President Donald Trump has signed a scaled-back executive order that creates a voluntary framework for frontier artificial intelligence companies to share advanced models with the U.S. government before public release.

The executive order, which highlights Washington’s growing struggle to balance national security concerns with the desire to maintain America’s technological lead over China, follows a faceoff with Anthropic.

The order, signed privately on Tuesday, marks a significant shift from earlier proposals that would have subjected cutting-edge AI systems to a much longer government review process. Instead of the previously discussed 90-day review window, companies will now have the option of providing federal agencies access to powerful AI models up to 30 days before launch.

The shorter review period underscores the administration’s recognition that lengthy regulatory hurdles could slow innovation at a time when competition among American AI developers has intensified dramatically. The White House has repeatedly emphasized that maintaining U.S. leadership in artificial intelligence remains a strategic priority amid fierce competition from China.

Trump himself signaled those concerns last month when he publicly questioned whether stronger oversight could inadvertently undermine U.S. competitiveness.

“We’re leading China. We’re leading everybody,” Trump told reporters on May 21. “And I don’t want to do anything that’s going to get in the way of that lead.”

The executive order arrives as policymakers grapple with a new generation of AI systems that are becoming increasingly capable of identifying software vulnerabilities, generating sophisticated code, and potentially enabling offensive cyber operations.

The emergence of so-called frontier models, the most advanced AI systems currently being developed by companies such as OpenAI, Anthropic, Google DeepMind, and other leading developers, has been at the center of those concerns.

Recent advances have alarmed cybersecurity experts because AI systems are increasingly capable of automating tasks that once required highly skilled human researchers. These capabilities can be used defensively to discover vulnerabilities before attackers do, but they can also potentially be used to identify and exploit software weaknesses at unprecedented speed and scale.

Anthropic’s handling of its powerful Mythos model illustrates the industry’s growing caution. The company disclosed earlier this year that it had restricted the release of Claude Mythos after internal testing revealed cybersecurity capabilities that exceeded the firm’s comfort level for a broad public rollout.

The startup subsequently indicated that it was developing additional safeguards before making Mythos-level systems more widely available, reflecting a broader industry debate over how quickly capable models should be deployed. The new model was a bone of contention between Washington and Anthropic as the former sought the use of Mythos for defense purposes. In March, the Pentagon formally designated the company a supply-chain risk, intensifying the rift and forcing Anthropic to sue.

The administration’s new order appears designed to address precisely those challenges. By encouraging companies to voluntarily share models before release, federal agencies gain an opportunity to evaluate emerging risks without imposing mandatory licensing requirements or lengthy approval processes that could slow development cycles.

The voluntary nature of the framework is particularly noteworthy. Unlike regulatory approaches being explored in some other jurisdictions, the U.S. government is seeking cooperation rather than direct control over model releases. That approach is likely intended to preserve goodwill with major AI developers, many of whom have warned that overly restrictive regulation could hamper innovation and push research activity overseas.

AI regulation has been immersed in a political tussle. The technology has become a key arena in the broader geopolitical contest between the United States and China, leading policymakers to weigh security concerns against economic and strategic considerations.

Many technology executives have argued that American leadership in AI depends on rapid deployment, large-scale investment, and the ability to commercialize breakthroughs quickly. From that perspective, a mandatory review system could create competitive disadvantages for U.S. firms relative to foreign rivals.

Yet cybersecurity officials worry that the same capabilities driving economic growth could also create new national security risks. Advanced AI models are becoming capable of accelerating vulnerability discovery, malware analysis, penetration testing, and other tasks traditionally performed by cybersecurity professionals.

The order therefore represents an attempt to thread a difficult needle: obtaining greater visibility into emerging AI risks without erecting barriers that industry leaders fear could slow innovation. Its effectiveness will ultimately depend on how many companies choose to participate and how much information they are willing to share with federal agencies.

Alternative investment giant Blackstone has closed its largest-ever Asia private equity fund at $13.1 billion, underscoring growing investor confidence in the region even as geopolitical tensions, inflation concerns, and market volatility continue to reshape global capital flows.

The fund, Blackstone Capital Partners Asia III, surpassed its original $10 billion fundraising target and more than doubled the size of its predecessor, making it one of the largest private equity vehicles ever assembled for Asia. The milestone underpins a broader trend among institutional investors seeking new growth opportunities beyond the United States, where elevated asset valuations and economic uncertainty have prompted a reassessment of portfolio allocations.

The successful fundraising also reinforces Asia’s emergence as one of the most important battlegrounds for global private equity firms. Just weeks ago, EQT AB raised $15.6 billion for what became the region’s largest private equity fund, while KKR & Co. is reportedly seeking another $15 billion for its next pan-Asia vehicle. Meanwhile, Bain Capital has already secured roughly $10.5 billion for its latest Asia-focused buyout fund.

Together, these fundraising efforts signal that despite concerns surrounding the conflict involving Iran, slowing global growth, and persistent inflationary pressures, large investors continue to view Asia as one of the world’s most attractive long-term investment destinations.

Joe Baratta, Global Head of Blackstone Private Equity Strategies, emphasized the region’s growth potential.

“Asia Pacific is the fastest-growing region in the world, presenting compelling opportunities to invest at scale behind our high-conviction themes and deliver for our investors,” he said.

His comments align with a growing consensus among global fund managers that Asia’s structural growth story remains intact. Rising consumer spending, accelerating digitalization, expanding middle classes, and government-led industrial policies are creating investment opportunities across technology, healthcare, financial services, manufacturing, and infrastructure.

India and Japan have emerged as particularly attractive markets.

India continues to benefit from strong economic growth, rapid technology adoption, and a large domestic consumer base. The country’s startup ecosystem has matured significantly, producing opportunities not only in venture capital but also in growth equity and large-scale buyouts. Japan, meanwhile, is experiencing a resurgence in corporate restructuring, governance reforms, and shareholder activism, creating opportunities for private equity firms to acquire and transform established businesses.

Blackstone has been especially active in both markets. Over the past two years, the firm has deployed more than $7 billion across 12 transactions in India and Japan.

Among those investments was funding for Neysa, a company seeking to capitalize on surging demand for artificial intelligence infrastructure. The firm also invested in TechnoPro Holdings, reflecting growing interest in sectors tied to digital transformation and advanced industrial services.

The fundraising success comes at a time when private equity firms are increasingly positioning themselves around the AI investment boom. Demand for data centers, cloud infrastructure, semiconductors, and AI-enabled enterprise services is creating new opportunities across Asia, particularly in India, Japan, South Korea, and Southeast Asia.

Additionally, Blackstone’s ability to return capital to investors has strengthened confidence in its regional strategy. During the last two years, the firm exited 15 portfolio companies, including through public listings of International Gemological Institute and Aadhar Housing Finance.

Successful exits are particularly important in today’s environment because many private equity firms globally have struggled to sell assets amid higher interest rates and weaker merger-and-acquisition activity. Investors increasingly favor managers that can both deploy capital effectively and generate liquidity through exits.

The scale of Blackstone’s latest fund also illustrates how global investors are adjusting to a changing geopolitical and economic landscape. Pension funds, sovereign wealth funds, insurance companies, and wealthy individuals have been seeking greater geographic diversification as concerns grow about concentrated exposure to U.S. markets.

The combination of high equity valuations, persistent inflation risks, and geopolitical uncertainty has encouraged many institutional investors to increase allocations to alternative assets and faster-growing regions.

For Asia, that shift could prove profitable. While fundraising conditions remain challenging compared with the boom years of 2020 and 2021, the region continues to attract large pools of capital from global investors betting that economic growth in Asia will outpace most developed markets over the coming decade.

Blackstone’s record fundraising indicates that, despite short-term volatility, many investors remain convinced that the next wave of value creation will increasingly come from Asia’s expanding economies, growing technology ecosystem, and deepening corporate transformation opportunities. The firm’s ability to exceed its fundraising target by more than $3 billion is a clear indication that global capital continues to see the region as a critical source of future returns.

OpenAI CEO Sam Altman has directly addressed one of the most pressing concerns weighing on investors and corporate boardrooms amid the artificial intelligence boom: whether the staggering sums being spent on infrastructure, chips, and software will ultimately deliver meaningful returns.

In a CNBC interview on Monday, Altman described the skepticism as not only valid but perhaps the “most fair criticism right now of AI.” He acknowledged the gap many companies are experiencing between heavy investment and visible business impact.

“You hear companies saying, I am spending a ton of money on AI. And I know some great stuff is happening, but I know there’s a ton of waste,” he said.

Altman went on to summarize the core questions echoing through executive suites. He said: “How long do I have to wait for it to really show up in revenue, and how long do I have to wait to really get the costs under control? I assume that the industry will figure that out pretty quickly, but I think that is a fair, a fair issue.”

The comments come at a critical juncture. The AI sector has seen unprecedented capital expenditure, with hyperscalers like Amazon, Google, Microsoft, and Meta collectively committing sums that rival, and in some cases exceed, major historical projects on a monthly basis. Yet tangible, widespread revenue generation and efficiency gains have been slower to materialize than many had hoped, leading to growing scrutiny from investors and analysts.

According to an April report from The Wall Street Journal, OpenAI itself missed some key internal targets for revenue and user growth last year, underscoring that even the industry’s flagship company is navigating execution hurdles in turning technological capability into sustainable commercial success.

The Utilization Problem and Signs of Inefficiency

Data from cloud optimization platform Cast AI reveals a striking inefficiency at the heart of the AI buildout. In an analysis of 23,000 GPU clusters across thousands of companies, average utilization stood at just 5%, meaning roughly 95% of provisioned graphics-processing capacity was sitting idle.

Cofounder and president Laurent Gil attributed this largely to “FOMO” — companies hoarding scarce AI chips out of competitive anxiety rather than immediate, well-defined needs, resulting in massive stockpiles of underutilized resources.

Longtime AI researcher and critic Gary Marcus, professor emeritus at New York University, has been particularly vocal. In a post on X, he described some companies’ AI capital expenditure plans as potentially the “Greatest capital misallocation in history,” noting that Amazon, Google, Microsoft, and Meta are collectively spending more per month than the entire Manhattan Project.

This critique resonates as investors increasingly demand proof that AI spending is translating into sustainable competitive advantages, productivity gains, or profitability, rather than speculative infrastructure bets.

The transition from experimental pilots to enterprise-wide deployment has proven more complex than anticipated, with persistent challenges around data quality, integration, change management, talent shortages, and measurable return on investment slowing the payoff curve.

Why This Matters for the Industry’s Trajectory

Altman’s willingness to confront these concerns head-on reflects a maturing phase in the AI industry. After years of explosive hype, fundraising, and infrastructure buildout, the sector is entering a period of greater accountability. Companies are under pressure to demonstrate not just raw technological capability but clear, quantifiable business value — whether through cost savings, new revenue streams, or transformative capabilities that justify the enormous upfront investments.

For OpenAI and its peers, the focus is shifting toward practical applications, agentic systems, and efficiency improvements that can deliver quicker returns. Altman’s comments suggest confidence that the industry will solve these issues relatively soon, but they also serve as a reality check for executives and investors who may have expected immediate, sweeping transformation.

The implications are significant and multifaceted. According to analysts, if major companies continue to pour billions into AI without corresponding revenue or productivity gains, it could lead to a pullback in spending, slower innovation cycles, increased investor caution, or even a broader “AI winter” narrative.

Conversely, successfully addressing the utilization and ROI challenges could unlock the next phase of AI-driven growth, with compounding effects across industries as the technology moves from experimental to foundational.

The scrutiny is particularly intense for hyperscalers and large tech firms, which have announced hundreds of billions in AI-related capital expenditure. Their ability to convert these investments into sustainable advantages, through better models, more efficient infrastructure, or new applications, will determine market leadership in the years ahead. Smaller or mid-tier players may struggle if the capital intensity remains high without clear paths to monetization.

Economically, the AI boom’s success or stumbles could have ripple effects because sustained high spending without returns risks crowding out investment in other sectors, inflating asset valuations in tech, and contributing to broader market volatility. On the positive side, meaningful productivity gains could help offset demographic challenges, boost GDP growth, and create new industries — provided the economics align.

While the reality still hurts, Altman’s acknowledgment of the criticism may help temper some of the more extreme expectations while reinforcing the long-term thesis. The coming quarters, marked by earnings reports from hyperscalers and AI infrastructure players, will provide crucial data points on whether the massive bet on AI is beginning to pay off or if the “waste” concerns require more urgent, industry-wide attention.

Currently, the AI industry is believed to be standing at an inflection point because the enormous promise remains intact, but the path from hype to efficient, value-creating deployment is proving more arduous than many anticipated.