“I won’t go into particular names,” Powell told reporters after the Fed’s policy meeting, “but they actually have earnings.”

“These companies… actually have business models and profits and that kind of thing. So it’s really a different thing” than the dot-com bubble, he added.

The comments mark what seems like Powell’s most direct acknowledgment yet that AI’s corporate build-out—spanning hundreds of billions of dollars in data-center and semiconductor investments—has become a genuine engine of U.S. growth. 

A productivity play, not a rate-sensitive one

Powell emphasized that the explosion of AI spending isn’t being driven by monetary policy—or by cheap money.

“I don’t think interest rates are an important part of the AI or data-center story,” he said. “It’s based on longer-run assessments that this is an area where there’s going to be a lot of investment, and that’s going to drive higher productivity.”

That remark cuts against one market narrative that loosening financial conditions might be fueling an asset bubble in tech. Instead, Powell suggested that the AI build-out is more structural: a bet on the long-term transformation of work. From Nvidia’s on track to have half a trillion dollars in revenue to Microsoft and Alphabet’s multi-hundred-billion-dollar capital-expenditure plans, the scale is unprecedented. But, in Powell’s telling, it’s also grounded.

Goldman Sachs agrees. In a research note titled “The AI Spending Boom Is Not Too Big,” chief U.S. economist Joseph Briggs argued that “anticipated investment levels are sustainable, although the ultimate AI winners remain less clear.” 

Briggs and his team estimated that the productivity unlocked by AI could be worth $8 trillion in present value to the U.S. economy, and potentially as much as $19 trillion in high-end scenarios.

“We are not concerned about the total amount of AI investment,” the Goldman team wrote. “AI investment as a share of U.S. GDP is smaller today (<1%) than in prior large technology cycles (2%–5%).” In other words, there’s still plenty of room to run.

Powell’s framing echoes that view: the AI race, while frothy at times, is being financed mainly through corporate cash flow rather than speculative debt.

A real-economy impact

Powell noted that the investment wave is showing up in the real economy. “It’s the investment we’re getting in equipment and all those things that go into creating data centers and feeding the AI,” he said. “It’s clearly one of the big sources of growth in the economy.”

Those remarks align with private-sector estimates. JPMorgan economists have projected that AI-related infrastructure spending could add up to 0.2 percentage points to U.S. GDP growth over the next year, roughly the same annual boost that shale drilling delivered at its peak.

The boom has already pushed industrial power demand to record levels and forced utilities to fast-track grid expansion, confronting with the realities of a too-slim grid. The AI boom isn’t just reflected on paper, in other words: Powell is talking about cranes, concrete, capital goods. 

Not without caution

Still, Powell didn’t give AI a free pass. He stressed that while the current investment surge looks healthy, it’s too early to call it a permanent productivity revolution.

“I don’t know how those investments will work out,” he said. 

For all its promise, the AI economy is unevenly distributed: capital-intensive and concentrated among a handful of firms. Economists warn that productivity gains from AI will take years to filter through the broader workforce, and that automation could suppress hiring in sectors now driving demand.

Powell acknowledged as much when he noted that many recent layoff announcements from major corporations “are talking about AI and what it can do.”There’s an irony, there: the same technology boosting output may also slow job creation—one of the central bank’s two mandates.

Powell said job growth, adjusted for statistical over-counting, is now “pretty close to zero.”Powell says that, unlike the dot-com boom, AI spending isn’t a bubble: “I won’t go into particular names, but they actually have earnings”

DistroKid has launched a new direct-to-fan sales platform marking the music distributor’s push beyond audio and video distribution services and into merchandise sales.

The new feature, called Direct, allows musicians to set up online stores to sell merch like T-shirts, tote bags and mugs printed with album artwork.

The company is rolling out the platform in beta to select artists before a broader release in the coming weeks, DistroKid said on Wednesday (October 29).

While DistroKid will handle production and shipping through on-demand manufacturing, artists using Direct will retain all revenue from sales. The service costs $6 per month.

Matthew Ogle, Chief Product Officer, DistroKid, said: “Direct is one more way DistroKid helps artists at every step — before, during, and after they release music.”

“We’re building simple tools that let artists share what they create, from music to merch and beyond, and connect directly with the people who care about them most.”

“Direct is one more way DistroKid helps artists at every step — before, during, and after they release music.”

Matthew Ogle, DistroKid

The move represents DistroKid’s effort to diversify. Founded in 2013, the New York-based company claims that it handles 30% to 40% of new music releases globally and serves more than 2 million artists.

Direct builds on technology from direct-to-fan platform Bandzoogle, which DistroKid acquired in 2023. The company plans to add more merch options and fan engagement tools in the coming months.

Several artists have started testing the platform. Jazz musician Devin Gray said: “DistroKid’s new Direct store makes that process seamless. It takes the stress out of designing, setting up, and shipping merch, so I can focus on creating music.”

“DistroKid’s new Direct store makes that process seamless. It takes the stress out of designing, setting up, and shipping merch, so I can focus on creating music.”

Devin Gray, Jazz Musician

Singer/songwriter Raye Zaragoza added: “DistroKid just gave indie artists the freedom to run a full-scale merch store without needing to personally front the money or the space for the inventory. It’s also more sustainable since you are only printing what is ordered. Not to mention DistroKid giving artists 100% of the earnings.”

Los Angeles–based singer/songwriter Jeddy Knox said: “I’d always wanted to launch merch, but it all felt too complicated to manage. DistroKid made it easy, though – I chose my artwork, picked the products, and my store was live within minutes. It made the whole process fast and painless.”

The merch push puts DistroKid in competition with platforms like Bandcamp, which already offer direct sales tools to musicians. Bandcamp was acquired by video game maker Epic Games in 2022, which sold it to music licensing platform Songtradr in 2023.

Last month, Bandcamp launched a new subscription service called Bandcamp Clubs that gives users access to monthly record selections, listening parties, recommendations and exclusive artist content.

For DistroKid, the new merch service marks its latest offering to users after integrating with Spotify in June to allow artists to upload music videos to Spotify via its DistroVid service.

Music Business Worldwide

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In fact, the percentage of companies scrapping a majority of their AI initiatives jumped from 17% to 42% this year, according to S&P Global Market Intelligence. While the technology is real, the operating model isn’t. 

At ServiceNow, we’ve led AI through shared leadership—not from the top down. The collaboration between technology and business functions may take different forms, but the goal remains the same: make AI deliver measurable business outcomes and avoid siloed innovation at all costs. Specifically, we’ve built a pact between the CIO and COO that treats AI as a business system and experience layer, with shared outcomes and measurable results. We’ve already realized $350 million in value from productivity and time savings, while focusing on innovating across the business with a shared approach to AI across all departments. 

This strategy worked for us and is a blueprint that any organization can adopt. If you want to escape pilot purgatory and move AI into production, here are five practical ways to operationalize AI at scale and see real business value in the first 90 days.  

Start with the work, not the model 

Too many companies get caught up in experimenting with the latest large language model before identifying where it can solve real business problems. Start with three enterprise use cases with a direct line to your P&L. Then set public, CFO-approved yardsticks: cycle time, deflection rates, cost-to-serve. 

At ServiceNow, we identified the key use cases that drive the most value for employees and customers, starting with help desks. ServiceNow has a fully autonomous IT service desk, with 90% of incoming tickets handled by AI. For customer support, 89% of incoming tickets are deflected with customer self-service for most basic inquiries, and 50% faster case resolution times for more complex issues. This created a scalable model we extended across HR, finance, sales and more. Not a pilot. Not a demo. Real outcomes. 

Fix data chaos first 

AI fails because it’s guessing. When your data is fragmented and unstructured, AI lacks the context to make smart decisions. 

Before layering in new models, invest in your data fabric—relationship graphs, lineage, reliable labels. Make your data human-readable, so AI can reason like a human would. 

Govern AI like a business system 

Governance can’t be a one-time committee review of deployed AI models and tools. It must be an operating discipline. It’s critical to establish a central control tower that oversees every agent and model, from provisioning and permissions to observability and rollback. 

Think of it like cybersecurity or finance. You don’t scale those functions without oversight. The same must be true for AI. 

Redesign work for human and agent teams 

The goal isn’t to replace humans. It’s to eliminate the digital friction that slows them down. 

Microsoft’s 2025 Work Trend Index shows that employees are interrupted every two minutes by meetings, messages, or alerts. Nearly half of workers say their day feels fragmented and chaotic. That’s not a productivity gap—it’s a structural failure. 

We start by mapping real journeys, not just workflows on paper. And we embed agents at the handoff points so people spend less time copying and pasting, and more time solving meaningful problems. 

Make the CIO–COO pact real 

Here’s how we structure our partnership: 

• One backlog, two owners: Fund value streams, not departments. 
• Dual-speed governance: Sandboxes move fast; production enforces rigor. 
• Monthly AI dashboard: Track outcomes like time saved, risk reduced, satisfaction improved. 
• Upskilling as policy: Incentivize managers for human-in-the-loop quality, not deployment quantity. 

This goes beyond collaboration and gives all leaders co-ownership of bigger business transformation. 

90-Day AI playbook 

Turning strategy into execution doesn’t require a full digital overhaul—it requires structure, speed, and clear accountability. This 90-day playbook breaks down the daunting task of AI transformation into four focused sprints. Each phase is designed to build momentum, prove value early, and give business leaders the clarity they need to scale with confidence. 

These steps get AI into production as the building blocks of the autonomous enterprise, where AI agents, data, and workflows operate in sync to drive resilience, speed, and growth. 

Run this sequence to move from pilots to AI value: 

Days 0–14: Choose 3 use cases with CFO-approved metrics. Define clear guardrails (privacy, auditability, bias). 

Days 15–45: Connect the data you already have. Label key entities. Build the control tower. 

Days 46–75: Deploy minimum viable AI workflows. Measure deflection, dwell time, and user satisfaction. This is the time to test, iterate, and improve.  

Days 76–90: Double down on what works. Publish results. Fund the winners. Retire the rest. 

What success looks like 

You’ll know it’s working when: 

Your board asks, “What else can we hand off to AI?” 

Employees spend less time toggling between tools and more time delivering value. 

Governance reviews are boringly predictable because the system just works. 

Why it matters now 

IDC estimates generative AI could add up to $22 trillion to the global economy each year by 2030. But that value won’t go to the companies with the most impressive demos. It’ll go to those with the discipline to scale, the governance to trust, and the partnership to lead. 

If CIOs and COOs can co-own the AI operating model, AI stops being a headline—and starts becoming a habit. And as AI continues to evolve, this partnership will become the foundation for a new kind of enterprise collaboration—one where CFOs, CHROs, CMOs, and beyond work together through intelligent systems that move with speed, transparency, and trust. 

The “honeymoon” phase of AI is over, and the organizations that lead with execution—not experimentation—will define the next era of enterprise transformation. The only question left is, who’s ready to lead? 

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.