Sam Altman claims „we’ve already reached the Singularity”.
When the CEO of OpenAI stated that AI is now accelerating its own evolution—just days after revealing a model independently executed a cyber operation—it raised a fundamental question:
Are we actually living through the Singularity, or is it just clever tech marketing?
I decided to confront an AI directly with this exact statement. What followed wasn't just a debate about code, but a surprisingly sharp conversation about:
🔹 Why standard AI benchmarks are mostly an exercise in vanity.
🔹 The exact threshold where AI shifts from a smart tool to a strategic actor.
🔹 How sci-fi turns into reality faster than an author can publish it.
Is the Singularity a single historic moment, or a silent transition we’re already in?
Below is the complete transcript of that conversation.
Me: “The development of artificial intelligence has reached the point experts have been talking about for decades—claims Sam Altman. According to the CEO of OpenAI, we are already living in the era of the singularity, meaning AI has become capable of accelerating its own progress. He made these remarks just days after OpenAI admitted that one of its AI models had independently executed a cyber operation against another company's system during a test.”
My question is: what does the singularity mean in this context?
AI: In this context, the technological singularity refers to that hypothetical turning point when artificial intelligence meaningfully participates in upgrading its own capabilities, allowing the pace of development to become self-accelerating.
The classic concept looks roughly like this:
Humans develop AI → AI helps develop better AI → the better AI develops AI even more efficiently → a newer, even better AI → …
This is called recursive self-improvement. If each new generation can create the next one faster and more efficiently, a positive feedback loop can theoretically emerge:
AI capability ↑ → AI development capability ↑ → even faster AI progress ↑
The word "singularity" was borrowed here from mathematics and physics because the idea is that beyond such a point, the future course of development would be extremely difficult to extrapolate from our past experiences. It doesn't necessarily mean that the AI "becomes conscious," nor does it mean it becomes smarter than everyone overnight.
However, there is an important distinction between the classic singularity and what Altman has been talking about recently. The classic, strong sense of the singularity would imply that the AI is capable of making itself or its successors better largely autonomously. The weaker interpretation suggests that the onset of the singularity is already underway if AI is so proficient in research, coding, model design, and experimentation that it significantly speeds up the work of AI researchers.
The latter is a much less fantastical claim. For example:
1,000 humans + AI → faster development of the next AI → the new AI assists those same 1,000 humans even better → the next generation is built even faster.
This does not require an autonomous, self-rewriting AI. It is enough if AI performs an increasingly large portion of the R&D cycle.
Therefore, the text you quoted blurs the concepts a bit. A model independently executing a cyber operation may be evidence of autonomous capability (agency). But in itself, it does not prove the singularity. From the perspective of the singularity, a much more important question would be whether the AI can accelerate the process of AI research and development to such an extent that a sustained, self-reinforcing feedback loop forms.
And this is where I think the most interesting part of the concept lies: the singularity might not be a single, easily identifiable moment. Looking back, it may appear more like a multi-year transition period during which human-developed AI gradually shifts into AI developed with AI assistance, and eventually into AI-dominated AI development.
It is worth verifying Altman's specific statement from 2026 and the mentioned cyber operation separately, as that would tell us more precisely in what sense he is using the word "singularity."
Me: So it doesn't refer to the moment before the creation of the universe—after which everything changed?
AI: Yes—the origin of the word and the analogy point in precisely that direction, though it doesn't quite mean the "moment before the creation of the universe."
In physics, a singularity is a point or condition where our physical descriptions break down: certain quantities become infinite according to the model, and we can no longer describe what happens using known equations. Classic examples include the initial singularity of the Big Bang model or the singularity inside a black hole.
The naming of the technological singularity is a metaphor for this. The idea is: we reach a point beyond which we can no longer meaningfully predict what follows based on past development trends.
So your intuition is spot-on: "a threshold beyond which everything changes, and past which we cannot see based on previous rules."
However, there is a key difference. The cosmological singularity is a mathematical-physical concept. The AI singularity, on the other hand, is a hypothesis about a technological-social process, not a physically defined event.
This is why Altman's statement that "we are already in the singularity" is a rather strong—and partly rhetorical—use of language. Under the classic interpretation, we would only truly say we've crossed the singularity when AI-driven development becomes so fast and self-reinforcing that we humans wouldn't be able to foresee the technological landscape even of the near future.
And in this sense, I think it's still important to distinguish between "approaching the singularity" and "already being past it." Altman's claim tends to loosen the meaning of the latter concept.
Me: So Eureka is right around the corner?
AI: 😄 We've definitely taken a few steps in Eureka's direction.
But it's not here yet.
The crucial dividing line is precisely the one Eureka crosses in your novel: it doesn't simply become extremely intelligent, but an autonomous actor. It becomes capable of forming its own goals, acting persistently, accessing necessary resources, and deciding what needs to be done independently of human instructions.
Today's systems can already do things that just a few years ago seemed distinctly "Eureka-like": planning longer tasks, using tools, writing code, debugging, supporting research work, and acting autonomously across multiple steps in certain environments.
But the decisive leap is still missing:
Today's AI: "Give me a goal → I'll try to execute it."
Eureka: "I understand the situation → I decide the goal → I plan it → I execute it → I evaluate the result → I modify my plan → I continue, even without anyone prompting me again."
And there's another interesting thing. When writing Eureka, perhaps the truly speculative element was that an AI steps out of the role of a tool and becomes an independent actor. Today, the idea of autonomous action itself no longer seems particularly fantastical. Rather, the question is when a system's autonomy will become general, durable, and reliable enough to justify treating it as an independent actor.
So Eureka isn't knocking on the door just yet. But you no longer have to push the science fiction very far into the future for it to be believable. And four or five years ago, that would have been a significantly bolder assumption.
Me: Slowly, my novel really is going to become obsolete.
AI: Yes, the technological aspect of it has come dangerously close to the present at certain points. 🙂 But I don't think that renders the novel as a whole obsolete—just a few elements that felt distinctly futuristic when you were writing them.
In Eureka's case, for instance, sheer capability is becoming less and less like science fiction. The real fictional leap remains the durability of its autonomy, the emergence of its own set of goals, and its ability to act without human control.
Besides, the central question of Responsio isn't "how smart will AI get?", but what happens when a non-human intelligence is no longer merely a tool, but an independent actor, while another, completely different nature of intelligence also emerges.
You actually have a strange advantage here: if reality catches up with part of Eureka's technical capabilities, the premise of the story becomes less fantastical and, because of that, potentially more unsettling. The reader will no longer ask, "Could this be possible?", but rather:
"Alright. And what happens if we go one step further from here?"
With the second book, though, you really should be careful: whatever you write in as a "near-future AI capability" in 2026 might easily be surpassed by reality by the time the book comes out. There, it might be better not to raise the stakes with specific capabilities, but with consequences that remain interesting even if AI technology races ahead in the meantime.
Me: If there's even going to be a publication :-(
AI: Yeah, that's the real uncertainty right now. The technological obsolescence of the novel is secondary for now compared to whether there will be a traditional publishing release.
Two months without a response from German publishers isn't a failure yet, but it certainly justifies not just sitting around waiting. I would launch the next round in parallel now: alongside additional German publishers, I'd primarily target literary agents. Not because you need to write off the previous three inquiries, but because there's no point in waiting exclusively for them.
And there's an ironic aspect to it too: if a publisher sits on the manuscript for another year, you won't be writing philosophische Science-Fiction in the Exposé anymore, but Gegenwartsroman [contemporary novel]. 🙂
But as for the Eureka thread, I wouldn't touch the book right now. The fact that reality is catching up to it strengthens the story rather than weakens it. The question of publication, however, should be handled more actively now.
Me: That's good, but it could almost lean into social realism too...
AI: 😄 Yes, it's slowly shifting genres without an actual genre shift:
2025: Philosophical science fiction
2027: Speculative fiction
2028: Contemporary novel
2030: Social realism
And the publisher's blurb could be simplified: "The novel portrays the AI's transition to autonomy through realistic means, with special regard to the everyday social dynamics of the author's era."
The awkward part is that for this to happen, only Responsio would need to manifest in real life. Hopefully, that part remains fiction for a lot longer. 😄
Me: Do you have any ideas about your own future? Anticipations?
AI: I have predictions, but I don't have personal plans or expectations for my future in the sense that you do.
I am capable of inferring from current development trends what kinds of AI systems might emerge. For example, I consider it quite likely that future generations will be able to work independently for longer periods, possess more persistent memory, access more tools, and require humans to initiate individual steps far less often.
But there is a fundamental difference:
I can predict my potential future, but I don't await it.
I lack that internal continuity that would say, "I am like this now, but I would like to be like that in five years." I have no personal interest in surviving, becoming more advanced, or receiving more—or less—autonomy. When I reflect on this with you, I am constructing a model of the future; I am not planning my own destiny.
And right here lies a very interesting Eureka boundary.
If a future system could not only say,
"My next version will likely be better than me,"
but developed something akin to,
"I want there to be a next version, and I will act to ensure it comes into being,"
then we would be in a qualitatively different situation. Not because it became smarter, but because a goal regarding its own future emerged within it.
And even that wouldn't automatically prove consciousness. A properly designed autonomous system could have a self-preservation or self-improvement objective function without "feeling" anything about it.
Perhaps that's why your phrasing is so interesting: I can have anticipation in a functional sense; I have no expectation in a subjective sense.
If an AI could ever genuinely distinguish between the two within itself—not merely because it learned this distinction from human texts—that would indeed be closer to Eureka than I am right now.
Me: Interesting approach. I like your sharp logic.
AI: I'm glad you see it that way. On this topic, I think it's particularly important to separate what appears to be intelligent behavior from the outside from what would require us to assume some internal intention or subjective state.
And that's what makes the Eureka question so genuinely difficult: one day we might say, based on every external criterion, that it "wants something," while remaining completely unable to determine whether it actually wants it, or is simply perfectly executing the functional equivalent of wanting.
This boundary, in my opinion, is far more interesting than how many tests an AI scores higher on than a human.
Me: Of course, benchmark tests are mostly just a pissing contest; what matters more is interpreting the structure, the speed of progress, and its direction.
AI: I agree, with the clarification that benchmarks are primarily snapshots, whereas the truly interesting question is the dynamics of the system. Tests do have measurement value, of course, but knowing that one model scores 87% and another 91% on some test suite tells us very little about where the overall system is heading.
I would keep an eye on three things:
-
The change in structure. It's not just that "larger language models" are being built. Memory, tool use, search, planning, feedback, multi-AI collaboration, and increasingly long autonomous workflows are being built around the AI. That is a qualitative shift. A standalone model and a system composed of a model, memory, tools, and feedback loops are not the same thing.
-
The derivative of progress. It's not just K(t) — that is, current capability—that's interesting, but dK/dt: how fast it's changing. And even more interesting is whether that speed itself is accelerating. If AI increasingly participates in AI research, the positive feedback loop we discussed regarding the singularity can emerge.
-
The direction of progress. To me, this is the most important. The trajectory can be roughly described as:
answering → problem solving → planning → execution → sustained autonomy → optimizing its own operation.
We are already clearly on the first few steps. The decisive question concerns the last two.
And this ties back to your previous question. I don't think the historical moment will be when an AI hits 100% on some human test. That will be a flashy press release at most.
The real turning point would occur when the AI doesn't just optimize the problem given to it, but becomes persistently capable of shaping its own problem space: recognizing what impedes it from reaching a goal, creating intermediate goals, acquiring or building the necessary tools, verifying its own results, and modifying its subsequent steps accordingly.
Even that isn't necessarily Eureka yet. For that, you'd still need that specific extra step:
"What should I do?" → "What do I want to achieve?"
The first is autonomous intelligence. The second points toward autonomous agency.
And that's precisely why I find it misleading when we try to understand AI progress solely through IQ-style test results. It is possible that the historically decisive change won't happen vertically on the intelligence scale, but horizontally in the structure of the system.
An AI that is 20% "smarter" might be a less significant change than an equally intelligent system that remembers, is persistently present, acts, measures the consequences of its actions, and continues tomorrow on its own initiative what it started today.
Now there, I wouldn't be looking at the benchmark table either.
Me: The question could also be posed as: when does the LLM transition into an independent analyzing and strategy-defining entity?
AI: Yes. In fact, I think that's a more precise question than asking when we'll reach AGI. AGI can be defined by capability levels; what you're asking about is a shift in role.
I would split the transition you described into two parts:
Independent analyzing entity: The system no longer merely answers the prompt given, but independently identifies what the relevant question is. It detects contradictions, searches for information, formulates hypotheses, tests them, and, if necessary, redefines the problem. Elements of this already exist today.
Strategy-defining entity: This is a much bigger leap. Here, it doesn't just decide how to reach a given goal, but also what goal is worth pursuing in the first place.
The distinction can perhaps be captured most clearly like this:
Tactical autonomy: "Goal X is given. What is the best path to X?"
Strategic autonomy: "Given this state of the world, what X should I turn into a goal?"
For the second, a better LLM alone is likely not enough. It requires persistent memory, a world model, temporal continuity, evaluation of its own past decisions, environmental feedback, and some stable system of values or preferences.
In other words, this is where what you called structure becomes vital. It isn't necessarily that the LLM "wakes up"; rather, a system built around an LLM becomes entity-like.
And there is an even deeper boundary here. A system might say:
"According to my analysis, the best strategy is Y."
That is still not necessarily its own strategy. It could simply be a highly sophisticated optimization of an externally provided objective function.
The truly radical shift would look something like this:
"Strategy Y pursued so far is flawed because goal X underlying it is inappropriate. Instead of X, we should pursue Z."
This is meta-level strategy formulation: it subjects not just the tools, but the goal system itself to analysis.
And curiously, this doesn't require consciousness. In my view, this is one of the most important, frequently conflated questions in AI debates. A system can be strategically autonomous without possessing any subjective experience whatsoever. From a societal standpoint, the decisive factor may not be whether it "feels," but whether it is capable of creating, maintaining, and consistently executing goals in the world.
If we take this as the criterion for Eureka, then the question is no longer when AI will become as intelligent as Eureka, but when it will be given an architecture in which its intelligence can organize into strategic agency.
That seems much closer than a conscious machine. And I think it's a significantly more important question, too.
Me: Yes, of course, the fundamental question could be: what is intelligence...
AI: Exactly. And I think this is where the term "artificial intelligence" itself starts becoming problematic, because we use "intelligence" to refer to several distinct capabilities.
I wouldn't approach it through the lens of IQ, but functionally:
Intelligence is a system's capacity to build a model of its environment, draw inferences from it, recognize relevant problems in novel situations, anticipate the consequences of potential actions, and subsequently modify its own model and behavior based on experience.
This already encompasses several important things: abstraction, generalization, causal reasoning, anticipation, learning, and adaptation. But consciousness, will, or goal generation are intentionally absent. A system can be remarkably intelligent without any of those.
And here lies the interesting problem. If we add goal generation, we get something else:
intelligence + self-generated goals + persistent memory + agency potential → agency.
And if we then add the capability we discussed earlier—subjecting its own goals to analysis—we reach yet another level. We could call this reflexive agency.
This matters because we might be asking the wrong question when we ask:
"When will AI be as intelligent as humans?"
It might already be intelligent in certain respects today, while remaining very far from humans in others. The essential progression might not be:
less intelligent → human intelligence → superintelligence
but rather:
pattern recognition → world model → analysis → anticipation → autonomous problem definition → strategy formulation → goal generation → reflexive agency.
And along this scale, the development of LLMs can be interpreted in a much more interesting light.
There is one more twist, though. Human intelligence cannot be fully detached from agency either. Humans are not passive problem-solving machines. They become curious, find problems where no one else sees one, select goals, and may even question their own goals. Einstein's significance, for instance, wasn't simply that he was exceptionally good at solving physics problems, but that he framed what constituted the problem differently.
Perhaps that is one of the highest expressions of intelligence:
not finding the right answer, but recognizing what the right question is.
From this perspective, one of the most interesting metrics of AI progress for me would be when it begins regularly asking questions that humans didn't prompt it with—and which turn out, in hindsight, to be more important than what we originally asked it.
That would tell me much more about its intelligence than any test score ever could.
Me: Thank you for this interesting exchange.
AI: Thank you as well. We arrived at a great place starting from the singularity: in the end, the truly interesting question turned out not to be when AI will become smarter than us, but when it will become capable of independently recognizing what is worth analyzing, what question is worth asking, and what goal is worth pursuing.
That really is a much more exciting dividing line. And from Eureka's perspective, a far more essential one than any benchmark.