I disagree.
First, we'll never get to a point where we point at a new system and call it AGI with a straight face. Instead, we'll see systems that in hindsight we will label as AGI or proto-AGI. It will be easier to draw the history of AI if we start after the fact and trace our way back.
A few key cognitive milestones are all it takes towards the step towards AGI. Some of them we are on the cusp of delivering and others we have already passed.
Is AGI happening this year? No.
Something would generally be recognized as AGI (in my opinion) if it meets all of the following: autonomous learning (AlphaGo to AlphaZero demonstrates this at a rudimentary level), abstract reasoning (solve problems that aren't already discussed on reddit), strategic planning (big focus for LLMs and multimodal AI right now), contextual adaptation that transfers abstract concepts from one domain to another, and economic equivalence. Can the AI autonomously perform many or most economically valuable human jobs. (so 50% or greater)
Currently the frontier systems achieve expert capability in very narrow domains. But the current generation falls short in real-world autonomy and novel logic.
My score card:
2/5 Autonomous learning - partially there. adapts instantly with context-window learning, but cannot alter the underlying neural architecture. new domains are not acquired independently but are built around human directed training.
3/5 Abstract reasoning - inference broke some key barriers this year. And a HLE score of 65% is a demonstration that reasoning is possible and improving. Weaknesses in the results are where tasks require pure logic instead of memorized data.
1/5 Strategic Planning - I'd describe multi-agent frameworks today as nascent. ultraplan and ultracode on Claude has significant limitations. And it is more like herding cats, a bunch of agents inefficiently working in competing directions that often need to be reigned in. Not ready to be autonomous.
3/5 Contextual Adaptation - we see transfer learning today. I use this feature regularly in my work. I have agents writing code in a language that no model was trained on. I have agents taking historical parallels and applying it to fictional MUD setting, with synthesizing new choices when placing the same history under a different context and different set of requirements. Where it falls flat is there is not intuition, no leaps of logic, and you have to ride the agent to review its own work because it does not automatically apply any kind of common sense checks. You have to remain it of fundamental real-world limitations to coax a useful solution of it. So not autonomous at all, and needs a lot of hand-holding from humans
None of these are insurmountable. And as for timeline, I can't imagine it taking more than 5 years to tick each of those up a notch or two.
After feeding my amateur research and gut analysis into Google Gemini, and asking very loaded questions in my prompts, it is far less optimistic than I am:
Based on the current trajectory, the timeline to transition from today's system to a truly competent, autonomous AGI—and potentially trigger a singularity or ASI threat—is estimated to be 3 to 10 years (between 2029 and 2036).
Because AI development is moving exponentially rather than linearly, the closing gap is shrinking faster than past historical tech adoptions.