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While working at a major renewable energy developer, Varun Sivaram realized that the boom in AI and data centers was outpacing the construction of new power generation, even as wait times for grid interconnections grew longer.

“I realized we couldn’t build our way out of this. We needed intelligent demand,” Sivaram told Fortune.

In a bid to address this need, Sivaram founded a software company called Emerald AI to develop grid flexibility for data centers—essentially reducing power consumption at times of peak load demand on the grid during the hottest or coldest days each year—without harming AI operations.

In addition to heightened energy efficiency, the goal is to speed up the time for AI factories and their power generation to connect to the grid while maintaining “the five nines”—the industry term for 99.999% reliability.

Let’s call it the Disney FastPass approach—now known as the Lightning Lane—for quickly moving ahead in the grid queue.

“We call it flexible-load fast track,” Sivaram said, correcting the Disney reference with a laugh.

Emerald AI’s pitch quickly won financial backing and support from Nvidia, which has helped fast-track the company’s growth and deployment of its AI software. “An AI for AI,” he said.

On March 31, Emerald AI announced the completion of a $25 million strategic funding round with Nvidia’s NVentures, Eaton, GE Vernova, Radical Ventures, Salesforce, Samsung, Siemens, and more, including IQT, the venture capital arm of the CIA and other U.S. intelligence agencies. The round was led by Energy Impact Partners. That brings total funding to $68 million in 16 months since Emerald’s founding.

Last week, Emerald and Nvidia partnered with leading U.S. power producers, including AES, Constellation Energy, Invenergy, NextEra Energy, and Vistra.

And later this year, once a series of pilots prove successful, Emerald and Nvidia will open the first power-flexible, commercial AI factory, Nvidia’s 96-megawatt Vera Rubin AI Factory Research Center, in Virginia.

“The advent of the AI revolution meant that this idea should face primetime because, suddenly, AI factories don’t have enough power,” Sivaram said. “Historically, the data centers had no problem getting power. They’ve been less than 5% of the grid, but now they’re headed toward 25% of the American power supply over the course of a decade.”

As Constellation Energy CEO Joe Dominguez said: “We don’t have a supply problem; we have a peak problem.”

And Emerald’s “grid-friendly AI factories” aim to solve that problem.

The Nvidia fast pass

While Emerald’s software aims to fast-track AI factories, it was Nvidia’s early support that fast-tracked Emerald.

“We’re just excited for the opportunity to commercialize this and push it out there in a bigger way,” said Marc Spieler, Nvidia senior managing director for global energy. “The pilots have been highly successful. We believe this will unlock the potential for getting more AI factories onto the grid faster, utilizing more of the untapped electrons on the grid.”

The longer-term goal is for power-flexible AI factories to unlock up to 100 gigawatts of extra grid capacity from the existing U.S. power grid thanks to increased efficiencies. For context, 100 gigawatts can power roughly 75 million homes.

A grid interconnection study can take years of regulatory reviews, but if you can offer power flexibility at peak demand times, developers may get almost immediate grid hookups, Spieler told Fortune. “Our goal is to have as much connected to the grid as possible and not go behind the meter, not being islanded, by being flexible,” he said. “You can really think of it as highly reactive, demand response at scale.”

And Nvidia was happy to support Emerald’s potential. It’s far from NVentures’ only support announced March 31. ThinkLabs, which has AI focused on compressing power grid studies from years to minutes, announced a $28 million Series A financing round also led by Energy Impact Partners.

“We’re an ecosystem company. We go to market through partners. It doesn’t matter if they’re a Fortune 100, or Fortune 10 company, or an AI startup,” Spieler added. “If somebody has the right idea and is able to execute, we’re going to get behind them and fill the gap.”

How it works

Eight years ago, Sivaram wrote Taming the Sun: Innovations to Harness Solar Energy and Power the Planet.

In it, he documented Microsoft’s work moving workloads between multiple locations to “chase” more clean energy. And Google later worked to move more computational work overnight to utilize wind power at its strongest.

“I thought, ‘Wouldn’t it be nice if, instead of trying to move electrons to where the bits are, if bits could move to where the electrons are?’ Or the bits could be virtually controllable—slowed down or paused,” Sivaram said.

From that idea came the Emerald Conductor platform to “orchestrate” on-site energy resources alongside computational flexibility so projects can connect faster and support the power grid.

“We found that there is inherent flexibility that we can tap into because some AI workloads can be delayed a little bit, and the customers are okay with that,” he said. “Some AI workloads can be shifted from one location to another with latency that is acceptable for customers.

“And there may be resources on the site of a data center, such as a [storage] battery or a [backup] generator, that we can also recruit. Emerald AI finds ways to recruit all these different flexibility levers to provide back to the grid a very precise response,” Sivaram added.

And through tests and pilots, customers’ critical tasks continued to function without degradation, he said. “They kept chugging along at 100% performance.”

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Will advanced generative artificial intelligence (AI) tools destroy established software companies? While these new capabilities are impressive, history suggests the doom-and-gloom commentators have it wrong. New technologies rarely wipe out whole industries. Instead, they change them for the better.

Market cap debates make good headlines, but they miss the larger picture. The biggest risk in the age of AI is not the technology itself. It’s leaders who go with the crowd and confuse consensus with truth.

I have spent decades in the technology world watching smart people make this mistake.

More than once, experts predicted that trade shows like CES would vanish, replaced by online marketplaces. On paper, it made sense. Why travel when you can click? Then COVID hit. After months of isolation, CEOs rushed back to in-person events. They wanted what digital tools could not offer: real relationships, chance meetings, new innovation-producing partnerships, inspiration, and the ability to see and touch innovation in real time. The consensus view forgot something basic about human beings.

I’ve seen the opposite, too. Whole industries stampeded in one direction. Many believed 3D television, the metaverse, and recordable CDs were sure bets. Companies poured in billions to support a shared “wisdom” that was confident and widespread. It was also wrong.

I have made my own mistakes. I was too bullish on the Microsoft “Bob” interface, 3D printing, and some early education software. At the same time, I backed ideas many dismissed — including movie rentals and online video distribution, internet commerce standards, HDTV, and over-the-counter hearing aids. The point is not that some leaders are always right — no one is. The difference is that successful leaders are willing to question the crowd and chart their own path.

That instinct matters more than ever given the transformative capabilities of AI.

Today we are drowning in predictions because change is happening so quickly. AI will erase jobs. AI will create jobs. AI will drive huge gains in productivity. AI will change everything overnight. Some claims will prove right. Many will not.

Much depends not just on the capabilities of technology itself, but the decisions made by leaders in both business and government. I’m encouraged to see the Trump Administration go all-in on AI, releasing both an Executive Order late last year and a follow-on national framework. 

As chair and CEO of the Consumer Technology Association, I believe that this roadmap recognizes that global competition is heating up and the U.S. risks falling behind if innovators must navigate 50 different state rules, our workforce is not ready, or we cannot meet the energy needs of our AI future. If Congress acts to turn this guidance into law, it would unchain AI innovators from a deluge of conflicting state regulations and give them clear, consistent guardrailsto support a vibrant AI ecosystem.

Legislation matters, but so does good judgment. The danger is not in picking the “wrong” forecast. It is assuming the loudest story must be true.

Reality is not that neat.

Effective leaders do not outsource judgment to groupthink. They test what they hear against what they see. They look at incentives, real behavior, and outcomes. They move before it is comfortable.

Often, the warning signs are obvious but ignored because they break with the prevailing narrative.

In the late 1990s, top regulators asked me about the shift to digital television. They worried the transition would hurt broadcast TV viewers. I countered that they should focus on a much bigger issue being ignored: banks pushing mortgages on people who could not afford them. That did not fit the hopeful narrative of endless growth, and that story ended with billions of dollars in federal bailouts.

These stories are not about perfect forecasts. They are about spotting when the standard view has drifted away from facts on the ground. AI is that kind of moment.

Right now, every company faces choices. How should they use AI? How do they manage risk? How do they train and deploy workers? How do they stay competitive? The easy move is to copy what others are doing. Buy what they buy, say what they say, and hope it works out. That is not how you lead.

The winners in the AI era will not be the firms that embrace every tool or reject them all. They will be the ones that think clearly about how AI fits their own mission — and have the courage to act before the answer is obvious.

There is a famous idea that large groups can be good at guessing simple things, like — like the weight of a bull. But running a company, shaping a market, or steering through a new technology wave is not a county fair contest or a prediction market. It is judgment under uncertainty.

In those moments, following the crowd is often the greatest risk. To win big, you must separate from the crowd.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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At 8:15 a.m. Eastern Time today, the price of oil sits at $104.86 per barrel, using Brent as the benchmark (we’ll explain what that means shortly). That’s a decrease of $5.83 since yesterday morning and roughly $30 more than at this time last year.

Will oil prices go up?

Nobody can predict the future path of oil prices with certainty. A range of factors influence how oil trades, yet supply and demand remain the main drivers. When fears of economic slowdown, conflict, or similar shocks rise, oil prices can move sharply.

How oil prices translate to gas pump prices

The price you see at the gas pump reflects more than just crude oil. Also built in are the costs of refining, distribution through wholesalers, various taxes, and the margin your neighborhood station charges.

Crude oil is still the largest single driver of the final pump price, typically representing over half of each gallon’s cost. Spikes in oil prices tend to push gas prices higher in short order. But when oil prices decline, gas prices often ease down gradually, a behavior known as “rockets and feathers.”

The role of the U.S. Strategic Petroleum Reserve

In the event of an emergency, the U.S. maintains a stockpile of crude oil known as the Strategic Petroleum Reserve. Its main goal is to safeguard energy security when disasters strike—think sanctions, severe storm damage, or war. It can also do a lot to ease the pain of sudden price jumps when supply gets disrupted.

It’s not a permanent fix, as it’s more meant to provide immediate support for consumers and ensure critical parts of the economy like key industries, emergency services, public transportation, and so on can keep operating.

How oil and natural gas prices are linked

Both oil and natural gas play key roles as major sources of energy. A big change in oil prices can affect natural gas by proxy. If oil prices increase, some industries may swap natural gas for some segments of their operations where possible, increasing the demand for natural gas.

Historical performance of oil

Oil prices are often measured by two key benchmarks:

• Brent crude oil is the main global oil benchmark.
• West Texas Intermediate (WTI) is the main benchmark of North America.

Between the two, Brent is a better representation of global oil performance because it prices much of the world’s traded crude. It’s also often the best way to review historical oil trends. In fact, the U.S. Energy Information Administration now leans on Brent as its primary reference in its Annual Energy Outlook.

When you look at the Brent benchmark across multiple decades, you’ll see that oil has been anything but consistent. It has experienced spikes driven by wars and supply cuts, as well as crashes linked to global recessions and an oversupply (called a “glut”). For example:

• The early 1970s brought the first big oil shock when the Middle East cut exports and imposed an embargo on the U.S. and others during the Yom Kippur War.
• Prices dropped in the mid-1980s for reasons such as weaker demand and more non-OPEC oil producers entering the industry.
• Prices spiked again in 2008 with rising global demand, but soon crashed alongside the global financial crisis.
• During the 2020 COVID lockdown, oil demand collapsed like never before, bringing prices to under $20 per barrel.

In short, oil’s historical performance has been far from steady. It’s massively affected by wars, recessions, OPEC whims, evolving energy initiatives and policies, and much more.

Energy coverage from Fortune

Looking to stay up-to-date regarding the latest energy developments? Check out our recent coverage:

• The Iran war is accelerating plans for Southeast Asia to go nuclear
• ‘Go get your own oil’: Trump’s message to allies who haven’t backed war in Iran
• The Strait of Hormuz crisis is drawing eyes to another point nearby: ‘the Gate of Tears’

Frequently asked questions

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Employers are under enormous pressure to adopt AI and ditch employees. Investors and CEOs fantasize about slashing costs and boosting margins; every CIO is pushed to come up with an AI plan, to keep up with competitors. Dreams of AI-agent-driven revolutions are everywhere.

But leaders shouldn’t feel like they have to rush to embrace a future that isn’t here yet. There are lots of reasons for caution. Here are nine:

“Experts” have often been wildly wrong in their predictions. The Nobel laureate and AI pioneer Geoffrey Hinton said in 2016, “People should stop training radiologists now… It’s just completely obvious that within five years, deep learning is going to do better than radiologists.” But few if any radiologists have been replaced a decade later. Google cofounder Sergey Brin promised in 2012 that driverless cars would be ubiquitous by 2017. Today, 14 years after that promise (and many subsequent ones by Elon Musk), fully autonomous vehicles remain a limited experiment, available in only a small number of fair-weather cities.

Big Tech wants you to believe it has created artificial general intelligence. That doesn’t make it true. When tech CEOs warn of employment Armageddon, they might be covering their bases in case that actually happens, but then again, maybe they just want you to drive up the valuations of their companies. Take every projection they make with a grain of salt.

When it comes to impact on employment, AI giants’ numbers don’t support their claims. Anthropic’s CEO has been warning of a jobpocalypse, but Anthropic’s own recent research showed the gap between perception and reality. The company projects great potential for what AI might do in fields like finance and architecture. But what it called “observed AI coverage” (a nice phrase for what is happening in the real world) made up a comically small fraction of that theoretical reach. What they imagine AI might do and what it is actually doing are light-years apart. 

Current AI is “jagged” (good at some things but not others), which means it can seldom entirely replace a human. AI can definitely help the productivity of some workers, but even on tasks that AIs are good at, models and agents often make silly mistakes, some of which are hard to detect. And tasks aren’t jobs: Even if AI can do some part of a person’s job, it doesn’t mean it can do all of that person’s job.

Current AI models still have trouble going beyond language. Some white-collar jobs involve only words, but many involve visual comprehension: interpreting images, charts, diagrams, blueprints, maps, and so on. It might seem easy to imagine AI taking over every job, especially if you think of it as some form of magic. But once you realize that current AI is a tool, with strengths and weaknesses, you start to realize that the tech is only likely to displace workers in some professions and not others (and more often will simply augment human jobs). Even in domains like customer service that might seem straightforward, results are often disappointing. The Remote Labor Index focused on jobs that could be accomplished completely over the internet, and found that less than 4.5% could actually be adequately completed by AI agents. 

Most physical labor goes well beyond what current AI can do. Don’t expect AI to replace plumbers, carpenters, auto mechanics, nurses, house cleaners, forest rangers, chefs, appliance repair workers, gardeners, or many other jobs anytime soon.

Many layoffs that have been attributed to AI aren’t really about AI. This may have been the case for the recent mass layoffs at fintech Block; some saw it as an effort by CEO Jack Dorsey to regain investors’ confidence after its stock tanked. In many cases AI may be serving as a fig leaf to cover layoffs that are actually driven by financial underperformance or earlier overhiring.

Some layoffs that are attributed to AI don’t last. I call this the Klarna Effect, after buy-now, pay-later company Klarna, which proudly made massive AI layoffs only to reverse them. Many of the people laid off worked in customer service, but after 11 months Klarna decided that (at least in some cases) “real humans” were required after all. 

Overall impact on productivity and return on AI investment has so far been modest. Every company is investing in AI, but so far most aren’t getting huge returns.

All this could change; probably someday it will—but most likely not until we see more radical advances in AI, which could be a decade or more away. In the meantime, the advice is simple: Don’t focus on replacing humans. Focus on how you can use AI to help the ones you’ve got.

Gary Marcus is an emeritus professor of psychology and neural science at NYU, and the author of six books, including Taming Silicon Valley.

This article appears in the April/May 2026 issue of Fortune with the headline “9 reasons not to freak out (yet) about AI.”

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In my recent Fortune opinion piece on how Trump-era policies are driving capital out of capitalism, I argued that markets cannot function without trust, transparency, and a shared sense of purpose. Since then, the Administration took another step to take that argument to its logical—and frankly terrifying—next step.

The Environmental Protection Agency (EPA) did something extraordinary as a preface to revoking the Endangerment Finding—they revised how they evaluate air pollution rules by not counting the benefits of lives saved and illnesses avoided. The technical term for what disappeared is the “value of a statistical life” (VSL)—previously measured at approximately $11.7 million per person. In its place? Nothing. Zero. As the New York Times reported, when it comes to regulatory decisions on fine particulate matter and ozone, a human life now carries a market value of $0.

The EPA’s rationale is bureaucratically brutal—and cruel and radical in its implications. The agency claims that quantifying health benefits is too uncertain. Unverified compliance costs to industry, however, are conveniently concrete. To be clear, the ledger now tallies only what companies pay, not what people lose—the asthma attacks, hospitalizations, shortened lives, or deaths. The result isn’t neutrality; it’s draconian ideology.

If a human life has no economic value, then what does? Why do we have a healthcare system at all? Why invest trillions in hospitals, pharmaceuticals, or medical research if the outcome—people living longer, healthier lives—registers as meaningless? By this logic, emergency rooms are sunk costs and preventative care is a frivolous indulgence.

Push this ideology a step further and the entire economy implodes. If humans have zero intrinsic value, and corporations derive value only from human spending, then the sum total of economic value is also zero. Congratulations: every stock index should trade at $0; the S&P 500 becomes a philosophical thought experiment while Bloomberg terminals blink into existential silence.

This is not hyperbole. It is the unavoidable math of the EPA and the Trump administration’s position on the value of human life. If a company dumps toxic waste into a local river and your children get sick and die, there is no value lost, there are no damages, no liability—the ultimate rationale for Milton Friedman’s externalization of costs.

As University of Chicago climate scholars pointed out, removing health benefits from cost-benefit analysis doesn’t make regulation more “objective;” it simply rigs the equation so that pollution is cheap and people are disposable.

Michael Greenstone, an environmental economist, said the change could result in dirtier air, undercutting the gains made since Congress strengthened the Clean Air Act in 1970—a law that added 1.4 years to the average American’s life expectancy since. “Clean air is one of the great success stories of government policy in the last half-century,” Dr. Greenstone said. “And at the heart of the Clean Air Act is the idea that when you allow people to lead longer and healthier lives, that has value that can be measured in dollars.”

A worldview in which the value of a human life is zero fits neatly within the broader Trumpian project. People are reduced to abstractions or enemies. Haitians. Somalis. Journalists. Comedians. Musicians. Stephen Colbert and Bad Bunny are assigned a value of zero because they are inconvenient, annoying, or insufficiently deferential to power. Does anyone in Trump’s orbit have a value? Melania? Eric? 

For decades, cost-benefit analysis—imperfect as it is—served as an acknowledgment that human life matters in economic decision-making. Assigning a dollar value to life was never meant to cheapen it; it was meant to ensure it wasn’t lost. Removing that value doesn’t make policy more rigorous — it makes it morally and ethically vacuous.

Capital markets are exquisitely sensitive to signals. When the government declares, implicitly or explicitly, that people don’t matter, investors should listen. Because an economy that prices human life at zero is one that ultimately has zero value.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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At Adobe, the AI era is a test of whether a company built on iconic creative tools can remake itself fast enough to remain indispensable without losing the professionals who made those tools matter.

Anil Chakravarthy is at the center of that effort. The former Informatica CEO now leads Adobe’s customer experience business as the company faces mounting pressure to stay ahead of the disruption bearing down on products such as Photoshop, Illustrator, and Acrobat. Such pressure has also shown up in Adobe’s stock. Despite record first-quarter fiscal 2026 revenue of $6.40 billion, its shares have fallen as investors worry that fast-moving AI agents and other new tools could weaken demand for parts of the traditional seat-based software model. 

The concern underlying both pressures is the same: Adobe has to keep pace with AI without undermining the trust of enterprise customers that depend on its software for critical business functions. Chakravarthy points to moments like the Super Bowl and the Olympics, when Adobe systems are expected to perform flawlessly under intense pressure. In those environments, he says the challenge is determining which parts of the company should move at AI speed and which must still move at the pace of customer trust.

“The fastest moving AI models and the AI companies, let’s say they’re moving at 100 miles an hour,” Chakravarthy says. “The customers are moving at 10 miles an hour.”

Caught between speed and trust

That gap leaves Adobe in a difficult position. If it moves too slowly, it risks looking dated in a market being reshaped by AI. If it moves too quickly, it risks weakening the reliability that large customers still pay for. Inside a company of more than 30,000 people, that split can create what seems like “whiplash,” as teams are pushed to move at AI speed without disrupting the software customers depend on.

“If we just move only at their speed, then we’re going to be slow, and we’re not going to be their trusted partner three years from now,” Chakravarthy says. “If we move completely at 100 miles an hour, like the AI is moving and break everything, including the software that currently works for them today, well, we won’t be their trusted partner three years from now either.”

That tension has grown more significant since Adobe said last month that longtime CEO Shantanu Narayen will step down once a successor is found. The transition has focused internal attention on whether the company’s future depends more on preserving its creative DNA or on doubling down on the enterprise discipline required to navigate the AI shift.

Either way, the stakes are rising as Adobe tries to satisfy enterprise customers, reassure investors, and hold on to a creative community wary that the company is prioritizing scale and efficiency over craft.

A company moving at two speeds

Chakravarthy sees the current moment as a genuine platform shift, on the scale of the move from mainframes to client-server computing, then to the internet, and now to mobile. But this transition poses a more destabilizing question for incumbents. The issue is no longer whether software includes AI. The question is whether conventional SaaS products will still feel current a few years from now.

For Adobe, that implies something larger than a product refresh. The company built its empire on powerful tools that users controlled directly. The model now taking shape gives software a more active role within the workflow itself, carrying out tasks and advancing work rather than waiting for instructions at every step.

Already, AI has lowered the barrier to producing content. Users can generate images, videos, copy, and campaigns with a growing number of tools with startling ease. As that capability becomes commonplace, the question shifts from who can produce content fastest to why anyone still needs an expensive, sophisticated software stack at all.

Chakravarthy’s answer rests on the distinction between generation and execution. Producing content is becoming easier, he acknowledges. Turning that draft into something a company can actually use, trust, govern, and recognize as its own is harder. That is where Adobe is trying to place its value.

“The more ubiquitous base capabilities become, the harder it actually becomes to differentiate and stand out,” Chakravarthy says. “And that’s where we believe we will continue to have a very vital role to play.”

The fight over what still matters

In that view, AI does not eliminate the need for software so much as shift its value toward brand consistency, workflow integration, enterprise controls, and creative distinctiveness. In a market crowded with capable models and fast-moving startups, the stronger position may lie in helping customers personalize content at scale without sacrificing quality. Chakravarthy argues that this is a more durable place for an enterprise company to compete than simply producing the cheapest image or fastest draft.

That logic may make sense in the boardroom. It is less reassuring to many of Adobe’s core creative users, who worry that in trying to serve everyone, Adobe could weaken the depth and control that made its tools indispensable in the first place. Creatives have been blunt about Firefly, Adobe’s generative AI system for creating and editing images and other content built into its products. Some question how the models were trained, whether copyrighted work was used, and whether tools like this will reduce the value of human creative labor.

That tension runs through the company’s public posture on AI. Adobe wants to present its new tools as accelerants for creativity rather than replacements for it. It wants to promise greater speed without implying that skill matters less, and it wants to reach a broader user base without signaling to core professionals that AI will devalue their work. Those are difficult positions to hold at once, especially as AI economics push software companies toward automation and volume.

Still, Chakravarthy’s bet is that originality, identity, and taste matter more when everyone can make content quickly and cheaply. In that world, Adobe does not need to win by being the only company that can generate content. It needs to win by helping customers turn generated material into work that feels unmistakably their own.

This story was originally featured on Fortune.com