The App Store is booming again, and AI may be why

TechCrunch AI reported: The working hypothesis here is that AI-powered tools, like Claude Code or Replit, could be behind the surge of new launches. It also seems possible that we're hitting some sort of tipping point in terms of AI usability, where it's easy enough for people to leverage these tools to build their own desired mobile apps more quickly — or even build their first apps ever. The explosion of new apps for Apple to review could also be behind some of the tech giant's recent missteps. This week, Apple pulled the rewards app Freecash from the App Store for rules violations, after letting the app climb the store's Top Charts and sit in the top five for months. Apple was also caught off guard by a malicious cryptocurrency app, a clone of Ledger Live, that drained $9.5 million in crypto from victims' accounts. While high-profile problems like this can generate bad PR for the App Store, the company still does a lot of heavy lifting in terms of blocking and rejecting dangerous or spammy apps. Apple's most recent analysis from 2024 said the company had removed or rejected more than 17,000 apps for bait-and-switch violations that year; rejected more than 320,000 app submissions that were found to be spam, copying other apps, or misleading; and took action to prevent more than 37,000 potentially fraudulent apps from reaching users on the App Store. Still, Apple.

Read narrowly, this is one more item in the daily flow of infrastructure news. Read against the buildout cycle, it points to a more practical question for cloud infrastructure: can the operating system around compute keep up with demand? The constraint is not only the price of electricity. It is the timing of grid access, the flexibility of large loads, and the ability of data center operators to behave less like passive consumers and more like active participants in the power system.

That makes the second-order detail more important than the announcement language. Power access and interconnection timing are likely to matter more than the announced demand signal itself.

For infrastructure teams, that makes power procurement and site selection part of the product roadmap. A campus can have customers, capital, and equipment lined up and still lose time if the grid connection, market rules, or operating model cannot absorb the load profile.

The financial question is whether this development improves pricing power, locks in scarce capacity, or exposes execution risk that the market may still be discounting, the operating question is procurement timing, facility readiness, network design, and the likelihood that adjacent constraints will slow realized deployment, and the customer question is whether this changes build sequencing, partner dependence, or the economics of scaling regions and clusters over the next few quarters.

The market tends to price the demand story first and the delivery work later. That can hide the hardest parts of the buildout: grid queues, procurement windows, permitting, vendor capacity, and the coordination needed to turn a plan into a running site.

For a board focused on AI infrastructure, the item matters because it clarifies where leverage may sit. Sometimes that leverage belongs to chip suppliers or cloud platforms. In other cases it moves to utilities, landlords, financing partners, equipment vendors, or regulators that control the pace of deployment.

The next signal to watch is the next disclosures on customer commitments, infrastructure readiness, and any evidence that power, cooling, silicon supply, or permitting becomes the real gating factor. The next test is whether this remains a narrow market experiment or becomes a normal tool for balancing AI demand with grid reliability.