In my TEDx talk at TEDxKamionek Salon in Warsaw, I argue that thriving with AI isn’t about being the fastest to adopt new tools — it’s about adopting them with the right people. Drawing on self-determination theory, I explain why fear-based motivation fails and why being a better early adopter is a collective responsibility, not an individual advantage.
This 18-minute talk is the distillation of everything I’ve learned building an AI education platform, running a community of thousands, and watching people either thrive or burn out trying to keep up with technology. The core message: the future isn’t individual or artificial — it’s collective.
Watch the Full TEDx Talk
Timestamps
| Time | Section |
|---|---|
| 0:00 | Opening — What We Really Want |
| 1:00 | Sam Altman’s “Most Valuable Skill” |
| 2:01 | Be More Adaptable — But How? |
| 3:10 | From Late Access to Early Adopter |
| 4:26 | “What Does Your Mommy Do?” |
| 5:50 | The Five Things We All Do on Computers |
| 7:03 | AI Is Electricity for the Digital World |
| 7:47 | Child Labor and the First Industrial Revolution |
| 9:23 | Behind Every Change Is an Opportunity |
| 10:23 | Self-Determination Theory |
| 10:48 | Pillar 1: Autonomy — Efficiency vs. Effectiveness |
| 12:07 | Pillar 2: Competence — Your Unique Domain Expertise |
| 13:47 | Pillar 3: Relatedness — It’s About People |
| 14:59 | The Baltic Way — 2 Million People Holding Hands |
| 16:04 | Collective Responsibility, Not Individual Advantage |
Why This Talk Matters Right Now
Every week there’s a new AI tool, a new model release, a new “you need to learn this or you’ll be left behind” headline. The entire AI discourse is soaked in urgency and fear. And I get it — the pace of change is genuinely unprecedented. But urgency without direction just creates anxiety. That’s what I wanted to address on that TEDx stage.
The early adoption conversation is usually framed as a personal race: who can learn the tool first, who can ship the fastest, who can automate the most. But after years of teaching AI to tens of thousands of people, I’ve seen that the ones who actually thrive aren’t the fastest adopters. They’re the ones who adopt with the right people around them.
That distinction — individual speed versus collective support — changes everything about how you approach new technology.
The Core Argument: Key Takeaways
We’re All Just Processing Data
Strip away the job titles and what do we actually do on computers all day? Five things: search for data, create data, transform data, move data, and analyze data. AI is getting better at all five of those things every single day. That’s not a threat — it’s a fact. The question is what you do with it.
This is one of the most important reframes I make in the talk. People think AI threatens specific professions — writers, designers, accountants. But the truth is more fundamental: AI is improving at the five basic operations that every knowledge worker performs. Once you see it that way, you stop asking “will AI take my job?” and start asking “how do I work with AI to do these five things better?”
Fear-Based Motivation Doesn’t Work
“Use AI or lose your job” — we’ve all heard it. It doesn’t motivate anyone. Self-determination theory, a well-established psychological framework, says motivation comes from three things: autonomy, competence, and relatedness. Not fear. Not even money.
This is backed by decades of research from Edward Deci and Richard Ryan. When people feel pressured by external threats, they either freeze, comply minimally, or rebel. None of those produce the kind of deep, sustained engagement you need to genuinely learn a new technology. The people I see thriving with AI are intrinsically motivated — they’re curious, they feel capable, and they’re learning alongside others.
Autonomy: Choice Over Efficiency
Just because everyone is mass-producing AI content doesn’t mean you should. There’s a difference between efficiency (doing all things right) and effectiveness (doing the right thing). Mass production creates noise and trash. Couture creates value. You have a choice.
This is a distinction I come back to constantly in my work on AI Second Brain. The goal isn’t to automate everything. It’s to automate the right things — the repetitive, draining tasks that steal time from work that actually matters. Autonomy means choosing what you use AI for, not blindly applying it to everything.
Competence: Your Domain Expertise Is Your Superpower
Your skills aren’t outdated — they’re transferable. When an engineer sits in a room with a biologist, their filters overlap and create something new. That’s biomimicry. That’s what happens when you apply your unique domain knowledge to new fields. Being a generalist with deep roots is an advantage.
I lived this myself. My architecture background gave me systems thinking, spatial reasoning, and a comfort with parametric design that directly translated into understanding AI workflows. The people who are best at using AI aren’t the ones with computer science degrees — they’re the ones who bring deep knowledge from another field and use AI to amplify it.
Relatedness: Surround Yourself with People
Social media isn’t social anymore — it’s media. Courses get outdated the day they launch. We need to move from static to dynamic, and that only happens with people. Find your community. Find your peers. The right people carry you through change.
This is the pillar I’m most passionate about, and it’s the one most tech-focused thinkers ignore. Tools change monthly. Courses go stale in weeks. But a community of people learning together — that adapts in real time. It’s why I built the Autonomee community. Not because the world needed another AI course, but because people needed a space to figure this out together.
The Baltic Way
In 1989, two million people held hands across Lithuania, Latvia, and Estonia — a 690-kilometer human chain called the Baltic Way. That’s what collective action looks like. Being an early adopter isn’t an individual advantage. It’s a collective responsibility. Shape how others adopt technology. Be who you needed when you began.
I grew up in Lithuania. The Baltic Way isn’t just a historical event to me — it’s a lived truth. It showed that massive change doesn’t require individual heroism. It requires ordinary people choosing to stand together. That’s the exact same energy we need in AI adoption: not a few tech elites racing ahead, but millions of people pulling each other forward.
“The future is not individual or artificial — it’s collective.”
Good vs. Bad Early Adoption
The talk touches on something I rarely see discussed in the AI space: there are genuinely better and worse ways to be an early adopter. Here’s what I’ve observed across thousands of community members.
| Good Early Adoption | Bad Early Adoption |
|---|---|
| Starts with a real problem to solve | Chases every new tool announcement |
| Shares learnings with others | Hoards knowledge for personal advantage |
| Applies domain expertise to new tools | Abandons existing skills to “go all-in” on AI |
| Builds repeatable systems and workflows | Creates one-off demos that never ship |
| Learns alongside a community | Learns alone and burns out from information overload |
| Chooses effectiveness over efficiency | Automates everything, including things that shouldn’t be automated |
| Helps others adopt at their own pace | Shames people for being “behind” |
The difference isn’t about speed. It’s about intention. A good early adopter asks “how can this help the people around me?” A bad early adopter asks “how can this give me an edge over everyone else?” Both are using the same technology. Only one is building something sustainable.
What Self-Determination Theory Teaches Us About AI Adoption
Self-determination theory (SDT) is a cornerstone of the talk, and I think it deserves deeper attention because it completely reframes how we should think about getting people comfortable with AI.
SDT was developed by psychologists Edward Deci and Richard Ryan in the 1980s. It identifies three basic psychological needs that drive intrinsic motivation:
- Autonomy — The feeling that you have choice and control over your actions
- Competence — The feeling that you’re capable and effective
- Relatedness — The feeling that you belong and are connected to others
Now look at how most AI adoption is being pushed right now. “Use AI or get replaced” attacks autonomy. “It’s too technical for you” attacks competence. “You’re falling behind everyone else” attacks relatedness. The mainstream AI narrative is designed to undermine all three pillars of motivation. No wonder people are anxious instead of excited.
Flip the script. Give people choice about how they use AI (autonomy). Show them they already have the skills to succeed (competence). Put them in a room with others on the same journey (relatedness). That’s when real adoption happens — the kind that sticks, the kind that actually changes lives.
How This Connects to Building AI Infrastructure
The philosophy in this TEDx talk directly shaped how I think about AI Second Brain. When I talk about building AI systems that work for you, I’m talking about autonomy in practice — not just as a motivational concept, but as an engineering principle.
Here’s the connection:
- Autonomy in infrastructure means owning your AI stack rather than depending entirely on one provider. It means building systems where you decide what gets automated and what stays human.
- Competence in infrastructure means using tools like Claude Code that let you build without needing a computer science degree. Your domain expertise is the superpower — the tool is just the amplifier.
- Relatedness in infrastructure means learning and building alongside a community, sharing configurations, troubleshooting together, and lifting each other up.
The TEDx talk is the philosophy. AI Second Brain is the practice. They’re two sides of the same coin.
Actionable Steps: How to Be a Better Early Adopter
If the talk resonated with you, here’s how to actually put it into practice:
- Stop chasing tools, start chasing problems. Pick one real bottleneck in your work and find an AI solution for that specific thing. Don’t install ten new apps this week.
- Find your people. Join a community where people are learning AI at your level. Not a hype chamber — a real group where you can ask questions and share failures.
- Share what you learn. The moment you figure something out, teach it to someone else. This is how collective adoption works. You don’t need to be an expert. You just need to be one step ahead.
- Protect your domain expertise. Don’t abandon what makes you uniquely valuable. Apply AI to your existing skills, don’t replace them with generic AI outputs.
- Choose effectiveness over efficiency. Before automating something, ask: should this even be done? Not every task deserves to be faster. Some deserve to be eliminated entirely.
- Be patient with yourself and others. Real adoption takes time. The people who rush in and burn out aren’t early adopters — they’re early exhausters.
About the Event
TEDxKamionek Salon — an independently organized TEDx event in Warsaw, Poland, using the TED conference format. This talk was part of the salon series exploring technology, culture, and human potential.
Links
Frequently Asked Questions
What is Goda Go’s TEDx talk about?
The talk, delivered at TEDxKamionek Salon in Warsaw, argues that thriving with AI requires collective action, not individual speed. Using self-determination theory as a framework, Goda explains why fear-based AI motivation fails and why being a better early adopter means helping others adopt alongside you, not racing ahead alone.
What is self-determination theory and how does it apply to AI?
Self-determination theory (SDT) is a psychological framework developed by Deci and Ryan that identifies three core needs for intrinsic motivation: autonomy, competence, and relatedness. Applied to AI adoption, it means people learn best when they have choice in how they use AI, feel capable of succeeding, and are learning alongside a supportive community — not when they’re threatened with job loss.
How can I be a better early adopter of AI?
Focus on solving real problems rather than chasing every new tool. Protect your existing domain expertise and apply AI to amplify it. Find a community of peers learning at a similar level. Share your learnings openly. Choose effectiveness (doing the right things) over efficiency (doing everything faster). And most importantly, help others adopt — early adoption is a collective responsibility.
What does “the future is collective, not artificial” mean?
It means that the biggest impact of AI won’t come from the technology itself, but from how groups of people choose to use it together. Individual tool mastery matters less than communities of people supporting each other through technological change — just like the Baltic Way, where two million people achieved independence not through individual heroism but through collective action.
Why does fear-based AI motivation fail?
Fear triggers fight, flight, or freeze responses — none of which produce genuine learning or lasting behavior change. Research in self-determination theory shows that external pressure leads to minimal compliance at best and total disengagement at worst. Sustainable AI adoption requires intrinsic motivation: curiosity, a sense of capability, and connection to others on the same journey.
