Grow Beyond Your Team Through Communities | Generated by AI
Question: When working in large companies, mid-size companies, or startups, if my close team is not interested in AI or new technology, is it good to share and discuss in other engineering groups? Since the right direction often looks useless early (like AI in 2016 or 2023), do we need same-interest groups to keep learning so that eventually we go far and get better jobs or build startups?
Answer:
Yes — this is not only reasonable, it is a very common and effective strategy. Many engineers grow outside their immediate team, not inside it. Your thinking is aligned with how long-term technical careers usually develop.
Here’s why this works:
1. Your team ≠ your growth boundary
A company team is optimized for delivery, not exploration.
- Team cares about deadlines
- Team cares about stability
- Team avoids risky tech
- Team follows proven stack
But career growth requires exploration:
- new fields (AI, distributed systems, infra)
- long-term bets
- uncertain technologies
- learning before it’s useful
So it’s normal your team isn’t interested.
This is why engineers create parallel learning circles:
- Slack/Discord engineer groups
- open-source communities
- internal cross-team tech channels
- meetups / online groups
- small AI study groups
These groups act as your second brain network.
2. Early correct directions always look useless
You are exactly right. History repeats this pattern:
- AI in 2016 → “just research”
- LLM in 2020 → “toy demos”
- LLM apps in 2023 → “not production”
- Agents in 2024 → “overhyped”
- 2025–2026 → companies suddenly hiring for all of them
This happens because:
Early stage:
- unclear value
- unstable tools
- low performance
- no business model
Later stage:
- infrastructure ready
- cost drops
- tools mature
- suddenly high demand
People who stayed consistent early win later.
3. Same-interest groups create long-term compounding
This is the most important part.
When you join a focused group:
- you share papers
- share tools
- discuss ideas
- build small projects
- track trends
- motivate each other
Individually:
- easy to stop
- easy to doubt direction
- no feedback loop
Group:
- momentum
- accountability
- idea cross-pollination
- long-term persistence
After 2–3 years:
- everyone in group becomes advanced
- some switch jobs
- some start startups
- some become experts
- network becomes powerful
This is how many startups form.
4. This is exactly how many AI engineers emerged
Typical path:
2019–2020 small group exploring transformers
2021 playing with GPT-2 / GPT-3
2022 trying Stable Diffusion
2023 building LLM tools
2024 doing agent systems
2025 become “AI engineer” / start company
They didn’t wait for their team.
They built parallel interest networks.
5. You also reduce career risk
If your team:
- uses outdated stack
- ignores AI
- doesn’t innovate
You risk:
- stagnation
- skill mismatch
- harder job switch later
But outside group:
- you track industry direction
- build future skills
- build portfolio
- build connections
So you’re future-proofing yourself.
6. Long-term effect (very real)
After 3–5 years:
You suddenly notice:
- you understand AI deeply
- you built multiple tools
- you know like-minded engineers
- you see opportunities early
- recruiters reach out
- startup ideas appear
It feels like: “One day I realize I already went far.”
That’s exactly how it happens.
7. Best practical strategy
Do both:
At work:
- do your assigned job well
- be reliable
- don’t fight team direction
Outside team:
- join AI groups
- discuss trends
- build side projects
- share ideas
- learn continuously
This is low conflict + high growth.
Final insight
The people who go far usually:
- don’t wait for environment
- build parallel learning paths
- find same-interest peers
- persist for years
You’re describing a very strong long-term strategy.
Yes — keep doing it.