long-standing philosophy of making outsized bets on what he sees as once-in-a-generation opportunities. From his early Alibaba investment to the Vision Fund era, Son has consistently pursued massive scale in technology themes he believes will reshape the global economy.

OpenAI now sits at the absolute center of that strategy, with SoftBank also investing across AI infrastructure, chips, robotics, and related technologies.

However, the heavy concentration has raised clear concerns. In March, S&P Global Ratings shifted its outlook on SoftBank from “stable” to “negative,” warning that the additional massive commitment to OpenAI could weaken the company’s asset liquidity, portfolio quality, and financial flexibility. The agency highlighted risks tied to SoftBank’s elevated debt load should the AI boom encounter any slowdown or valuation reset.

To help finance its OpenAI ambitions, SoftBank has been systematically selling down stakes in mature holdings, including T-Mobile and Nvidia. These disposals contributed 218.1 billion yen ($1.4 billion) in gains for the year. Yet, when stripping out foreign exchange effects and other costs, the company recorded a 472.1 billion yen investment loss outside the Vision Fund, highlighting the mixed performance across its broader portfolio.

CFO Yoshimitsu Goto emphasized financial discipline during the earnings call, pointing to SoftBank’s solid 3.5 trillion yen cash buffer — sufficient to cover more than two years of bond redemptions. This liquidity provides a meaningful cushion as the company continues its aggressive AI deployment.

At the overall group level, SoftBank posted a strong net profit of 5 trillion yen for the year, supported by both the Vision Fund’s exceptional performance and resilient results from its core domestic telecommunications business.

The results paint a classic high-risk, high-reward playbook. SoftBank has captured enormous paper gains from the AI surge and positioned itself as one of the most influential corporate investors in the sector. The heavy bet on a single private company, however promising, creates significant volatility and concentration risk that traditional investors and rating agencies view warily.

OpenAI’s rapid valuation climb has validated SoftBank’s thesis in the near term, but questions remain about sustainability. The company still burns substantial cash on compute and talent, operates in a hyper-competitive environment, and faces ongoing regulatory and geopolitical scrutiny around AI development.

Against that backdrop, analysts believe that SoftBank’s success will depend on its ability to balance this concentrated AI exposure with portfolio diversification, prudent debt management, and continued strength in its telecom operations. While the Vision Fund’s historic gains provide breathing room, they also raise the bar for future performance.

In many ways, SoftBank’s latest results encapsulate the current era of technology investing: extraordinary rewards for those who back the right winners early, coupled with elevated risks when bets become oversized.

JPMorgan Overhauls Investment Bank Leadership as Jamie Dimon Warns Political Turmoil Could Threaten London Expansion

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JP Morgan Chase puts contents through its CEO account, it goes viral. But the same content via JPMC account, no one cares (WSJ)

JPMorgan Chase is simultaneously reshaping the leadership structure of its powerful investment bank and reassessing the political risks tied to one of its biggest international expansion projects.

The bank is set to announce a sweeping reorganization of senior investment-banking roles, according to a report by the Financial Times. The move comes as mergers-and-acquisitions activity rebounds sharply on the back of artificial intelligence-driven corporate spending, financial-sector consolidation, and infrastructure investment.

At nearly the same time, JPMorgan chief executive Jamie Dimon publicly warned that political instability in Britain could force the bank to rethink plans for a multibillion-dollar headquarters project in London if a future government became hostile toward the financial industry.

Together, the developments offer a revealing look into how the world’s biggest banks are recalibrating both internally and geopolitically as they compete for dominance in an increasingly fragmented global economy.

Under the planned investment-banking reshuffle, coverage chief Dorothee Blessing, global head of capital markets Kevin Foley, and global co-head of the financial institutions group Jared Kaye are expected to become co-heads of global investment banking.

According to the report, the bank plans to place mergers-and-acquisitions practitioners more directly under industry coverage teams. On Wall Street, banks are increasingly moving away from siloed advisory structures toward integrated sector-focused models capable of handling increasingly complex transactions.

The strategy is particularly relevant in the current environment, where corporate clients increasingly want bankers with expertise spanning regulation, geopolitics, artificial intelligence, supply chains, and sector-specific operational risks, rather than traditional deal execution alone.

Banks have been aggressively positioning themselves for the resurgence in global dealmaking after several sluggish years caused by high interest rates, inflation concerns, and geopolitical uncertainty. Global M&A revenue surged 19% in the first quarter to a record $11.3 billion, according to Dealogic data, driven largely by transactions linked to AI infrastructure, healthcare, and financial services. Artificial intelligence has become especially lucrative for investment banks.

Technology companies are pursuing acquisitions tied to semiconductors, data centers, cybersecurity, and cloud infrastructure, while private-equity firms are targeting businesses positioned to benefit from the enormous capital expenditure cycle tied to AI deployment.

That wave of activity is transforming the structure of investment banking itself. Banks are increasingly prioritizing cross-functional teams that can originate deals, arrange financing, advise on regulation, and provide long-term guidance within fast-evolving industries. JPMorgan’s restructuring appears designed to strengthen precisely that kind of coordination.

The changes also carry internal significance because they further deepen JPMorgan’s succession bench at a time when investors continue to scrutinize the long-term leadership outlook under Dimon, who has led the bank for nearly two decades and remains one of the most influential figures in global finance.

As part of the shake-up, Charles Bouckaert, currently co-head of industrials investment banking, is expected to replace Anu Aiyengar as the bank’s global head of mergers and acquisitions. Aiyengar, a 26-year JPMorgan veteran who became one of Wall Street’s most prominent female dealmakers, is expected to move into a global chair position within investment banking, according to the report.

The restructuring comes as JPMorgan continues to widen its lead over many rivals across trading, investment banking, and wealth management. The bank benefited heavily from the U.S. regional banking turmoil of recent years, which pushed corporate and wealthy clients toward larger institutions viewed as more stable and better capitalized.

Staying in London is No Longer Certain

But while JPMorgan is reorganizing internally to capture more business, Dimon’s comments in Europe showed the bank is also increasingly focused on political and regulatory risk abroad. Speaking to Bloomberg in Paris, Dimon warned that the bank could reconsider its planned office tower in London if Britain’s political direction turned against the banking industry.

Asked whether the instability surrounding the government of Keir Starmer affected his view of the project, Dimon responded: “If a new government was hostile to the banks, then yes.”

The remarks were striking because they touched directly on one of JPMorgan’s largest international real-estate and infrastructure commitments. The bank announced late last year that it intended to build a new three-million-square-foot tower in London’s Canary Wharf financial district to serve as its U.K. headquarters and house up to 12,000 employees.

Construction is expected to take about six years. JPMorgan also plans to renovate its existing building on Bank Street during that period. At the time of the announcement, the bank said the project remained “subject to a continuing positive business environment in the U.K. and the receipt of the necessary approvals and agreements at a national and local level.”

Dimon’s latest comments suggest that caution is becoming more important as Britain faces mounting political uncertainty. Starmer has come under pressure after his party’s poor showing in local elections triggered calls from some lawmakers for him to resign. As of Tuesday, dozens of Labour Party members of parliament had reportedly called for him to step down, while others publicly defended his leadership. The political instability has unsettled bond markets, with British government bonds, known as gilts, experiencing volatility amid concerns about Britain’s fiscal outlook and leadership uncertainty.

Dimon also criticized the tax burden JPMorgan faces in Britain, telling Bloomberg the bank had already paid $10 billion in “additional taxes” tied to the construction project.

Even so, he offered unusually strong support for Starmer and Chancellor Rachel Reeves.

“I think Keir Starmer’s a very smart guy,” Dimon said. “Politics is very tough. They’re in a bind because of debts and deficits, they inherited a lot of that, I think the world of Rachel Reeves, and they’ve got to be tough.”

He added: “They’ve got to say ‘we’re going to do these things [that] in the short term may not be great,’ but governments have to get the stuff done right that grows the economy.”

Dimon also praised Starmer’s efforts to repair Britain’s relationship with Europe after Brexit.

“I think they need to work closer with Europe,” he said. “If you remember, Keir Starmer and [French President Emmanuel] Macron, they were going to work closer.”

“Not reversing Brexit, but military alliances, intelligence alliances, making sure the economies have economic relationships that are good for both the continent and good for the U.K.”

JPMorgan employs more than 20,000 people in the United Kingdom, including roughly 13,000 in London. The bank estimates that its new headquarters project and broader office upgrade plans could contribute nearly £9.9 billion to the British economy and create more than 7,800 jobs over six years. Its existing operations already contribute an estimated £7.5 billion annually to London’s economy.

How AI and Crypto Merge through Compute Markets

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The convergence of artificial intelligence and cryptocurrency is creating a new economic layer for the internet: compute markets. In the same way that oil powered the industrial era and data fueled the social media era, computational power is becoming the defining commodity of the AI age.

As demand for AI models grows exponentially, crypto networks are emerging as decentralized marketplaces where compute can be bought, sold, verified, and distributed globally. AI systems require enormous computational resources. Training large language models, running inference engines, generating images, processing video, and powering autonomous agents all depend on high-performance chips such as GPUs and specialized AI accelerators.

Traditionally, access to these resources has been dominated by centralized hyperscalers like NVIDIA, Amazon, Microsoft, and Google. However, the explosive growth of AI has created shortages in compute infrastructure, pushing costs higher and limiting access for startups, developers, and independent researchers.

This is where crypto enters the equation. Blockchain networks are uniquely suited to coordinate distributed resources across the globe without relying on a central authority. Crypto protocols can tokenize computational power, allowing idle GPUs and servers to become productive assets in decentralized compute marketplaces.

Instead of a small group of cloud providers controlling AI infrastructure, anyone with hardware can contribute compute and earn tokens in return. Projects like Render Network, Akash Network, and Bittensor are early examples of this model. These networks use blockchain incentives to connect compute suppliers with AI developers who need processing power. The result is an open marketplace where prices are determined dynamically and resources can be allocated more efficiently.

The economic logic is powerful. Around the world, millions of GPUs remain underutilized for large portions of the day. Gaming PCs, enterprise servers, crypto mining infrastructure, and dormant data center hardware represent a massive reservoir of untapped computational capacity. Crypto networks transform this unused hardware into productive AI infrastructure by introducing programmable incentives through tokens.

At the same time, AI enhances crypto ecosystems. Artificial intelligence can optimize trading systems, improve blockchain security, automate smart contract auditing, detect fraud, and power autonomous decentralized agents capable of managing capital or executing on-chain strategies.

This creates a feedback loop: crypto provides decentralized infrastructure for AI, while AI increases the sophistication and efficiency of crypto networks. One of the most important developments is the emergence of compute as a financial asset class. In traditional markets, commodities such as oil, electricity, and bandwidth are traded based on supply and demand. AI compute is rapidly evolving into a similar category.

As AI adoption accelerates, access to GPUs and processing power may become one of the most valuable resources in the digital economy. This idea has gained traction among institutional investors and technology leaders. Discussions around compute futures and tokenized compute credits suggest a future where computational power can be traded like energy or foreign exchange. In such a system, businesses may hedge against rising AI infrastructure costs using blockchain-based markets.

Crypto solves a major coordination problem in AI development: global participation. Centralized AI development is heavily concentrated in a few countries and corporations because of the immense capital required to build data centers and acquire chips. Decentralized compute markets lower the barrier to entry. Developers in emerging markets can access distributed infrastructure without depending on a single cloud provider, while hardware owners anywhere in the world can monetize their resources directly.

Another key advantage is censorship resistance and resilience. Centralized cloud providers can restrict access, enforce geographic limitations, or prioritize certain customers. Decentralized compute markets distribute workloads across thousands of nodes, making the system more robust and politically neutral.

This could become especially important as AI increasingly intersects with geopolitics and national security concerns. However, challenges remain. Decentralized compute networks must prove they can deliver reliability, low latency, data privacy, and consistent performance at scale. Verification of computational work is another technical hurdle, since networks need mechanisms to confirm that tasks were executed correctly.

Token incentives must also be carefully designed to avoid speculation overwhelming utility. Despite these obstacles, the merger of AI and crypto appears increasingly inevitable. AI needs scalable and flexible infrastructure, while crypto needs real-world utility beyond speculative trading. Compute markets provide a natural intersection between the two industries.

The next phase of the digital economy may not be defined solely by cryptocurrencies or artificial intelligence independently, but by the fusion of both into decentralized computational economies. In that future, compute itself becomes money, infrastructure becomes programmable, and AI becomes a globally coordinated network rather than a centralized monopoly.

JPMorgan Launches Second Tokenized Money Market Fund on Ethereum

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JP Morgan Chase puts contents through its CEO account, it goes viral. But the same content via JPMC account, no one cares (WSJ)

JPMorgan Chase’s reported launch of a second tokenized money market fund on Ethereum marks another incremental but structurally significant step in the ongoing convergence between traditional asset management and blockchain-based financial infrastructure.

Rather than representing a speculative crypto product, tokenized money market funds sit at the intersection of regulated yield-bearing instruments and distributed ledger technology, aiming to modernize settlement, custody, and liquidity management.

A money market fund is a low-risk pooled investment vehicle that typically holds short-dated government securities, commercial paper, and cash equivalents. It is designed to preserve capital while generating modest yield. The innovation in tokenization is not the underlying assets themselves, but the representation of fund shares as blockchain-based tokens.

These tokens can be transferred, fractionally owned, and potentially settled near-instantly compared to traditional T+1 or T+2 financial rails. By issuing a second such product on Ethereum, JPMorgan is signaling that its initial experiments in tokenized fund structures are moving beyond pilot programs into a multi-product architecture.

Ethereum, as a programmable settlement layer, allows financial instruments to be embedded within smart contracts, enabling automated compliance, transfer restrictions, and programmable liquidity controls. This is particularly important for regulated funds, where identity verification, jurisdictional constraints, and investor eligibility must remain enforceable even in a decentralized environment.

The strategic rationale is twofold. First, tokenization reduces operational friction. Traditional money market fund distribution relies on intermediaries such as transfer agents, custodians, and clearing systems. Tokenized representations can streamline these roles, potentially lowering costs and reducing settlement delays.

Second, it expands accessibility. Fractionalized token units can, in theory, allow broader participation in institutional-grade yield products, subject to regulatory approval. However, the deployment of such instruments on public blockchain infrastructure introduces complexity. Ethereum’s transparency model, while advantageous for auditability, raises questions around privacy, especially for institutional investors who prefer confidentiality in portfolio positioning.

Additionally, smart contract risk becomes a material consideration; bugs or vulnerabilities in fund logic could introduce systemic operational risks not present in conventional ledgers. From a regulatory perspective, tokenized money market funds occupy a carefully monitored space. They remain securities, and therefore must comply with existing financial laws, including custody requirements, anti-money laundering rules, and investor accreditation standards.

The innovation lies not in bypassing regulation but in encoding compliance into the asset’s digital structure. Market implications are broader than the product itself. If large-scale institutions like JPMorgan continue expanding tokenized fund offerings, it could accelerate the migration of real-world assets onto blockchain rails.

This would deepen liquidity in on-chain capital markets and potentially create interoperable pools of tokenized cash equivalents that can be used as collateral across decentralized finance and traditional trading systems.

The launch of a second Ethereum-based tokenized money market fund is less about novelty and more about infrastructure evolution. It reflects a gradual but persistent shift in how major financial institutions conceptualize settlement, ownership, and liquidity in a digitized financial system.

If sustained, this trajectory may redefine the operational backbone of short-term capital markets over the coming decade. JPMorgan is the largest global systemically important bank to launch a tokenized MMF on a public blockchain like Ethereum.

These products bridge traditional liquidity management with DeFi-like features: composability, programmability, real-time settlement, and use in crypto ecosystems. Tokenized MMFs overall have grown to roughly $10 billion in assets with many on Ethereum, part of a broader ~$30+ billion tokenized assets market. Peers like BlackRock are also active in this space.