How I Built My AI Survival System: Turning Disruption Into a Playbook for Real Life

What I Learned Studying the Past, Building Models, Games, and My Own Peace of Mind

Every time I dive deeper into AI, whether I’m building a new tool, reading the latest research, or just chatting with friends, the same question keeps popping up.

Is AI going to replace our jobs?

Sometimes it's a real worry, sometimes it's just background noise from the latest headline. But it's always there, humming in the background.

I'll admit, I'm not immune to the anxiety. But I’m also wired to investigate. When I hit a technical problem, I always assume someone has faced it before, and left behind a map. That mentality guided me through a lot of explorations.

So, with this anxiety, I asked:
What does history teach us about surviving disruption?

But here's what I discovered: the survival system isn't a set of answers, it's a way of thinking. Instead of trying to predict the future, I built a methodology for thinking clearly about uncertain futures. It combines historical research with personal experimentation, uses tools to reveal hidden biases, and iterates based on what you learn.

What follows is how I developed this approach, and how you can use it too.

I remembered: with AI, we can do more than read about the future, we can simulate it. I'd seen fascinating experiments such as AgentSociety, where agents interact, adapt, evolve. That got me wondering:
Could I simulate my own version?

Then came the Bolt Hackathon offering very affordable convenience resources. A perfect excuse to go hands-on.
What if I could build something, anything, that let me feel what this kind of disruption might look like?

That’s how this article started:

1. A dive into how people have survived past technological revolutions

2. A simulation of how opinions and wealth might shift

3. A minimal, playable game to help others explore their own AI future

4. And a bit of a personal therapy session

But before I could build, I needed to look back.
How do people actually survive massive change? And what might that look like this time?

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Part I: Echoes from the Past — Historical Technological Revolutions and Human Adaptation

1. The Great Disruptions: A Brief Tour

Looking back, AI isn't the first thing to come along and threaten to upend everything. If you zoom out, history is basically a highlight reel of "oh no, everything's changing again." The only thing that really changes is the speed and the flavor of the panic.

• The Industrial Revolution: Factories and steam engines didn't just make things faster, they made entire professions obsolete. The result? Mass migration to cities, overcrowded slums, and a lot of angry, unemployed craftsmen.

• Agricultural Mechanization: Machines started replacing farm workers. The share of Americans working in agriculture dropped from 53% to 21% in just a few decades—imagine half your town packing up and leaving because a tractor showed up.

• Electrification and the Assembly Line: Ford's assembly line cut car production from 12 hours to 93 minutes. Sounds great? Except turnover at Ford hit 380%, people hated the monotony so much that Ford had to hire nearly 10 workers for every one they wanted to keep.

• The Computer and Digital Revolutions: The late 20th century was brutal for some industries: 238,000 newspaper jobs lost since 2000, a 57% decline. Office automation wiped out entire categories of clerical work.

2. The Human Impact: Displacement, Migration, and Reinvention

The numbers are big, but the real story is what happened to people. Every revolution left a trail of families uprooted, communities hollowed out, and workers scrambling to reinvent themselves.

• Displacement at Scale: The agricultural shift forced 6 million African Americans north in the Great Migration²¹. The Industrial Revolution packed cities with desperate workers, life expectancy for Manchester laborers dropped to 19 years⁴.

• Geographic and Demographic Divides: Each wave hit different groups hardest—Black sharecroppers, women and children in factories, older manufacturing workers, rural communities²⁶'²⁷. The digital revolution has left 70 million Americans in "news deserts"¹¹.

• Family and Community Upheaval: Unemployment and economic stress ripple out, divorce rates rise, mental health suffers, and whole towns can empty out almost overnight. The Rust Belt's decline is a living example: Youngstown, Flint, Cleveland, places that lost half their population when the jobs left²⁸.

3. Adaptation Timelines and Survival Strategies

Here's the part nobody likes to talk about: recovery takes a long, long time. Not months. Not even years. Sometimes, it's generations.

Specific recovery timelines vary greatly by individual, industry, and economic conditions. Research shows that most displaced workers find something within a few years, but "finding work" and "economic recovery" are very different things. A 25% earnings loss in the first year, with 9% never coming back even after six years? That's not recovery—that's permanent scarring.

The first generation often never fully recovers. It's their kids, or their grandkids, who finally find stability in the new world. And some communities? They just never come back.

Collective Action

• 1800s: Early unions, Luddite resistance (1811-1816)

• 1930s: UAW grew from 150K to 1M+ members (1937-1944)²²'²³

• New Deal: WPA employed 8.5 million at $41.57/month²⁴'²⁵

• 1960s: Federal retraining programs launched

• 2000s: Coding bootcamps, online education platforms

Skills Adaptation

• Apprenticeships: Traditional craft-to-craft transitions

• Factory Training: Learn assembly line work in weeks

• Government Programs: Federal retraining initiatives with mixed results

• Community Colleges: Primary retraining providers

• Corporate Programs: Company-sponsored career pivots

Each wave hit different groups hardest, black sharecroppers, women and children in factories, older manufacturing workers, rural communities. The pattern is depressingly consistent: whoever has the least power gets displaced first and recovers last.

4. Opinion and Attitude Shifts

People don't just lose jobs—they lose identities. The emotional arc is real: denial, anger, bargaining, depression, acceptance. (Yes, the Kübler-Ross model fits here too.)

• Initial Resistance and Fear: The Luddites literally smashed machines. Factory workers walked out in droves. Every revolution starts with pushback—sometimes violent, sometimes just stubborn.

• Gradual Acceptance and Reinvention: Over time, people adapt. New jobs emerge, sometimes in places nobody expected. Bank tellers survived ATMs by shifting to customer service and relationship management.

• Intergenerational Change: The first generation often never fully recovers. It's their kids—or grandkids—who finally find stability in the new world.

5. The Uncomfortable Truth

History is a guide, not a blueprint. Every revolution is different. The speed of AI, the scope of cognitive automation, and the global interconnectedness of today's world are all new variables.

Market forces alone have never managed a transition without massive human suffering. Policy, support systems, and collective action matter.

We know disruption is inevitable, that recovery is slow, and that the pain is not evenly distributed. But AI's speed, reach, and ability to automate "thinking" work could break old patterns, or just make them play out faster and harder.

If you’re looking for a neat, reassuring answer, you won’t find it here. History only guarantees that change is hard, messy, and deeply human. But it also shows that, with enough time and support, people do adapt, sometimes in ways nobody could have predicted.

6. Your Personal Migration Map

The most striking pattern from history isn't just that people adapt, it's that successful adaptation almost always involves some form of "migration." Not necessarily moving cities (though sometimes that too), but migrating toward new skills, new networks, new identities.

The agricultural workers who thrived didn't just learn to use tractors; they moved from thinking of themselves as "farmers" to "equipment operators" or "agricultural technicians."

Take a moment to map your own potential migrations:
What adjacent skills could you develop?
What communities exist just outside your current professional circle?
What identity shift might you need to make, not just learning new tools, but fundamentally changing how you see yourself and your value?

The people who navigated past disruptions best were often those who started these migrations before they absolutely had to.

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Part II: Simulating the Future

— GenAI Challenge 9

I've always believed that if you really want to understand something, you have to get your hands dirty. Reading about disruption is one thing; trying to simulate it is a whole different beast.

So, as part of the GenAI Challenge series , I set out to build a minimalist simulation, part experiment, part therapy, part "let's see what breaks."

1. Why Build a Simulation?

Let's be real: nobody can actually predict the future. But that doesn't stop us from trying. The real value isn't in getting the answer right, it's in seeing what happens when you poke the system, change the rules, or just let a bunch of agents talk to each other and watch the chaos unfold.

I wanted to know:

• How do people's opinions about AI actually change over time?

• What happens to wealth and opportunity as attitudes shift?

• Can a simulation surface the hidden biases and feedback loops that shape real life?

2. Project Design and Architecture

I tried popular frameworks like Mesa (for agent-based modeling) and LangChain (for LLM apps), but they felt like overkill for what I wanted: a simple, honest playground. So I built my own barebones setup:

• Six agents, each with a backstory: From the AI researcher to the factory worker, each one had their own starting opinion, wealth, and set of concerns.

• Monthly pairings: Every month, agents were randomly paired for a conversation. Over 10 years, that's 360 unique dialogues.

• Local LLM conversations: Powered by Ollama (llama3.2), with no external APIs or canned scripts, just raw, personality-driven talk.

• Opinion and wealth feedback loops: After each chat, agents' attitudes and financial trajectories could shift based on trust and tone.

• Economic modeling: Optimism toward AI led to compounding income growth; skepticism came at a cost.

3. Key Findings and Results

The end results got interesting:

• Everyone became more positive about AI. Even the skeptics. Over time, conversations chipped away at resistance.

  

• Wealth inequality exploded. The agents who embraced AI early ended up way ahead. Attitude wasn’t just a vibe, it was a compounding advantage.

  

• Conversations mattered. The more agents talked, the more their opinions shifted. Trust and familiarity built up over time, and even the most stubborn characters softened (a little) after enough exposure to new perspectives.

  

• Biases were everywhere. The system rewarded optimism and punished skepticism. There were no modeled AI failures, no negative shocks, just a steady march toward acceptance and wealth for the "right" kind of agent.

And here's the kicker:
The simulation didn't just reflect reality, it amplified certain patterns. The rules you set at the start (who gets rewarded, what counts as "success") shape everything that follows.

4. What the Simulation Taught Me

I wish I could say this was a perfect model of the future. It wasn’t. But it was honest—and that made it useful.

• It was a mirror, not a crystal ball. The simulation reflected my own assumptions, values, and blind spots. The rules I set shaped everything that followed.

• Built-in biases mattered. Optimism was rewarded. Skepticism got punished. There were no modeled failures, no shocks, just a steady march toward success for the "right" kind of agent.

• Representation was limited. Six agents can’t stand in for society. There were no group dynamics, no media influence, no cultural diversity beyond the initial setup.

• Complexity is hard to fake. Real life is messier. People change jobs, move cities, face setbacks, get lucky. The simulation couldn’t capture that, but it did surface patterns worth paying attention to.

If there’s one thing I took away, it’s this:

Simulations don’t predict the future, they help you see your own logic, assumptions, and blind spots more clearly.

And for that alone, it was worth building.

5. What Your Assumptions Reveal About You

Building this simulation reminded me that: the future isn't just uncertain, it's shaped by whose assumptions get built into the system.
What if the early adopters hit a wall?
What if the skeptics were protecting something valuable?

This makes me wonder:
What assumptions are you building into your own mental model of the future?
Are you unconsciously betting that your current advantages will compound, or that your current struggles will continue?

The most valuable thing you can do right now might be to actively seek out perspectives that challenge your own predictions, not to change your mind, but to stress-test your strategy against scenarios you haven't considered.

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Part III: Playing with Possibility — The Bolt Game as a Preview of AI Life

Reading about disruption is one thing. Living through it, even virtually, is something else entirely.

I’ve always loved how games like The Sims, BitLife, or Papers Please, they show how simple stats and branching choices can create stories that feel real. Not just facts, but stress, trade-offs, weird little wins and unexpected losses. That’s the kind of empathy and clarity I wanted to build.

When I see the Bolt Hackathon coming out, it’s a great opportunity to maximize the affordable resources to build something like that. A game that doesn't just tell you "AI will change everything," but let you see how those changes ripple through your career, your family, your finances, and your mental health.

That’s what I tried to do with the Bolt Game: it doesn’t hand you answers. It hands you dilemmas.

1. Game Design Highlights

There are unmeasurable complexity in real life, but here's what I focused on:

• Interconnected Systems: Your career decisions affect your stress, family, finances—and vice versa.

• Probabilistic Modeling: Instead of giving you a single outcome, the game runs thousands of Monte Carlo simulations to show you a range of possible futures.

• Psychological Realism: Your character has personality traits, cognitive biases, and mental state that evolve over time.

• Social Context: Where you live, your background, and your support systems all shape what's possible—and what's not.

2. How the Game Works

The Bolt Game is built on a layered architecture, what you actually do:

• Create a character with job, family, location, and traits.

• Face yearly decisions like retraining, relocating, investing, or doubling down.

• Navigate trade-offs across money, relationships, and wellbeing.

• View results through charts, timelines, and “what if” paths.

3. What the Results Taught Me

The game wasn't about finding the right answers. It was about feeling the right questions.

• Personalized disruption beats generic headlines. It’s different when you feel the impact.

• Small choices compound. One good or bad decision doesn’t make or break you, but patterns do.

• Inequality is structural. Starting position, geography, and support systems shape your odds, and games make that painfully clear.

When you're forced to make choices, even virtual ones, you discover priorities you didn't know you had. You realize which risks you're actually willing to take and which stability you're not willing to sacrifice.

👉 Try it out at lifepivot.site

4. The Tradeoff: Simplicity vs. Realism

This wasn’t a sprawling game with servers and real-time updates. It was lightweight, local, and ephemeral, intentionally simple so I could focus on the emotional clarity over simulation complexity.

That simplicity had tradeoffs:

• No persistence — If you closed the tab, it was gone. No saving progress, no picking up where you left off.

• Static data — All economic and policy conditions were hardcoded. The “what if” scenarios were scripted, not connected to real-world news or data.

• No multiplayer or community features — Each simulation was a solo run. You couldn’t compare paths with others, share results, or explore collective outcomes.

• Performance constraints — Monte Carlo simulations are heavy. The browser could only handle so many iterations before lag set in.

• Abstracted realism — Mental health, relationships, and financial systems were simplified. No machine learning, no emergent behavior—just rules and randomness.

• Limited personalization — It only used the inputs you gave it. No syncing with real-world data, no adaptive feedback, no learning over time.

Was it limited? Totally.
But that was also the point.

By stripping it down to the core mechanics—choices, consequences, feedback loops—I could focus on emotional clarity over simulation complexity. And for a lot of people who tried it, that was enough to trigger reflection.

So no, it wasn’t dynamic in a persistent or social sense.
But it was dynamic in a human sense: every run was different, and what you learned depended on how you played.

5. Practice Before Pressure

Here’s what stuck with me: the hardest part of disruption isn’t getting it right, it’s moving forward when you’re not sure what “right” even looks like.

The game gave me a space to try, fail, and reflect, with zero real-world risk. That’s the kind of low-stakes rehearsal I need more of in real life.

You don’t need to build a simulation.
But you can create space to practice decisions:

• Talk through hypothetical career shifts with friends

• Sketch out “what if” financial plans

• Learn from people who’ve weathered change before

Because when things get uncertain, what you’ve rehearsed matters more than what you’ve read.

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The real survival system isn't the simulation I built or the game I created. It's the methodology: when facing uncertainty, combine historical research with personal experimentation, build things that reveal your biases, and iterate based on what you learn. The thinking process matters more than the tools.

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Putting It All Together: Your AI Adaptation Playbook

If you've made it this far, you've seen the patterns: history's messy recoveries, the weird feedback loops of simulation, and the gut-check moments that only a game can deliver. So what do you actually do with all this?

The System Behind the System

Looking back, I can see the thinking system that developed:

1. Historical Grounding: Research past disruptions to understand patterns, successful adaptation usually starts before you have to.

2. Bias Detection: Build models, run scenarios, or just make your assumptions explicit. The goal isn't accuracy, it's clarity about your own thinking.

3. Experiential Learning: Find ways to feel abstract concepts. Games, simulations, conversations, anything that makes trade-offs concrete rather than theoretical.

4. Iterative Reflection: Regularly revisit your assumptions. What's changed? What's working? What blind spots have you discovered?

In uncertain times, the ability to think clearly about scenarios is more valuable than any single prediction. This methodology doesn't tell you what will happen, it helps you prepare for multiple possibilities while staying honest about your own blind spots.

Your Personal Playbook

You don't need to build a full simulation or design a game to start adapting. Here's a simple system you can use—one that borrows from all three worlds:

1. Audit Your Current Skills and Networks

• What do you know how to do? Who do you know who can help?

• Where are you most vulnerable to disruption? Where are you most resilient?

2. Map Out Possible Scenarios

• What's the best case, worst case, and most likely case for your work, your family, your finances?

• What would you do if your main source of income disappeared tomorrow? What if a new opportunity landed in your lap?

3. Identify Support Systems

• Who can you call on for advice, resources, or just a reality check?

• What communities, organizations, or tools are available to help you retrain, relocate, or regroup?

4. Set Up a Regular Review or "Adaptation Checkpoint"

• Once a month (or quarter), check in: What's changed? What's working? What needs to be rethought?

• Don’t wait for a crisis—make adaptation a habit, not a scramble.

Keep In Mind

This isn’t a one-and-done process. The world will keep changing, and so will you. Share your system, get feedback, and keep iterating. Adaptation is ongoing—your playbook should evolve with you.

If you try any of these steps, or build your own version, I’d love to hear about it. The more we share what works (and what doesn’t), the better prepared we’ll all be for whatever comes next.

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