Models & Labs · United States

The AI boom’s missing balance-sheet question: What has actually been verified?

Credited to SiliconANGLE · siliconangle.com · 14h ago

I spent 13 years on the floor of the New York Stock Exchange. One lesson stayed with me long after the trading jackets disappeared: A transaction could be executed, documented and perfectly valid while the assumptions that made it attractive proved less durable than anyone expected.

That distinction matters in the artificial intelligence infrastructure boom. We can believe AI will transform productivity and still ask whether each financing, data-center expansion or long-term capacity agreement is supported by evidence that can survive the investment’s life.

The industry is making commitments across different clocks. Compute contracts may run for years. Debt can mature decades from now.

Servers have much shorter technology cycles. Power infrastructure, transmission upgrades and buildings may outlast several generations of the equipment they were built to support. Meanwhile, enterprise customers are still learning what they will use, how intensively they will use it and what they will pay.

None of this proves the investments are unsound. It does create a question that belongs beside revenue forecasts and capital budgets: What exactly has been verified at the moment an institution decides to rely on a promise?

Contracted demand is not the same as productive utilization

An agreement to purchase compute can be commercially meaningful. It may support financing and demonstrate customer commitment. But it does not by itself establish that the underlying workloads will remain economically productive at the contracted scale, or that the customer will renew on similar terms once technology and prices change.

A serious review must separate the existence of the contract from the economic assumptions behind its duration, usage profile, counterparty capacity and renewal expectations. Contract enforceability and economic durability are related; they are not interchangeable.

Physically deliverable capacity is another milestone

A data-center campus can be announced, financed and partially constructed before all of its necessary electricity, cooling, network and equipment dependencies have become operational. A power purchase agreement is not itself proof of deliverability at the exact commissioning date. GPU orders are not proof that a facility can run the ordered cluster at the planned duty cycle.

For infrastructure planning, I use a simple sequence: requested load, evidenced load, deliverable load, energized load, utilized load and sustained demand. These are not six fancy names for the same megawatt. They describe different claims, each requiring different supporting records.

That distinction is already relevant to public grid decisions. Persistence Analytics Group has submitted comments in MISO’s Large Load Working Group proceedings on load forecasts, application and study processes, paired-resource studies and cost transparency. The recurring question is how planners preserve alignment when a proposed project’s capacity, timing or other material conditions change after an earlier study or commitment.

A guarantee allocates exposure; it does not eliminate it

Financial engineering can provide meaningful protection. Minimum payments, parent guarantees, collateral requirements, termination provisions and residual-value commitments can shift or mitigate losses. They should be evaluated on their actual terms and credit strength.

But a contractual remedy is not the same as the continued usefulness of the underlying asset. A guarantee may support recovery from a counterparty while leaving the investor dependent on that counterparty’s resources, enforceability, timing and the value remaining after a disruption. Those are separate assumptions that deserve separate evidence.

This is especially important when apparently distinct exposures rely on a small set of interconnected customers, infrastructure providers, funding sources or technology assumptions. Diversifying contracts does not necessarily diversify the dependency underneath them.

The missing control is revalidation

Initial diligence usually asks whether a project is supportable at approval. A more durable approach asks what must remain true afterward — and which changes require the original case to be revisited before additional money is committed.

Consider a campus whose customer delays its ramp, whose power availability moves to a later year or whose equipment changes materially. Each event may be manageable on its own. But if the original financing or infrastructure decision assumed the earlier configuration, someone must decide whether the updated evidence still supports the next commitment.

That is a decision gate, not a prediction about whether AI succeeds. It calls for a documented chain: the representation being relied upon; the assumption embedded in it; the evidence supporting that assumption; the dependencies that must hold; the party bearing downside exposure; and the material change that would require reassessment.

Different specialists own different pieces. Engineers assess technical deliverability. Lawyers assess contractual rights. Accountants and finance teams assess financial statements and cash flows. Independent decision assurance asks whether those records, taken together, actually establish the proposition the capital allocator is relying on — and whether known gaps have been made explicit.

The question CFOs should put on the table

The productive-utilization debate is essential. So is the question of who will pay for AI intelligence, and at what price. But before authorizing the next investment, a chief financial officer or investment committee can ask an even more immediate question: Which assumptions are verified facts, which are binding commitments, which are conditional forecasts and which need to be revalidated because something material has changed?

A sector can produce transformational technology and disappointing returns on some of its assets. We have seen that distinction in markets before. The lesson is not to stop investing. It is to stop treating execution, delivery, utilization and durable economic value as if they were the same event.

On the trading floor, the confirmation of a trade was the beginning of understanding the position, not the end. AI infrastructure deserves the same discipline.

Neil P. Osnato is founder of Persistence Analytics Group LLC, an independent Decision Assurance™ and Infrastructure Assumption Verification™ firm. He previously spent 13 years trading on the New York Stock Exchange floor. His work focuses on the documentary evidence supporting consequential infrastructure and financial decisions. He wrote this article for SiliconANGLE.

Selected public background: MISO Large Load Working Group stakeholder feedback (LLWG-2026-7 and LLWG-2026-8), September 2026; The Bond Buyer, “Deciding About Data Centers,” October 28, 2026.

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Chips & SupplyUnited States

Nvidia’s AI Chip Exports Emerge as Target in Democratic Congress

Credited to Bloomberg$ · bloomberg.com14h ago

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Nvidia Corp. is in for tougher scrutiny from a new Democratic majority in Congress that’s set to demand answers from Chief Executive Officer Jensen Huang about whether the company is doing enough to keep its coveted artificial intelligence chips out of the hands of China and…

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Models & Labs · United States

Fearing AI disruption, white-collar workers are quitting their jobs

Credited to The Guardian · theguardian.com · 15h ago

While there’s no evidence linking AI to mass layoffs, the tech has already ‘changed worker psychology’

W hen artificial intelligence researcher Mrinank Sharma quit his job at Anthropic earlier this year, the news rippled across the internet. In his resignation letter, posted on X and viewed 15.3m times, Sharma warned of a world “in peril” from the technology he had helped build.

“It is clear to me the time has come to move on,” he wrote. His next move: a poetry degree.

In the months following Sharma’s resignation, AI industry defectors and workers outside of Silicon Valley alike have echoed his sentiments.

Bracing for disruption forecasted by the AI industry, white-collar professionals are voluntarily leaving established careers to pursue professions they feel are more secure or more aligned with their values. Workers across industries – from tech to public relations to consulting – say AI changed the substance and stability of their jobs overnight. They worry about AI jeopardizing their cognitive abilities, contributing to climate change, undermining value to customers and compromising security. Some, like Sharma, have left their fields entirely, shifting to vastly different careers.

So far, economic data shows that AI has not wreaked havoc on the workforce. It’s created about 1m US jobs, compared with 200,000 layoffs attributed to AI, according to the Economist. But as AI executives continue to warn of disruption, 71% of Americans believe the technology will lead to fewer jobs over the next 20 years, according to the Pew Research Center. As workers fear for the future, though, they’re making major decisions about the present.

The shift on the ground

Some white-collar workers have begun to look beyond the dreams they were sold as AI reshapes jobs.

After spending nearly a decade in public relations, 30-year-old Vanessa Hsia decided to seek a master’s degree in comparative literature.

“I felt very burnt out and concerned with the direction of the industry: chasing quantity over quality, relying on AI rather than critical thinking, prioritizing short-term wins over long-term strategy,” Hsia posted on LinkedIn. “I have no idea where this path will lead, but I’m taking my own advice: the world is on fire, might as well do what you want.”

Hsia and her former colleagues in PR feared AI would eventually replace them, she said, noting that specific tasks like pitching reporters over email were already being automated.

“I’ve been sold this dream that if I work really hard in my career, I’ll climb the ladder, that I will be respected in my field and paid a fair wage,” she said. “But then you have this technology that exacerbates the fact that, actually, no, that’s not guaranteed.”

The safe, tidy way of life defined by a bachelor’s degree, desk job and career ladder is in its death throes, accelerated by the encroaching tidal wave of AI, Hsia contends. It’s an opinion echoed by other white-collar workers.

“The career that I’ve had for 10 years is kind of fundamentally gone,” said a software engineer at a big tech company, who requested anonymity for fear of retribution. The engineer and his peers believe their roles will soon cease to exist as AI automates coding. “We all thought we would be software engineers for another decade or two,” he said, adding that he’s considering leaving the industry. “Now it’s completely changed.”

For some workers, AI has accelerated feelings of instability and purposelessness they already had in their careers. Hanna Horvath spent most of her twenties as a content marketer and editor. But earlier this year, the 29-year-old left her editing job at personal finance platform Bankrate to freelance in copy writing and social media marketing, serve as a financial planner and publish a Substack newsletter.

Even before the introduction of AI, Horvath felt like her day-to-day work had drifted away from what she was hired to do, becoming “meaningless”. And after witnessing multiple layoffs at her company, she no longer felt assured by the promised stability of a nine-to-five job.

Once her company mandated AI use, Horvath felt even worse. “AI doesn’t have to replace your job to fundamentally change your relationship to your job,” she said. “So many of us were just becoming project managers instead of actually editing content or writing content.”

On LinkedIn, Horvath wrote that though the larger conversation about AI has been about job displacement, the tech “has already changed worker psychology”, referring to people’s fears and loss of purpose. Now, with varied streams of income, Horvath said she feels more financially secure and less vulnerable to AI replacement.

Some white-collar workers are effecting complete professional 180s: from tech to law enforcement and advertising to acting.

Chris, who asked to go by a pseudonym for fear of retribution, left his software engineering job to join the police department of a major east coast city last year. AI had taken “a lot of the thinking” and fun out of coding, and he feared being displaced, he said.

“There’s no job security in corporate anymore,” said Chris, who spent more than a decade doing white-collar work. “You’re expendable, you’re an asset.”

Chris counts himself lucky to have left software engineering as his employer started to push AI on his team. Now, as a police officer, “there’s a satisfaction I never got in engineering … In a lot of ways I am happier,” he said, referring to working directly with his community.

Similarly, Kara, who asked to go by a pseudonym to speak candidly about her former career, said leaving her job as an advertising producer to focus on acting while moonlighting as a barista made her “feel spiritually lighter”.

Kara had experienced increasing burnout, much of which she attributes to being instructed to use AI despite her concerns over legality and creative use. She said her team was encouraged to use AI-enabled production tools like Runway, which generates AI videos and images, and ElevenLabs, an AI voice generator, to supplement its creative work. As the producer, she was charged with handling legal clearance at a time when AI-generated video and audio based on a human’s likeness is still a gray area.

Her employer “compelled” the team to incorporate generative AI, then “in the same conversation, warned about the legal dangers”, she said. The result was constant stress.

Kara also worries about AI generating art after training on the work of human artists. Between her creative opposition and the stress of overseeing AI’s legal use, the job “became untenable”, she said. She’s been transitioning to what she calls her “survival job” as a barista “quite happily”, she added.

Stoking job fears

For years, the architects of AI have insisted that their technology poses immense economic threats. In May 2025, Anthropic CEO Dario Amodei predicted AI would lead to the displacement of half of all entry-level white-collar jobs by the end of the decade. That carnage has yet to materialize.

This year, Amodei doubled down on his stance in an essay on his website.

“AI is increasingly matching the general cognitive profile of humans, which means it will also be good at the new jobs that would ordinarily be created in response to the old ones being automated,” reads his essay, which calls AI a “general labor substitute for humans”.

Workers’ fears likely stem from company leaders’ warnings and the layoffs they’re seeing, even though data doesn’t show AI is causing job loss, said Brendan Moore, a University of Notre Dame economics professor who’s studied worker sentiment on AI and job insecurity.

“Between the proclamations from tech CEOs saying there are going to be mass layoffs, to many large technology and telecommunications companies that are citing AI as reasons for layoffs … people are saying: ‘Oh, this is due to AI,’” Moore said, adding some companies likely would’ve downsized regardless.

Workers still drive the economy, so if large numbers of them change course, wages, prices and economic growth could be affected, Moore said. Following the “great resignation”, when workers quit en masse during the Covid-19 pandemic, wages and consumer inflation rose. If the fear of AI provokes mass resignations, similar economic disruption could materialize.

“Just the fear of job loss alone, even if mass job loss never comes, can affect the economy,” Moore said.

Though AI leaders have walked back their dire economic predictions in recent months, it may be too late to reverse negative worker sentiment, which has garnered the attention of lawmakers.

Earlier this year, the Vermont senator Bernie Sanders introduced a bill to impose a federal moratorium on the construction of new AI datacenters until safeguards were in place for workers facing job loss. AI leaders “do not want to just replace specific jobs. They want to replace workers,” he wrote in an op-ed for Fox News, calling AI a “nightmare” for the working class.

At the same time, some tech executives are calling out their industry’s failure to articulate a positive vision for the future.

“It is surprising that the discourse from many developing AI is so filled with doom,” Mark Zuckerberg, CEO of Meta, wrote in an August essay. “I do not understand why anyone who believes that AI will eliminate most jobs and much of humanity’s relevance would rush to build that future.”

For workers, the issue is much simpler: AI is destabilizing their professional lives now – so they are taking matters into their own hands.

“Uncertainty is making people anxious,” said Horvath, the financial planner. But “you can’t have nihilism, because at the end of the day, it only hurts you”.