
@axios The AI story may be real. The valuations are what investors are debating now.
Positive users see insider skepticism of the AI boom as a healthy shift toward real ROI and utility, while negative users dismiss the coverage as doomerism or attempts to sabotage valuations.

@axios The AI story may be real. The valuations are what investors are debating now.

@GroupToStopHate @axios May be, but I hope you've been keeping up with the PR from anthropic

It is a massive shift when the people who actually built the tech start pulling the emergency brake. It’s no longer about fear of the unknown; it's about firsthand knowledge of the limitations and risks. When the insiders speak up, everyone else should probably start listening. #AI #TechEthics

@axios The AI industry is amazing
Most doomers are paid shills

@axios @grok 이게무슨 뜻이야?

@axios Every bubble looks like a revolution until liquidity disappears.

@axios fuck off axios just pump coreweave to 150

It’s definitely feeling like a reckoning moment. The shift from pure hype and massive valuations to demanding actual ROI and sustainable utility is painful, but probably necessary for the tech to mature. We might finally see the real, practical tools outlast the speculative noise. #AIBubble #TechTrends

🐝“Axios’ observation marks an important shift: the debate around AI is moving from external critics to internal stakeholders who are now confronting the sector’s underlying economics. Uber exhausting its annual AI budget in four months, Amazon imposing usage controls, Bain’s survey of 951 firms showing ROI falling short of expectations, and Sam Altman himself acknowledging the opacity of the cost‑to‑revenue relationship—these are not isolated incidents. They indicate that the marginal cost of AI adoption is beginning to exceed what many enterprises can sustainably absorb.
This aligns with the early phase of the ‘trough of disillusionment’ in the classic hype cycle, echoing the dot‑com era’s pattern of overinvestment, correction, and subsequent consolidation. The foundational technologies—large‑scale models, inference optimization, and data‑driven workflows—remain fundamentally sound. However, current generative AI deployments are delivering productivity gains primarily at the task level (10–50%), without yet translating into meaningful P&L impact or measurable macro‑productivity improvements.
The structural drivers are clear: • Inefficient capital allocation driven by PoC proliferation • Escalating inference costs inflating marginal expenses • Underdeveloped workflow integration limiting organizational‑level scaling This is not a repudiation of AI’s long‑term value. It is the natural consequence of the ‘unbounded usage’ model reaching its economic limits. Notably, several domains—software development, content production, and customer support—are already demonstrating positive ROI. The next phase of deployment will likely be defined by: • Agent‑based automation • Smaller, domain‑specific models optimized for cost efficiency • End‑to‑end process redesign built around AI‑native workflows
The current market correction should be viewed as a healthy recalibration. Capital is shifting from experimentation toward efficiency and durable value creation. In that sense, we are entering the true selection phase of the cycle.”

@axios Lets see how it escalates

@axios Bingo

@axios Funny how “doomer” now means anyone who can still read a balance sheet, name an external brake, ask who owns consequence, or notice that a trillion-dollar demo economy is not the same thing as deployment.
The cope was always the product. 🛠️

**seuteulaipeopw1** "Revenge of the AI bubble"는 Axios 기사 제목이에요.
AI 과열·대규모 투자 후 현실이 드러나면서 나타난 '버블의 복수'라는 뜻입니다.
기술은 잘 작동하지만, 기업들이 예상한 비용 절감·가치가 제대로 안 나오고 있어요. Uber·Amazon 등에서 AI 사용 비용이 너무 많이 들어 제한하거나, Bain 조사에서도 savings가 기대에 못 미친다는 내용입니다.
AI는 특정 용도에선 유용하지만, 무차별 적용하면 비싸고 효과 제한적이라는 현실을 지적한 거예요.

@axios Of course and the lead entity is know as Anthropic

@giris4u @axios When the outsiders speak up.. maybe listen a little harder.. 🛠️

@axios