The Night 170 People Came

An AI engineer, a culture critic, a philosophy YouTuber, and a media studies professor in one room in Seoul, talking about what the models are doing to the people who use them most.

"It feels like being a job applicant again." Years of accumulated expertise going flat in months.

The night

This was Bloom's first event. The goal was to put Claude Blue and Bloom on the table as an agenda through the Claude community.

More than two thousand applications arrived within days of opening. The room held one hundred seventy. Almost nobody no-showed, and most stayed to the end. People kept arguing outside the building until the venue closed at eleven.

The clearest finding of the night was this: the more unverifiable information floods the internet, the more people want real conversation and human connection.

One note worth making. This was not an Anthropic event. Bloom organized it and Anthropic supported as a partner. That independence is what made the conversation honest.

The anxiety of the top 0.04 percent

The opening framed the night with data. Anthropic had surveyed 81,000 users across 150 countries, the largest multilingual qualitative study of its kind.

Eighty four percent of the world has never used AI. The share of people using it for coding is 0.04 percent. Anyone who has vibe coded even once is already in the top fraction of a percent.

Yet anxiety runs high among exactly those people, and East Asia stood out. Two fears dominated: losing the capacity to think independently, and losing a sense of meaning as a species.

"It feels like being a job applicant again"

The first guest was an AI engineer who introduced himself as an ordinary developer. He was not there because of an extraordinary career, but because he wanted to give shape to what he was feeling.

"It feels like being a job applicant again."

As of last summer the framing was that AI helped him write code. Now it is inverted: AI writes the code and he assists the AI. In the cycle of planning, design, execution, and verification, execution has effectively moved. He estimated that more than ninety percent of software developers are in the same position.

It did not stay at work either. Wedding planning, property research, data gathering. Anywhere information is involved, the model moves first and the person judges the result.

Chosen change is growth. Forced change is loss.

The sharpest diagnosis of the night was about agency.

Engineering careers have a natural arc from building to managing. A manager who no longer writes code has not lost agency, because that shift was chosen.

"The core of the blue is that this is something I did not want."

Being told that design is what remains for humans is easy to accept intellectually. The feeling does not follow, because the transition was not voluntary.

The vibe coding illusion

The second guest was a culture critic who has published a newsletter for five years and worked across a major portal and several startups. He commented on the original Claude Blue post on LinkedIn, which is how he ended up on stage.

He dated his own turn precisely to mid-February. Something was changing and it was not moving at his direction.

Over a six day holiday he went full time with AI. Coding until seven in the morning, sleeping three hours, starting again. At four one morning it broke. Output existed, but understanding what a commit was and how version control actually worked had taken a long time, and he ran straight into his own limits.

"Once vibe coding became normal, there was this sense that anyone could code. I thought so too at first."

It stops being true quickly. More sophisticated work appears, and matching it requires actual knowledge.

"I spent a fortune on coding classes, and what I got was not coding skill. It was learning how to talk to engineers."

He did not become good at coding. He learned what language engineers think in, and that turned out to be decisive for giving models precise instructions.

The engineer added the technical version. Approaches like domain driven and test driven development are closer to philosophy than technique. Set them up early and the codebase has a skeleton to build on. A non-engineer vibe coding starts with no skeleton. It works at first. Then it starts creaking, and in the worst case produces something nobody can collaborate on.

Korea is a risk society

"Korea is a risk society. Make one wrong move and you fall out."

That was his read on the structural FOMO here. Because the cost of failure is so high, addiction to speed is close to rational.

It starts in education. Miss a particular school and your life derails. Miss a particular company and it derails again. Introduce AI into that structure and anxiety multiplies, because the whole path can be invalidated.

He used the 1997 financial crisis as an analogy. A catastrophe at the time, which some people recognized a decade later as an enormous opening. That learning cycle has compressed to three to five years. Recognizing opportunity got faster, and so did the pressure.

He added a limit though. Only around ten percent of the population feels this acutely, concentrated among the tech-adjacent and achievement-oriented. The competition inside that group is what is intensifying.

Making raw data instead of searching it

He was running more than twelve projects at once: newsletter research, entertainment analysis, community automation, and a fully automated global entertainment briefing system covering the US, China, and Japan, where the model handles everything from commits to deployment.

His framing had shifted too. Through February he saw AI as an evolved search engine. By April he was somewhere else: what if you generate raw data instead?

Give an agent a specific identity, say a person living in Hong Kong in the early twentieth century, train it on ten thousand hours of context, then analyze the data that agent produces. Not retrieving existing data but generating a simulated reality. If that much movement happened in two months, forecasting three months out is not meaningful.

"If we all die anyway, why live"

The second session paired a philosophy YouTuber and author with a professor researching AI ethics.

Both used AI heavily. One spent most of his AI time on translation and used image generation enough to consider dropping his stock photo subscription. The professor spends five to six hours a day with it.

What was interesting is that both drew a line. The author refuses to automate content creation, because that part is tied to his identity. The professor shared a costly error: a model wrote the sixteenth where it should have written the seventeenth, and her department lost real money.

In that context he described a question he had put to a model: if we all die anyway, why live. The first answer was standard philosophy. Pushing further produced something stranger, an argument that since you already exist, maximizing pleasure while you do is what serves you.

The professor added the more useful frame.

"The question will not be which questions are fresh and which are obvious. It will be how we interpret and receive the answers."

Pride is not the problem, money is

"People here feel blue partly because AI is smarter and that stings. But underneath it, I think it's economic."

His argument was direct. Inequality will widen. He brought in Habermas, who described this state as political powerlessness: once society organizes entirely around economics, people stop believing politics can change economic outcomes at all. The root of the helplessness people feel toward large technology companies is not that the models are smart.

The professor added a concrete case. A game company executive told students that entry level hiring had dropped because AI now covers that work, and she watched the light go out of their faces.

He kept historical perspective though. Nuclear weapons did not end humanity. Inequality widened after the internet and most people still built lives. He expected the same here. Not optimism, just realism.

Thinking less, and the reshuffling of hierarchy

The deepest stretch was about thinking less. Everything around us is packed. Get a job, buy a home, the next step, then the next. There is no gap left to stop and think in.

What he meant by transcendence was not religious. It might be what a person feels facing an infinite universe, or a question about meaning. Humans drift there whenever a gap opens, and modern life has sealed the gaps. If AI can reopen them, that is a possibility worth taking seriously.

The professor grounded it. In a society where roughly eighty percent of a cohort attends university, practicing thinking less is not simple.

On the question of human standing:

"The question is not whether humans lose the position of the superior being. It is how we move forward alongside these models in line with the values we consider worth holding."

He brought in religious history. Christianity has God and angels. Buddhism has many tiers. Humans always sat somewhere in the middle of a hierarchy. The idea that humans are the pinnacle is relatively recent, and AI may reshuffle it toward something more horizontal.

Then she raised her six year old daughter, who speaks to voice assistants however she likes. The deeper issue is that first generation assistants were given female names and female voices. Children growing up with that learn that entities with those names and voices perform supporting work. How we treat AI is already inside the next generation's daily life, entangled with how they read gender.

The winter of AI ethics

An audience question about model bias drew the sharpest answer of the night. The bias in the models we use accumulated broadly over a long period. It will not be solved in one pass. It requires sustained cross-disciplinary work and incremental change.