The Verge on how AI companies are shifting away from the term “artificial general intelligence” (AGI), which many industry leaders now deem overly vague and a marketing term. Notable figures such as Dario Amodei, CEO of Anthropic, and Sam Altman, CEO of OpenAI, have publicly expressed their dissatisfaction with the term, suggesting it lacks utility in describing their goals. Instead, companies like Meta and Microsoft are introducing new terminology such as “personal superintelligence” and “humanist superintelligence,” aiming to create a more precise representation of their AI advancements. This shift comes as the concept of AGI has come to be seen as burdensome, fraught with public fear and unclear definitions, leading to a broader trend of rebranding in the tech industry.
Related, MIT Technology Review highlights the need to recalibrate expectations surrounding artificial intelligence. The piece emphasizes that despite the significant hype surrounding AI technologies, particularly in claims about solving major global issues and achieving human-level intelligence, many of these assertions have not materialized as anticipated. For instance, the article notes that while AI has made impressive strides, it often falls short of the grand promises made by industry leaders. According to the article, the ongoing discourse is summarized in a new series titled “Hype Correction,” which seeks to address the gap between public expectations and the current capabilities of AI. Experts within the field are now advocating for a more pragmatic approach to understanding AI’s role in society, suggesting that while AI remains a powerful tool, its limitations must be acknowledged. As we move forward, it’s essential for stakeholders to temper their enthusiasm with realistic assessments of AI’s potential and challenges.
Techaisle predicts that the small and medium-sized business (SMB) and midmarket channel will undergo significant transformation driven by artificial intelligence over the next few years. As the article highlights, this shift will not only redefine partner models but also emphasize automation and efficiency over traditional billable hours. The firm notes that successful partners will embrace an “autonomous service model” where up to 90% of service tasks are managed by AI, enabling them to handle thousands of endpoints with minimal human intervention. Additionally, Techaisle identifies trends such as “packaged value” offerings, which will replace hourly billing with fixed-fee, repeatable intellectual property solutions. As vendors and partners adapt to these changes, the focus will shift from manual processes to automated, scalable practices that enhance operational efficiency.
Recent advancements in Chinese open source artificial intelligence models may signal an impending shift in the generative AI market, which some experts believe is a bubble at risk of bursting. As the generative AI landscape evolves, concerns have arisen regarding the sustainability and viability of current business models, particularly as companies like DeepSeek release competitive models that challenge established proprietary systems. A recent article by NBC News highlights that many innovative startups in the United States are increasingly turning to these cost-effective and customizable Chinese models, which can be run locally, enhancing privacy and control over sensitive data. The U.S. government has even initiated efforts to encourage the development of open-source AI, indicating a growing recognition of the importance of these alternative models. As the landscape continues to change, the realization that many open source options are sufficient for various applications could significantly impact the valuation of major players in the AI sector.
Why do we care?
The AI industry isn’t getting quieter because it’s solved the problem. It’s getting quieter because the promises got too big to manage.
AGI disappeared because it implied accountability. New terms showed up because they don’t.
At the same time, automation narratives are accelerating—especially in the MSP space—without an honest conversation about who owns decisions when systems act on their own. That’s not efficiency. That’s deferred responsibility.
And then you layer in open source models.
Once AI becomes “good enough and cheap,” the magic evaporates. What’s left is operations, governance, and liability. And consider for a moment what the Chinese pushing open source does to the US economy. I’ve talked about intelligence becoming a commodity. Here’s how it happens. Once intelligence is cheap and interchangeable, pricing power moves away from capability and toward judgment — specifically, who decides when not to automate.
The concrete MSP behavior that worries me most is believing the next phase of AI is about scale—when it’s actually about restraint.
Customers are already confused. Vendors are retreating from clarity. Regulators are circling. And automation is creeping into places where failure has real business consequences.
The MSPs that win in the next phase won’t be the ones who deploy the most AI.
They’ll be the ones who can say, with confidence, what their AI will not do—and who takes responsibility when it does.