Today, burnout, extreme working conditions, and the hidden human costs behind them have become a critical topic across Silicon Valley and the AI industry at large. Research and development of LLMs (Large Language Models) is becoming increasingly competitive, bringing with it workplace fatigue and sacrifice. Amid high technical standards, fierce speed races, and ever-rising market expectations, many researchers and developers are losing their balance. This summary provides a detailed, chronological overview of the burnout phenomenon in AI development, its causes, and the path forward.

1. Silicon Valley and the Dominance of '996' Work Culture

These days in Silicon Valley, "how hard everyone is working" is the most visible topic of conversation. In particular, extreme work patterns like '996 (9 AM to 9 PM, 6 days a week)', '997', and even '002 (midnight to midnight with only 2 hours of rest)' are spreading. Some of this is merely performative posturing on social media, but a significant portion reflects actual reality. I've been affected too, and so have my friends around me.

"All this hard work stems from the endless pressure to remain relevant within the most exciting technology of our generation."

The 'game' of LLM technology has changed from what it used to be. The window of opportunity to participate in the market is genuinely closing -- this is no longer just a feeling, but reality. While the market has grown and models have diversified in type and size, the bar for technical achievement keeps rising. In this environment, a relentless race continues where people sacrifice work-life balance just to survive the competition.

2. The AI Industry: A Vicious Cycle of Extreme Competition and Burnout

AI is walking the same path that other industries have traveled before, but at an extremely accelerated pace. For example, the case of Apple engineers stationed in China for extended periods, unable to maintain their marriages, is frequently cited.

"Beyond the divorce stories, you need to look at the death cases." -- Patrick McGee, in an interview about Apple in China

This closely mirrors the current reality of the AI industry. The Wall Street Journal recently shed light on this reality, publishing an article stating that "AI workers are putting in 100-hour weeks consumed by the race for new technology." Looking at the lives of the researchers in the article:

"Josh Batson no longer has time to even check social media. The only place he gets dopamine is Anthropic's Slack channel."

"Several researchers compared the current situation to wartime."

There is also criticism that comparing AI researchers to wartime is inappropriate. The important point is that people are learning firsthand just how exhausting it is to perform at an elite level in a group environment for an extended period.

3. AI Research and the 'Elite Athlete' Mentality -- But Rest Is Desperately Needed

Over the past few months, the sensation of working at the LLM frontier has been compared to team sports -- specifically, the experience of being on an elite college rowing team aiming for consecutive championships. The goal is distant, the gap between success and failure is incredibly subtle, and daily life is a sequence of small, repetitive tasks. However, unlike athletics, the difference is that workplace culture isn't as tight-knit and cohesive as a sports team.

"OpenAI's culture is often described as cult-like, but the core members working 996, 997, 002 truly seem to love it. When you love what you do, work isn't work -- it's similar to when you're training for a sport."

Like athletes, the value of proper rest is also important.

"If you push without resting, your mind dulls much faster than your body. Push too hard and creativity vanishes -- you end up seeing only narrow paths. The deeper into burnout I fall, the worse my writing and judgment become."

The confession here is that the very process of crossing psychological and physical limits is already the swamp of burnout -- a fact recently felt with acute urgency.

4. Teams, Culture, and the Reality of Fierce AI Development Competition

In LLM development, the quality of team culture is decisive for outcomes.

"Even if you bring in experienced hires who know the codebase well, that doesn't change the fundamental dynamics of the team."

The three pillars of AI capability are:

• Internal tools (code, recipes, etc.)
• Resources (compute, data)
• People (leadership, management, practitioners) All three elements are critical.

Emerging organizations like SSI, Thinky, and Reflection may have abundant resources, but:

"Given that existing companies already have established tools and architectures, simply obtaining 'unlimited compute' doesn't confer competitiveness."

Ultimately, the gap in development velocity keeps widening, making 'catching up' increasingly difficult.

5. Human Limits and the Future of the AI Industry

Interestingly, previously people expected the AI bubble would burst at economic limits (money, M&A, etc.), but:

"Now I've come to think that human limits might actually be the more sensitive factor."

As technical standards rise, only focused, concentrated work can produce quality models. This process is harder than ever. In the past, tweaking a pipeline or two would yield results, but:

"Now, if you build something half-heartedly, it becomes an expensive experiment that nobody uses."

Even individual work has become explosively complex.

"I feel less like a researcher now and more like a PM who anticipates what problems will arise and coordinates resource allocation accordingly."

Hobbies and personal rest are growing ever more distant, and the field is increasingly becoming an 'all-in or nothing' domain.

"The line between culture and performance is very thin. It's hard to clearly tell where it becomes poison and where it becomes strength."

On a personal level:

"I'm burdened by the responsibility to make the future Olmo model truly excellent, and simultaneously the pressure to keep the open model ecosystem healthy."

6. The Long Race of AI Progress and the Questions That Remain for All of Us

Right now, there is no immediate solution or finish line in sight.

"Looking back someday, we'll surely recognize that moment as the turning point, but right now, I can't tell."

And the biggest question:

"Is all of this really worth it? How much longer can we endure this? It's not like we'll suddenly quit when AGI arrives -- AI is now a complete 'long game.'"

Finally, regarding the only reason to keep going:

"The only reason I keep challenging myself in this work is because I want to make AI a wonderful technology for the world."

Some agree with this sentiment, while others are running purely in pursuit of wealth and fame, it's added. The moment you hit the wall (your limits), each person's 'why' becomes the only force that keeps them going.

Closing

Burnout and competition in the AI industry are intensifying by the day. In an era where people, culture, and teamwork matter more than ever, it's time to ask ourselves whether our efforts are truly for 'better AI' or whether we're becoming expendable fuel in an endless race. Going forward in this 'long journey,' the greatest challenge for each of us will be to protect our own values and find balance.