When an idea felt like science fiction and the famous Turing test seemed far away, life kept going the same. Suddenly the distance shrank, and then we were on the other side: machines that talk and solve complex problems are already part of everyday life, even if many people still see them as "just chatbots." Do you feel the gap closing too?
What OpenAI says about the progress of AI
OpenAI explains that AI already surpasses human minds in some very difficult intellectual tasks. Does that mean an immediate revolution is coming? Not necessarily. Systems are powerful but uneven: they shine in some tasks and fail in others. Still, for researchers the path to even greater capabilities seems closer than it did recently.
One key point: the cost per unit of intelligence has dropped very quickly — a reasonable estimate for recent years is 40x per year. That changes the equation: tasks that used to take a lot of time or resources are now more accessible.
Timeline and expectations
OpenAI sets concrete expectations: by 2026 there could be systems able to make very small discoveries. By 2028 and beyond, they expect systems capable of more significant discoveries, while acknowledging uncertainty.
They also draw attention to capability escalation: today AI goes from doing tasks that take seconds to tasks that take an hour; soon it will do tasks that today take days or weeks. And tasks that would require centuries of human work? There the epistemic uncertainty is real: we don’t know how to reason well about that jump.
A practical example
In software engineering, AI already automates microtasks that used to take a person seconds; now it helps with activities that took hours. That speeds up projects, but it also changes who does what and when.
Main risks and recommendations
OpenAI stresses that commitment to safety is not rhetoric: they believe studying safety and alignment empirically is crucial for global decisions, including whether to slow development as we approach self-improving systems.
They propose several concrete measures:
- Cutting-edge labs should agree on shared safety principles and share research results about risks.
- Standards and evaluations for AI controls could work like building codes or fire standards: nobody questions that those standards save lives.
- Create an AI resilience ecosystem, analogous to cybersecurity, with protocols, monitoring, response teams, and standards.
"No one should deploy superintelligent systems without being able to align and control them robustly."
Two visions of the future and a common response
OpenAI describes two schools of thought: one that sees AI as another technology — like the printing press or the internet — and another that fears an unprecedented diffusion and speed that would require more radical solutions.
In both scenarios there are consistencies: the need for smart public policies, international cooperation, and for public institutions to have responsibility and a voice in governance.
Social and economic impact, and concrete benefits
The organization expects tangible benefits: better understanding of health, accelerated materials research, drug development, more accurate climate models, and globally personalized education. That’s not just efficiency; it can translate into longer lives and better opportunities.
At the same time, they acknowledge that the economic transition can be hard for many people. That’s why they insist on practical measures: continuous impact measurement, promotion of equitable access, and policies that avoid fragmented regulatory patchwork.
What this means for you today
- If you work with technology, prepare to integrate tools that boost your productivity: AI is here to stay.
- If you’re a public official or a business leader, the invitation is to participate in standards and build trusted infrastructure for AI. This isn’t just a technical responsibility: it’s political, economic, and social.
- If you’re a citizen, a reasonable expectation is that AI becomes a basic utility — on par with electricity or water — and society should ensure that access is safe and fair.
Practical action and recommended steps
- Encourage agreements among labs on safety principles and the sharing of relevant findings.
- Invest in a resilience ecosystem: standards, detection, responses, and training.
- Measure AI’s real impact on employment, health, and education to inform public policy adjustments.
- Promote broad access to advanced tools within clear social limits.
Final reflection
Technology history teaches us that big changes often arrive in waves: something feels ordinary while social infrastructure adapts. The same is true for AI: we can build it to improve lives, but that requires foresight, collaboration, and a serious focus on safety. Can you imagine an AI that speeds up a key medical discovery? It’s possible. Are you worried about who will control that AI? That’s a legitimate concern too. What matters is how we decide to act today.
