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What if a few AI companies end up with all the money and power?
Last year, a lot of people (including me) were wondering if the AI industry was in a bubble. These days it’s looking a lot less likely. The technology has found its killer app — agentic coding, which has upended the software industry as we know it. For power users, AI is no longer just a chatbot — you can tell it to go make you an app, or run some data analysis, and it’ll just do it for you and come back with the results. This is making a LOT of money. As I predicted, Anthropic has been quicker to capitalize on the agentic coding boom than OpenAI. Anthropic focused on selling to businesses, while OpenAI focused on building its brand and selling to consumers; the revenue from agentic coding is almost all in the former category. So as Ruben Dominguez reports, Anthropic has probably overtaken OpenAI in revenue, or will do so soon:In case you don’t realize how much money this is, or how fast this growth rate is, here’s some perspective:Some of that will be eaten up by computing costs, of course. But as the WSJ recently reported, Anthropic’s computing costs are much lower than OpenAI’s. As a result, it’s expected to start turning a profit faster than OpenAI — and even OpenAI’s projections depend heavily on a comeback push that eats into Anthropic’s enterprise market share. The rise of coding agents isn’t just changing the corporate horse race; it’s changing the whole picture of how we think about competition and profit in the world’s most important new industry. In a post last December, I wondered if AI would end up being a vitally important but low-margin business, similar to solar power or airlines:Jason Furman wrote something similar, declaring that “instead of consolidating, as so many other industries have done, the leading edge of A.I. has become fiercely competitive.”That’s still possible, of course. Fast followers, including Google and various Chinese model-makers, are still racing to catch up; if progress slows down, they may catch the market leaders and drive down margins. It’s still not clear how much of a “moat” AI has, even with agents. But right now, the business of making and renting out AI models seems dominated by two giants. Meta and xAI, who recently were considered at or near the frontier, seem unable to keep up. And there’s now a pretty clear path for those two giants to become even more dominant: cybersecurity. Anthropic recently delayed the wide release of its new frontier model, Mythos, because it was too good at hacking. The model suppo...
Enterprise AI Rollout Failures: Causes and Case Studies
Executive Summary The rapid embrace of artificial intelligence (AI) in enterprise settings has delivered some success stories, but high-profile failures and widespread underperformance have revealed profound systemic issues. Studies and surveys report that the vast majority of corporate AI initiatives either stall or fail to produce significant business value ([1]) ([2]). In practice, “what went wrong” is rarely a simple technical glitch in the AI models themselves, but rather a convergence of planning, data, organizational, and integration failures. Key pitfalls include overhyped and unrealistic goals, poorly planned architecture and integration, low-quality or fragmented data, insufficient governance and oversight, and ignoring human change management and expertise ([3]) ([4]). For example, a McDonald’s pilot of an AI voice-ordering system (in partnership with IBM) was shut down after repeated misunderstandings (ordering extra items like “ketchup and butter”) when the system misheard accents or repeated commands ([5]). Similarly, Australia’s Commonwealth Bank was forced to rehire 45 customer service staff after its new AI chatbot “Bumblebee” failed to reduce call volumes as promised ([6]) ([7]). In government, U.S. Immigration and Customs Enforcement (ICE) discovered its AI resume-screening tool had inadvertently fast-tracked unqualified applicants (merely using keywords like “officer”) into law-enforcement training, requiring mass retraining ([8]) ([9]). These tangible examples echo broader findings: MIT’s NANDA study and independent surveys warn that roughly 95% of AI pilot programs do not deliver measurable ROI ([1]) ([2]). Common themes emerge across industries and geographies: enterprises underestimated integration complexity, misjudged data readiness, and failed to align projects with clear business outcomes and governance. This report examines in depth what went wrong in numerous enterprise AI rollouts (as of April 2026) across sectors. It synthesizes historical context, empirical data, expert analyses, and detailed case studies. We first contextualize the limits and lessons of early AI optimism. Next, we analyze major failure factors: hype vs reality; data and technical flaws; organizational and cultural barriers; security, ethics and compliance gaps; and the economics of AI. We then present a series of case-study vignettes illustrating how these issues manifested in real projects (with citations). Tables summarize notable examples and recurring ...
Lessons from a Trust Crisis: 'AI Made Me Do It' Is No Excuse
A hiring platform's AI misstep sparked outrage, regulatory scrutiny, and a trust crisis. Sandra Sucher suggests six questions every leader should ask to use ...
Anthropic Study: How AI Is Impacting Tech Jobs and Workers | Built In
... AI industry. Matthew ... According to Anthropic's most recent labor market study, artificial intelligence is still being used sparingly in the workplace.
