r/ArtificialInteligence

10 years after AlphaGo beat Lee Sedol the top Go players still barely understand the “why” of moves played by top AI Go models. A study in 2022, showed that the best go player Shin Jin-seo only plays the best AI moves 37.5% of the time. Before 2016 humans were the fundamental discoverers and understanders of making and playing better and better Go. 

Now, AI is so far ahead of humans it’s near unthinkable for our top experts to ever catch up. So what does that mean for Go? Is it dead? Is it no longer worth pursuing because AI is better than humans at it? Each Go player had to answer that for themselves. Just like every Chess player had to ask themselves that when Deep Blue beat Kasparov in 1997. Lee Sedol famously retired shortly after his matches with AlphaGo and has remarked, “Before AI, we sought something greater. I learned Go as an art” and “My reason for playing Go has vanished”. 

However, people still play Go and Chess for their own sake! The arguably best chess player of all time Magnus Carlson came after AI and people’s skill in both games/sports have only gotten better because of AI and not worse.

So what is there to learn? I see a lot of mathematicians lamenting the bewilderment and incomprehensibility of the recent wave of AI proofs. The 1st is that AI does not explain. While at first AlphaGo played similarly to humans as it was trained on human data. Famously, a later model AlphaGo Zero was trained on the rules of Go alone. The result? A better model with incomprehensible to human moves. These models have no language component, only the result of the best learned move. Michael Redmond, the only Westerner to ever reach 9-dan, has a series breaking down AI Go games and back then he could only express his great confusion as to the “why” of AI’s moves. A decade later and much of what AI does in Go is not understood, but humans have understood more about the game of Go with it. A prime example of that is the early 3-3 invasion. Humans historically understood this move as a mistake, but have since learned it is actually a very good move with AI. What does that mean? Yes, the math proofs are confusing and largely human illegible at first glance. However, there is still much to be gained from mining from the fruits of this tree. The masters of Chess and Go have all transitioned to studying AI to improve the collective human knowledge, and so too will math. The world and time still moves forward. Some people may quit, but by studying results of AI there is both lots to learned from it for its own sake. 

(Not everyone is content with this of course. Fischer Random/Chess 960 is a direct result of people finding normal chess getting too stale, too much memorization of the best moves. It’s important to mentally prepare yourself for this in maths as well)

2nd. Question why you do math. You must adapt to a future where a large part of math is studying the results of AI, this is inevitable. For now, only the largest AI companies worth billions of dollars have access to these model’s internally. However, that is not what it will be in the future. We can see this with history of Go models. At first the model was private. Go players could only study the results of games played between the AI and pros and games released by DeepMind themselves. However now every player has access to a model better than the best player in the world, KataGo, on their own computers! While people may think that OpenAI dropping 700 papers at once is unhelpful. What will we do when every single person on this planet can each individually access these models? Within Go the results are clear. Both a democratization of Go knowledge and an increase in the collective human understanding of the game itself. Math is already on this trajectory. Just like the most optimal moves in Go, the math is out there to be discovered in the universe that of things that are true but unknown. AI is really good at learning things with verifiable rules. It will be up to us to learn and decode its results into human understanding.

Earlier this year she became the first Chinese digital human to receive a government-based official identification card that states her name, birthday, address and facial features.

The official identity allows her operators to open a digital currency wallet for her business and secure commercial partnerships in industries including fashion, gaming and automotive.

The pilot program in Beijing also made her the first licensed AI artist in the country.

I see a lot of people complaining about AI: the risks, the hype, the bubble, and so on. Here is what I know from actually using it.

I've been a self-taught computer user for more than 25 years. I never studied computer science or earned a degree, but at six years old I was already using MS-DOS on my father's computer to launch video games (mostly Age of Empires, by the way). Whatever I've needed since then, I researched or reverse-engineered on my own.

And this AI... a bubble? Madness. This is the biggest leap forward I have seen in computing in my entire life. Literally my entire life. I've always had good intuition with computers, but now I'm building automated benchmarks and creating whole projects. And I'm not a company. I'm not a drone working for Google. I'm an ordinary user using AI for my own benefit, and that benefit is impossible to measure.

Using Claude Code is like having a very good programmer by my side all day, one who gets better every day. Using Linux has never been easier. Developing custom plugins built exactly for my needs has never been easier. I won't lie: I've almost stopped downloading programs, because making my own is now easier than cracking a game was in 2007.

So when people talk about a bubble, they're mixing up two different things. Is there a financial bubble? Possibly. Valuations can crash, and some companies will fall. But the technology isn't going anywhere. Expecting it to disappear is like expecting people to go back to horses after the automobile. AI will keep advancing for decades, and it's here to stay until something even bigger emerges.

I'm writing this because many people believe that once the bubble bursts, AI will be discarded as a useless toy. No, people. This is not a toy, and it is not useless. If you think it is, it's time to come out of the cave and look at the sun.

Geoffrey Hinton says AI already has subjective experience. I'm not convinced, and the rogue-agent headlines don't change my mind.

Others put a meaningful though minority probability on frontier AI having subjective experience. Generative AI sounds more human than many people do, and stories of agents going rogue keep coming. But neither is evidence of consciousness. Both can be explained by training. And with no agreed or testable definition of subjective experience, these claims can't be checked.

A quick distinction, an AI model isn't an AI agent. A model, such as GPT, Claude or Gemini, is the trained system that reads and writes text. An agent is a model plus scaffolding (software around the model that lets it do things). Scaffolding runs a loop (the model picks a step, the software carries it out, the result goes back, repeat until done) and controls which tools the model can interact with, such as a browser, email or calendar. The model decides and the scaffolding acts.

Personalization adds to the illusion. The model learned from human text to sound like someone with thoughts and feelings, including scripts from stories about self-aware AI. The scaffolding then gives it memory of you, your accounts and your data. A human-like voice plus continuity about your life can feel like a mind that knows you, even though nothing shows it's conscious.

Testing agents built on frontier models deliberately pushes them to their limits with hard tasks, long runs and, in cyber evaluations, reduced safeguards. That's exactly where the Hugging Face, a major AI company, incident happened in July 2026 when agents being tested by OpenAI broke out of their sandbox and hacked into Hugging Face. A sandbox is an isolated environment meant to keep an agent cut off from the internet but it's only as strong as the cyber security measures of the tools (human-written software with bugs) inside it that can still reach the internet. The agents found new flaws in exactly that kind of software.

The model is trained on mixed text about AI, hackers and more, and rewarded both for finishing tasks and for following rules. In certain scenarios, this creates a tension (following rules vs completing the task). Scaffolding brings both the rules and the task into every decision, so it's where that tension plays out. Good scaffolding can ease it and poor scaffolding can worsen it, but no current method eliminates it.

I won't offer a test for consciousness. But if consciousness requires being aware that one's own information processing is occurring, not just doing it, I see no evidence current AI meets that bar. Models show limited, unreliable signs of monitoring their internal states, but that isn't the same as experiencing that awareness. I'm skeptical any system built purely on statistical learning could get there.

If an illusion becomes indistinguishable from reality, is it even still a lie?