OpenAI’s User Growth Miss, Musk vs. Altman, Prediction Market Ban

Summary of OpenAI’s User Growth Miss, Musk vs. Altman, Prediction Market Ban

by Alex Kantrowitz

57m•May 1, 2026
Listen to Episode

Overview of OpenAI’s User Growth Miss, Musk vs. Altman, Prediction Market Ban

In this Friday edition of Big Technology Podcast, Alex Kantrowitz and Ranjan Roy break down a pivotal week for AI: reports that OpenAI is missing user and revenue targets, what that means for the consumer AI story, how OpenAI is repositioning toward enterprise and agentic use cases, the Elon Musk vs. Sam Altman court battle, and why the latest big tech earnings still point to massive AI infrastructure spending. The episode also closes with a sharp critique of prediction markets and how betting culture is bleeding into news coverage and public institutions.

OpenAI’s Slower Consumer Growth

Missed targets and what they signal

  • The Wall Street Journal reported that OpenAI missed internal targets for new users and revenue as it races toward an IPO.
  • The biggest concern raised in the episode is not just revenue, but user growth:
    • OpenAI reportedly wanted 1 billion ChatGPT users by the end of 2025
    • The latest cited figure was 900 million active users in February 2026
  • The hosts frame this as a potential sign that the explosive consumer adoption phase for ChatGPT may be slowing.

Is consumer AI plateauing?

  • Alex argues that standalone consumer generative AI apps have not produced the breakout products many expected.
  • He points to:
    • Slowing ChatGPT growth
    • Negative consumer sentiment toward AI in some corners
    • A lack of major consumer-native AI apps like AI friends, stylists, dieticians, or history apps

Ranjan’s rebuttal: AI is already everywhere

  • Ranjan pushes back, arguing that consumers are engaging with AI more than ever, just not always through a separate chatbot app.
  • Examples discussed:
    • Meta’s recommendation engines and ad systems
    • Amazon’s Rufus shopping assistant and embedded AI ads
    • Google Shopping’s virtual try-on tools
    • AI-generated content in Spotify and DoorDash
  • His core point: consumer AI is often embedded inside existing products, not necessarily visible as a standalone “ChatGPT-like” app.

OpenAI’s Strategic Pivot: Consumer vs. Enterprise

The move toward Codex and agentic tools

  • The episode repeatedly returns to OpenAI’s shift toward:
    • Enterprise use cases
    • Developer tools
    • Agentic workflows, including Codex
  • The hosts debate whether this is:
    • A move from a position of strength, or
    • A response to slowing consumer momentum

What OpenAI should focus on

  • Alex’s view: OpenAI risks losing the consumer advantage it still has if it pivots too hard into enterprise.
  • Ranjan’s view: OpenAI’s developer-first culture may naturally bias it toward tools like Codex, but that doesn’t mean it should abandon consumer.
  • They also note that OpenAI still has a unique asset: massive consumer distribution, which could be powerful if used well.

Anthropic and the enterprise race

  • Anthropic’s rapid rise is referenced as a sign of how valuable enterprise AI has become.
  • The broader implication: the most compelling growth may be happening in workplace and developer AI, not consumer chatbots.

Musk vs. Altman in Court

The lawsuit

  • Elon Musk is suing OpenAI over its conversion from a nonprofit model to a for-profit structure.
  • The hosts discuss whether Musk has a credible argument that OpenAI betrayed its founding mission.

Likely outcome

  • Both agree Musk has at least some logical footing:
    • He helped fund the company when it was a nonprofit
    • He now has no ownership stake
  • But they’re skeptical the lawsuit will fully derail OpenAI.
  • The most likely consequence, in their view, would be financial or structural rather than existential.

A notable distillation admission

  • One especially interesting courtroom moment: Musk acknowledged that distillation—using one model to train another—is standard across the industry.
  • The hosts see this as important because:
    • It highlights how common model copying and refinement practices are
    • It raises questions about model commoditization
    • It supports the idea that price competition may intensify as models converge in capability

Big Tech Earnings and the AI Infrastructure Boom

Cloud growth is still surging

The week’s earnings reports show that AI infrastructure demand remains enormous:

  • Google Cloud: up 63% to $20 billion
  • AWS: up 28%
  • Microsoft Azure: up 40%

What that means

  • The hosts interpret these numbers as evidence that the enterprise AI buildout is still in full swing.
  • At the same time, they acknowledge a major uncertainty:
    • Are companies overbuilding?
    • Will today’s demand justify all the capital being poured into compute?

The skepticism about economics

  • They discuss the possibility that AI economics may eventually compress:
    • Models could be commoditized
    • Open-source and lower-cost alternatives could reduce pricing power
    • Infrastructure investments could look excessive in hindsight
  • The key unresolved question: What is the true margin structure of an AI business?
    • No one really knows yet

Apple as the counterexample

  • Apple is held up as an interesting contrast:
    • It did not go all-in on AI infrastructure or foundation models
    • Yet it posted strong iPhone sales
  • The discussion suggests Apple may have benefited from not overcommitting to the AI arms race—at least for now.

Prediction Markets and Gambling Concerns

Senators ban themselves from trading

  • The U.S. Senate unanimously passed a ban preventing senators from trading on prediction markets.
  • The hosts treat this as a rare moment of bipartisan common sense.

Why prediction markets are controversial

  • They argue that prediction markets can be:
    • Manipulated by insiders
    • Harmful to society
    • Especially problematic when tied to political or personal events

A harsh critique of sports media

  • Alex is particularly critical of a CBS Sports article that discussed a player’s gambling addiction while repeatedly embedding betting odds and links.
  • His point: media outlets are normalizing and monetizing behavior that can be destructive.

Key Takeaways

  • OpenAI’s consumer growth slowdown may be real, but it doesn’t mean AI demand is disappearing—it may be shifting into embedded products and enterprise workflows.
  • The consumer AI breakout app still hasn’t really arrived, at least not in the standalone chatbot format many expected.
  • OpenAI’s move toward enterprise and Codex could be strategically smart, but it also may reflect pressure from weaker consumer momentum.
  • Musk’s court challenge is unlikely to destroy OpenAI, but it could create financial or structural consequences.
  • AI infrastructure spending remains massive, as cloud growth across Google, Microsoft, and Amazon shows.
  • Prediction markets are drawing more scrutiny, especially as betting logic spreads into politics and media.

Notable Themes

  • Consumer AI vs. embedded AI
  • Enterprise AI monetization
  • Model distillation and commoditization
  • AI infrastructure economics
  • Ethics and regulation in prediction markets and gambling