Anthropic CEO Dario Amodei has been repeatedly talking about how AI can cause massive job losses, particularly among entry-level workers, but not everyone in AI agrees with his view.
Jason Droege, the interim CEO of Scale AI, offers a contrarian perspective on the timeline of AI’s impact on employment. Scale AI, one of the most prominent data labeling companies that helps train large language models and computer vision systems for major tech companies, recently became a focal point in the industry after Meta invested $14.3 billion for a 49% stake in the company. Droege’s measured take on AI job displacement comes at a time when his own company has had to navigate the complexities of AI adoption and recently laid off 14% of its workforce due to rapid scaling challenges.
“I think it’s very overhyped that it’s going to eliminate jobs in the next one to two years,” Droege said on the TBPN podcast. “I think this line of thinking would require a change in the curve of capabilities and the change management side of these organizations where I think it’s way overblown. The promise is just so high, and the reality of what’s going on in the ground is this: there’s value, but the value needs to be extracted and that requires a ton of work.”
Droege’s skepticism extends to the current state of corporate AI investments, which he believes are driven more by fear of missing out than concrete returns. “What’s gonna happen with these customers in the next year, if I had to make a prediction, is you’re gonna go from having a certain amount of money, which is usually a lot in these companies, allocated for AI initiatives to what is the ROI?” he explained. “The Grim Reaper is going to come for every AI company that is not delivering value to these customers.”
The Scale AI CEO attributes this disconnect to unrealistic expectations that have been building in the market. “What’s happened is, they’ve all been sucked into this idea that if we don’t invest in this, we’re gonna miss out because AGI is coming. And the reality is, is that if that doesn’t pay off, that expectation is very high. And if you’re not finding a way to give ground truth to these customers, they’re gonna be very disappointed.”
Droege’s perspective carries particular weight given his position at the intersection of AI development and enterprise adoption. Scale AI’s core business model revolves around data labeling and annotation, supplying AI labs with labeled, structured data to train AI models. The company has seen firsthand both the promise and the practical challenges of AI implementation across various industries, from autonomous vehicles to government services.
This measured outlook aligns with broader industry observations about the gap between AI hype and practical deployment. Scale AI itself recently experienced the challenges of rapid AI expansion, with Droege citing that the company had ramped up its generative AI capacity “too quickly” and created excessive bureaucratic layers. The company’s recent workforce reduction, despite its significant funding and market position, underscores the practical difficulties even AI-native companies face in scaling efficiently. But several voices, including Vinod Khosla, Dario Amodei and others, have spoken about the potential job losses that AI adoption would cause.
Droege’s comments come as the industry grapples with mounting pressure to demonstrate tangible returns on AI investments. While companies across sectors have allocated substantial budgets to AI initiatives, the transition from experimental projects to productivity-enhancing tools that could genuinely displace workers appears to be more complex and time-consuming than initially anticipated. His prediction of a coming reckoning for AI companies that fail to deliver measurable value suggests that the next phase of AI adoption will be characterized more by pragmatic evaluation than speculative investment.