What White House Executive Orders Tell Us About AI Risks Ahead
Image generated with Midjourney.
Building with AI today feels a bit like borrowing your parents’ car at 16—you want to go as far as you can, as fast as you can, without worrying about maintenance, gas mileage, or the cost of speeding tickets. But just as a teenager eventually grows into an adult with an insurance premium, those practical considerations will catch up with all of us.
It’s a fun time to work with AI. However, development is moving at breakneck speed, and with it comes the temptation for organizations to chase short-term wins without accounting for long-term considerations. Case in point: The White House’s recent release of a trio of AI executive orders didn’t land with much immediate impact for most companies, but they’re interesting as signals of the broader forces already reshaping the AI landscape. I’m going to focus on a review of the two with the most future relevance.
For leaders in highly regulated industries, these indicators are a chance to read the winds. The policies themselves may only touch a handful of large players in the short term, but they highlight underlying currents that will affect every organization building on AI in the future. The speed of those winds depends on where your organization sits in the AI food chain. Most brands will fall in the third tier, Secondary Consumers of AI, and so this discussion will focus there.
Read on for thoughts on how to watch the AI horizon while keeping your eyes on the road in front of you.
Exporting the American AI Technology Stack
Let’s look at the first executive order, centered around exporting the American AI technology stack. It reads, “The Secretary of Commerce, in consultation with the Secretary of State and the Director of the Office of Science and Technology Policy (OSTP), shall establish and implement the American AI Exports Program (Program) to support the development and deployment of United States full-stack AI export packages.”
TL;DR: Most companies won’t be able to apply directly for this. The main takeaway for tech leaders is that the highest levels of government are concerned about the threat if companies outside the U.S. become truly competitive in AI.
Details:
The primary companies likely to benefit are the major players producing AI or core components—OpenAI, Anthropic, NVIDIA—for others to consume. The average company that has simply added an AI chatbot to its platform doesn’t have a play here. Some specialized providers with targeted vertical solutions could also apply. The order promises economic incentives funded by the Department of Commerce, though details are not yet specified.
So what’s the impact for everyone else? In the next year, probably very little. The companies you buy AI from may apply for incentives to export that same technology overseas. The Secretary of Commerce has 90 days (from July 23rd) to open applications, then applicants have 90 days to respond—so roughly six months before awards, and additional time before deployment.
Over the longer term, things get more interesting. The White House is aiming to give more global consumers easy access to American AI tech, with the goal of making U.S. stacks the global standard. This will clearly benefit players like NVIDIA, OpenAI, and Anthropic (if they apply and win), who hope to cement their leadership against competitors like DeepSeek.
For everyone outside that rarefied group, there are two potential impacts:
Increased competition driving prices down or options up for the AI products they consume*
Increased competition driving prices down or options up for the AI products they produce*
(*See note below.)
The asterisk above matters because much of the price competition will come from overseas. If you’re in a highly regulated industry and can’t use products that send data abroad, the effect will be diminished—or absent altogether.
There are further downstream effects. If most U.S. tech companies are building on the same major domestic players, there’s a degree of risk reduction if those players dominate. The pessimistic view is that if overseas firms like DeepSeek become global leaders, American companies built on U.S. stacks could be left with tech debt. They’d face the costly choice of moving to overseas AI technology or staying with an underperforming platform. Many things would have to align for that scenario to unfold, but the potential cost is large enough to keep in mind.
That’s not the argument you’d make at a press conference, since it doesn’t fit into a soundbite. But for those inside the industry, it’s the most salient argument in favor of the order.
On the flip side, if U.S. players—or a single stack—become too dominant, competition could wither. That’s typically a negative for consumers of AI, echoing criticisms of Big Tech players like Google and Meta in recent years. The White House seems willing to “burn that bridge when they get to it.”
Other relevant signals
While the first executive order focuses on exporting U.S. technology, the second hits much closer to home. Another executive order signed the same day aims to speed permits for AI data centers. While most regulated-industry companies won’t be building $500M+ data centers, the downstream effects could impact everyone consuming AI.
TL;DR: For consumers, the main effect would be flattening the AI cost curve for SaaS-type AI products. Right now, platforms are keeping prices low to drive adoption, but without intervention, costs could rise sharply as demand outpaces supply. Faster data center approvals could help—though likely at the expense of environmental protections.
OpenAI, for instance, is projected to hit $12.7B in revenue this year, but Bloomberg estimates it needs nearly 10x that to be profitable. With ~700M weekly active users out of 5.64B adults online, the math doesn’t add up unless prices rise, costs drop, or both. The administration seems to recognize that lowering infrastructure costs is one way to soften this looming reckoning.
The question is what happens with environmental regulations. If weakened now, they’re likely to swing back harder later, creating costly remediation requirements.
What’s a leader to do?
The forces at play—tech debt, competitive risk, rising infrastructure costs—are macro-level, beyond the influence of all but a handful of massive organizations. So what can a savvy Fortune 500 or even Inc. 5000 leader do?
First, acknowledge that today’s cost structure is a temporary gift. Costs are likely to rise, both as your AI investment scales and as the underlying components (compute units, tokens, SaaS subscriptions) increase in price.
You need a plan to offset these costs, and it shouldn’t rely solely on efficiency. Efficiency gains almost always underperform relative to expectations. Leaders should also explore revenue-side strategies, such as:
AI-driven product upsell recommendations
Creating or enhancing a premium-tier product (especially effective in B2B)
Using AI to reposition offerings as premium and charge accordingly
We generally don’t recommend charging extra solely for AI features outside of a premium tier. Adoption is still uneven, and it’s more valuable to get broad uptake than to squeeze a few dollars from a smaller user base.
Navigating the AI Ecosystem With Clear Eyes
The latest executive actions make one thing clear: the U.S. government views AI not only as a competitive advantage but as a matter of national strategy. For most organizations, these moves won’t create immediate opportunities to apply for incentives or programs—but they will shape the ecosystem your AI investments rely on. Costs will rise, competition will shift, and the global balance of influence may tilt in unpredictable ways.
Leaders in highly regulated industries can’t control which companies win federal incentives or how the cost curve ultimately bends, but they can control how they prepare. That means recognizing today’s favorable economics as temporary, building strategies to offset future cost increases, and aligning AI investments with revenue growth—not just efficiency gains. It also means avoiding the trap of overconfidence, keeping a realistic view of what AI can and can’t do, and focusing efforts on the use cases that truly matter to your customers.
The companies that thrive won’t be those chasing hype cycles, but those that marry innovation with discipline: deploying AI in ways that build trust, create value, and position them to weather whatever shifts the AI landscape brings next.