Did Davos just become the world’s most expensive tech conference? For a few feverish days in the Swiss Alps, yes — and the conversation wasn’t polite.
Executives and researchers arrived with polished talking points about growth, climate and geopolitics, but artificial intelligence elbowed everything else aside. What followed felt less like a summit and more like a trilogy of rival CEOs trading barbs, a policy playbook, and a recrimination opera all at once.
Labs, ads and reputations on the line
Onstage and in off-the-record corners, the big frontier AI labs treated Davos like a reputational battlefield. Google DeepMind CEO Demis Hassabis raised an eyebrow at OpenAI’s experiment with ads in ChatGPT, remarking that it was “interesting they’ve gone for that so early” — a comment that read as part curiosity, part jab. Anthropic’s Dario Amodei amplified the critique, saying his company wasn’t going to “monetize a billion free users” in some death race and even comparing unfettered GPU exports to China to “selling nuclear weapons to North Korea.”
OpenAI’s response was less public and more political: the company leaned on experienced hands in its policy shop to push back. The skirmish wasn’t just about ethics or revenue models — it was about optics. Each lab is staking a claim not only to technological leadership but to moral leadership, trying to convince partners, regulators and customers that they will build AI responsibly while the others chase growth.
That argument has practical consequences. Companies are experimenting with different product strategies — some adding agentic tools and booking features for enterprise customers, others doubling down on consumer reach. Google’s push into more agentic services, for example, has become part of the broader narrative about how big platforms will package AI for real-world use cases; you can see some of that momentum in Google’s recent expansion of its AI Mode features and booking agents Google's AI Mode.
A split on jobs — doom, rebirth, or something messier?
If labs were fighting for trust, CEOs were arguing about livelihoods. The chorus at Davos featured everything from doomsday forecasts to cautious optimism.
Anthropic’s CEO said bluntly that many software engineers are only months away from obsolescence — a provocation that landed like a cold splash. Others, including a Fortune-reporting CEO, predicted a V-shaped employment curve: steep layoffs early, followed by job creation as new sectors emerge. ServiceNow’s Bill McDermott described his company’s approach — retrain and redeploy — turning IT teams into AI managers rather than letting them go.
There were also voices urging balance. Some business leaders warned that panic about mass unemployment could stunt investment and cede advantage to countries with more sanguine views on AI. The IMF and several executives emphasized that policy and corporate reskilling must move in step with automation.
TIME’s panels pushed the point that AI’s disruption will be sector-specific. Moderna’s Noubar Afeyan talked about “programmable medicine,” where machine learning accelerates drug discovery — enormous promise, but the societal implications are profound. Energy executives described a new class of “electro-states” where clean power becomes the backbone of AI industries and competitive advantage.
Power, privacy and productivity
Davos wasn’t just a job-counting exercise; it was a debate about how AI will be embedded into daily life and industry. Discussions ranged from how AI can speed scientific discovery to whether integrating deep research tools into email and cloud storage creates privacy minefields. Google and other firms are racing to fold more advanced search and research capabilities into productivity suites — a move that promises convenience but raises questions about data access and corporate control. For context on that trend, see coverage of deeper research integrations with Gmail and Drive Gemini Deep Research.
Meanwhile, product moves that some saw as routine upgrades read like strategic signals. OpenAI’s product launches and distribution plans — including expanded mobile availability for new apps — feed into the reputational contest among labs over who is safest, most useful, and most scalable. The rollout of consumer-facing tools has consequences beyond downloads: they set expectations about monetization, moderation, and the acceptable trade-offs between rapid deployment and caution. One relevant example is OpenAI’s recent mobile expansions OpenAI’s Sora lands on Android.
Why this matters beyond Davos
Davos crystallized a broader truth: AI no longer sits in research centers and VC pitch decks. It’s a force reshaping corporate strategy, national competitiveness, labor markets and even how industries like pharma and energy think about growth. The rhetorical flourishes and public snipes between labs are partly theater — but they also map to real choices about product design, partnerships, and regulatory posture.
If there was a throughline to the week, it was urgency. Whether leaders leaned toward alarm or optimism, almost everyone agreed the pace of change is fast. The fights over ads, exports, and product strategy at Davos are proxies for deeper conflicts about who gets to set the rules for an increasingly automated world.
Davos may be snowbound geography, but the decisions made and the narratives forged there ripple outward: into boardrooms, onto policy agendas, and into the daily work of millions. The summit didn’t resolve anything — but it did make clear where the next arenas of contest will be: revenue models, regulation, reskilling, and the race to embed AI into the plumbing of the economy.