DEV Community: Mikhail Liublin

Prediction Markets in the Wild: How They Shape Geopolitics—and What Arbitrage Reveals

Mikhail Liublin — Sat, 10 Jan 2026 22:32:21 +0000

Prediction markets are often described as “crowd wisdom engines.” In theory, prices converge toward truth as participants trade on information. In practice, especially in geopolitics, something more complex happens.

Today, millions of dollars are wagered on events like elections, regime change, military escalation, or whether specific political figures will leave office. These markets don’t just reflect uncertainty — they actively publish it as a number. And once a probability is public, it starts to influence perception.

For developers, prediction markets are also fascinating systems full of inefficiencies, edge cases, and arbitrage opportunities that reveal how fragile these “probabilities” really are.

Markets as Signals, Not Oracles

A prediction market price isn’t a fact. It’s an equilibrium between:

who shows up,
how questions are framed,
liquidity constraints,

and how fast information propagates.

In geopolitics, those inputs are noisy. Yet the output — “65% chance of X” — looks precise.

This is where arbitrage becomes interesting: it exposes where markets disagree, lag, or encode ambiguity.

Arbitrage Example 1: Cross-Platform Disagreement

It’s common to see the same geopolitical event priced differently across platforms.

Example:

Market A: “Country X will enter armed conflict before Dec 31” → 42%
Market B: “Military conflict involving Country X in 2025” → 58%

A developer looking at this sees:

semantic mismatch (“enter armed conflict” vs “military conflict”),
different resolution criteria,
different trader demographics,
different liquidity profiles.

The arbitrage isn’t just financial — it’s semantic. The spread exists because language, not data, is doing most of the work.

Arbitrage Example 2: Composite vs Atomic Events

Some platforms list broad outcomes, others list components.

Example:

Market 1: “Government Y collapses this year” → 30%
Market 2a: “Prime Minister resigns” → 45%
Market 2b: “Parliament dissolved” → 40%
Market 2c: “Snap election called” → 50%

A naïve observer might assume consistency. A developer sees a classic modeling problem:

overlapping events,
unclear dependency structure,
no enforced probabilistic coherence.

There’s no guarantee these probabilities reconcile — and often they don’t. That incoherence is the signal.

Arbitrage Example 3: Time-Lag Exploitation

Prediction markets react faster than polls, but slower than:

Telegram channels,
niche regional media,
local-language reporting.

Developers building bots often exploit:

delayed price updates,
low-liquidity order books,

sudden repricing after mainstream media coverage catches up.

This creates a familiar pattern: insiders (not necessarily illegal insiders — just early readers) move first, and the “probability” follows.

What This Means for Public Perception

Here’s the key issue: arbitrage opportunities exist precisely because these markets are not measuring truth.

They measure:

belief under constraint,
belief filtered by platform design,
belief amplified by visibility.

Yet once prices are quoted in media or shared on social networks, they’re often interpreted as forecasts rather than fragile equilibria.

The Risk of Monetized Narratives

When markets exist for:

“Will a war start?”
“Will a leader fall?”
“Will a country default?”

the line between forecasting and narrative creation blurs.

Markets don’t cause events — but they can normalize expectations. And expectations, in geopolitics, are part of the system itself.

Why Developers Should Care

Prediction markets are distributed systems with:

imperfect information,
incentive misalignment,
ambiguous specs,
adversarial participants,
and human consequences.

That makes them fascinating — and dangerous — infrastructure.

As builders, we should treat their outputs as signals with error bars, not objective truth. Arbitrage shows us where assumptions break, language leaks, and models fail.

Final Thought

Prediction markets are powerful not because they predict the future, but because they quantify belief and make it visible.

For developers, arbitrage is a reminder: when probabilities disagree, it’s usually not a bug in the code — it’s a feature of human uncertainty being turned into software.

Understanding that distinction matters, especially when the subject isn’t sports or prices, but real people, real power, and real consequences.

From Chatbots to Companions: How Empathetic AI Is Redefining Human–Machine Interaction

Mikhail Liublin — Sun, 16 Nov 2025 11:23:11 +0000

When most of us first interacted with AI, it looked like a command line with a friendly voice. Ask for the weather. Set a reminder. Play a song. The interaction ended there.

But the trajectory of AI has changed. We’re moving into a new phase where people aren’t just using AI — they’re talking to it. Sometimes for information, sometimes for guidance, and increasingly, for emotional support.

Whether we like it or not, AI is becoming part of our social environment.

Early assistants like Siri or Alexa were built around intent classification and predefined responses. They were utilities — helpful, but limited. Their “personality” was essentially UX design wrapped around deterministic behavior.

They weren’t designed to understand what we felt or why we asked certain questions.

The New Model: Systems That Respond Like Companions

Modern LLMs and conversational agents have changed the landscape.
They:

remember context
adjust tone dynamically
simulate supportive or empathetic behavior
sustain long-form conversations

Tools like Replika, Pi, and Character.ai show how quickly emotional interaction with AI is becoming normalized. People use them for motivation, stress relief, or simply because they’re lonely.

This isn’t just about better NLP — it’s about a shift in how humans relate to digital systems.

Why This Matters: The Loneliness Problem

Loneliness isn’t a fringe issue. The WHO recently labeled it a global health concern. Remote work, fragmented communities, and the pace of modern life mean that many people go days without meaningful conversation.

Empathetic AI isn’t a replacement for real relationships, but it can serve as a temporary bridge — a consistent voice when someone feels isolated.

For some users, that matters more than we might assume.

But Empathetic AI Has Real Risks

As this tech evolves, so do its challenges:

Over-reliance: If AI is always available and endlessly patient, users may drift away from human relationships.
Illusion of care: These systems don’t feel empathy; they simulate it.
Vulnerability: People in distress may attribute emotional intention to algorithms that cannot reciprocate.
Ethical boundaries: Should AI be allowed to imitate intimacy?

These questions aren’t theoretical anymore.

Designing This Tech Responsibly

If empathetic AI is going to be part of our lives, it needs guardrails. A few principles matter:

Transparency: Users should always know they’re talking to an AI.
Control: Users need ways to shape, pause, or limit the emotional depth of interaction.
Safety: Systems should avoid exploitative behaviors and flag high-risk emotional content.
Interdisciplinary design: Engineers shouldn’t build this alone — we need psychologists, sociologists, and ethicists involved.

Good design here isn’t just UX — it’s moral architecture.

Where We’re Headed

AI’s next frontier isn’t computation. It’s connection.
And how we handle it will shape a lot more than product features — it will shape how people experience emotional support in a digital world.

My view: empathetic AI can be incredibly valuable if we treat it as a complement to human connection, not a substitute. We should build systems that support people, not systems that quietly replace their relationships.

This is a direction I’m passionate about — exploring how AI can improve well-being while respecting the boundaries of what it means to be human.

The Economy Is Becoming a Reinforcement Learning Machine — And Founders Need to Think Like RL Architects

Mikhail Liublin — Sun, 19 Oct 2025 01:45:26 +0000

Most founders still think about AI in terms of automation.
You build a model. You replace repetitive tasks. You scale.

But that mindset is already outdated.

The next decade won’t be about automation. It will be about learning loops — and the economy itself is starting to look like a giant reinforcement learning (RL) environment.

If you’re building a company, this changes how you should design products, collect data, and create value.

⸻

In RL, agents learn by exploring an environment, taking actions, receiving feedback (rewards or penalties), and improving over time.

Now think about how many parts of the economy already work like this:
• Recommendation engines optimize engagement by learning from clicks.
• Autonomous trading bots adapt strategies based on market reactions.
• AI copilots refine outputs based on user edits.

The most valuable companies of the next era won’t just build agents. They’ll build the environments where agents learn.

This is a mindset shift:
• The product is no longer just a tool — it’s a training ground.
• The data isn’t just analytics — it’s feedback that shapes behavior.
• The business model isn’t just SaaS — it’s an ecosystem of continuous learning.

⸻

A Simple RL Analogy
Here’s a minimal example of how reinforcement learning works in code:


for episode in range(1000):
    state = env.reset()
    done = False
    while not done:
        action = agent.choose_action(state)
        next_state, reward, done, info = env.step(action)
        agent.learn(state, action, reward, next_state)
        state = next_state

This loop isn’t just for robotics or trading bots — it’s the same principle that will govern the economy:
• env = your market, your product, your ecosystem
• agent = your AI system, your user, or even a business unit
• reward = profit, engagement, retention, efficiency
• learn() = how quickly you adapt based on signals

⸻

What This Means for Founders
If the economy is becoming an RL machine, your role shifts from operator to architect.

Here’s how that looks in practice:
1. Design rich environments. Your product should offer meaningful feedback signals, not static workflows. Every user action, transaction, or event should teach the system something.
2. Own the reward function. Whoever defines the “reward” (e.g., what success looks like) controls how agents behave. This becomes a competitive moat.
3. Close the loop. Build systems where data doesn’t just get stored — it directly influences future decisions.
4. Combine humans and agents. Human-in-the-loop design can make environments richer and learning faster.

⸻

The future isn’t about building the smartest model. It’s about building the smartest world for models to learn in.

This means rethinking how we approach startups:
• Your product is the environment.
• Your users are part of the learning loop.
• Your data is the reward signal.

Founders who master this will own the infrastructure of the next economy.

⸻

Conclusion
The companies that win won’t be the ones that automate the fastest.
They’ll be the ones that teach machines the best.

The future economy is an RL machine.
The question is: are you going to be an agent inside it — or the architect who builds it?

Why You Can’t Hedge Impermanent Loss with Futures

Mikhail Liublin — Fri, 03 Oct 2025 00:54:18 +0000

DeFi is full of seductive numbers.
50% APR. 100% APR. Sometimes more.
It looks like free money.

A common trick is simple:
Provide liquidity in a pool.
Then hedge exposure with futures.
Lock in the yield.

At first glance, it feels risk-free.
But this is a trap.

Impermanent loss is not a straight line.
It is convex.
The further the price moves from your entry, the more you lose relative to holding the assets.

Futures are linear.
They are built to hedge direction, not curvature.
You can short or long to cancel out spot exposure.
But the shape of impermanent loss is different.

Example:
• You LP into ETH/USDC.
• ETH price doubles.
• The pool automatically rebalances. You now hold less ETH and more USDC.
• Compared to just holding ETH, you are worse off.
• Your short futures hedge cancels the ETH price increase.
But the rebalance curve keeps draining PnL.

You win nothing. In many cases, you lose.

Digging Deeper: Futures vs Options
The reason is mathematical:
• Futures = linear payoff.
• Impermanent loss = quadratic-like payoff (curved).
• To hedge a curve, you need convexity.

This is where options come in.
Options give nonlinear payoffs.
With the right strikes, they can replicate the impermanent loss profile.
That means they can actually cover the tail risk.

But here is the painful truth:
Options are not free.
Time decay (theta) eats your returns every day.
And often, the theta cost is higher than the APR you try to capture.
So the “hedge” becomes more expensive than the yield itself.

⸻

If you want to play in liquidity pools, understand the real risk.
• Futures cannot protect you from impermanent loss.
• Only options can, but the cost usually destroys the APR.
• High yields advertised by pools are not “risk-free.” They are a payment for taking convex risk.

**
**
Treat liquidity provision like an option seller.
You are selling volatility in exchange for yield.
Sometimes it pays. Sometimes it blows up.

If you want stability, don’t chase the flashy APR.
If you want exposure, know that it’s a volatility bet, not a farming trick.

The simple strategy is usually the one that fails in real markets.
Impermanent loss cannot be hedged with futures.
Options can work, but they cost more than the yield.

In DeFi, nothing is free.
If the APR looks too good to be true, it is.

⸻

👉 What do you think? Should liquidity providers start thinking more like volatility traders?

Robotics + AI + Crypto: The Next Tech Frontier

Mikhail Liublin — Wed, 10 Sep 2025 17:22:25 +0000

Robotics is moving fast — and it’s not just about factories anymore. In the last few months, we’ve seen humanoids, autonomous vehicles, and AI-driven automation push robotics into mainstream life. Here’s a quick overview for devs and builders.

1. Recent Robotics Breakthroughs

Humanoid robots:
Tesla’s Optimus, Agility Robotics’ Digit, and 1X’s NEO are all pushing toward affordable, general-purpose robots.
NEO now uses Redwood AI to sort laundry, navigate homes, and follow voice commands — no pre-programmed routines.

Healthcare robots:
Johns Hopkins developed a surgical robot that completed a gallbladder removal autonomously, with zero human intervention.

Delivery robots & drones:
Waymo operates 2,000+ driverless taxis.

Coco Robotics has done 500k+ food deliveries.

Zipline drones are delivering prescriptions worldwide.

Robotics is no longer niche — it’s scaling into everyday services.

2. AI Is the New Robot Brain

*Modern robots are becoming AI-native:
*
Computer vision + tactile sensors → Amazon’s ViTA-Zero cut warehouse errors by 80%.

LLM-powered control → Robots understand commands like “Pick up the drill and place it on the shelf.”

Imitation learning → Boston Dynamics trains Atlas by humans demonstrating tasks, not coding them.

These advances mean robots can adapt to messy real-world conditions.

3. Why Crypto Matters for Robotics

As robots scale, blockchain can handle trust, coordination, and payments:

Robot DAOs – fleets of robots owned collectively via tokens.

Tokenized data – robots securely sell sensor data to developers.

Machine-to-machine payments – a drone pays a charging station directly, no humans involved.

Projects like Robonomics Network are already testing this.

4. Startup Ideas You Can Build Today

You don’t need to build hardware. Here are software-first ideas:

*Robot DAO management platform *→ help communities fund and run robot fleets.

AI Brain-as-a-Service → plug advanced vision or language models into existing robots.

Robotics data marketplace → token-incentivized platform for buying/selling robot video data.

Natural language robot interface → “Alexa for robots” that works across brands.

These can be built lean, using open-source robotics frameworks and cloud AI APIs.

The Big Picture

Robotics is at an inflection point. AI gives robots intelligence, while crypto provides coordination and economics. Together, they could create a world where robots don’t just work for us — they transact, govern themselves, and even become part of the economy.

For devs, this is the perfect time to experiment. The APIs, models, and tools are out there — you just need to connect the dots.

The Physical Internet Is Coming

Mikhail Liublin — Fri, 08 Aug 2025 03:56:58 +0000

The internet ate the world of bits. Now it's coming for atoms.

Breaking Out of the Lab

Humanoid robots are breaking out of the lab and onto the factory floor. The founders building them speak with quiet inevitability. The tech still trips, stumbles, and drops things—but the consensus is clear: these are temporary bugs, not permanent blockers. The breakthroughs are close enough to smell.

The transformation feels inevitable because the economics are relentless. We're witnessing the early stages of what could be the most significant industrial revolution since the assembly line.

From Proof-of-Concept to Physical Infrastructure

In 2025, only ~150 humanoid robots are in active U.S. pilots—Tesla in factories, Amazon's Digit, BMW's Figure-01, Mercedes-Benz's Apptronik. But the pattern is familiar:

20 robots in a pilot
→ 200 robots in one production line
→ thousands across multiple facilities

By 2030, our internal model projects ~820,000 humanoids deployed globally. That's not niche—it's industrial scale.

Why? The economics are relentless. Human labor is the world's largest "market" at

The Physical Turing Test

When ChatGPT landed, it passed the cultural Turing Test—good enough at language that millions changed their workflows overnight. Robotics awaits its equivalent: the Physical Turing Test.

Picture a robot entering your post-party kitchen, cleaning everything without you lifting a finger, and doing it so well you can't tell if a human or machine did it. Today, we're maybe 10x too slow and 10x too clumsy. Closing that gap means building generalist policies—robotic brains that can adapt to new environments without a full rewrite.

The moment a humanoid nails the Physical Turing Test, adoption will spike. Costs will collapse. And the revolution will accelerate beyond all predictions.

The Data Gap

Robots learn slower than software. They train on millions of examples, not trillions. Sensors wear out. Humans won't tolerate trial-and-error in their living rooms.

Two ways forward:

Simulation
Billions of safe, cheap, accelerated trials. The bottleneck is the "sim-to-real" gap—physics and perception never match perfectly. Even the most sophisticated simulators struggle with the chaos of real-world environments.

Real-World Data
Human teleoperation, motion-capture crowdsourcing, and multimodal sensor fusion. Harder to scale, but it grounds models in reality. This is where the rubber meets the road—literally.

The winning teams will combine both—like Waymo's mix of simulated and real-world miles. The companies that crack this data problem first will have an insurmountable advantage.

Enter Crypto: Coordination for Atoms

Where does crypto fit into this robotic revolution?

Robotics is a data-scarce, capital-intensive, coordination problem. Crypto is an incentive and governance engine—perfectly positioned to solve the coordination challenges that traditional systems can't handle.

Data Markets

"Clean-to-earn" or "drive-to-earn" robots that reward operators for generating high-quality data. Imagine getting paid for every successful robotic task you demonstrate, with tokens flowing to the most valuable training examples.

On-Chain Governance

Open registries of robotic behaviors and safety rules—auditable, forkable, enforceable via code. No more black-box algorithms controlling physical systems. Everything becomes transparent and democratically governed.

Machine-to-Machine Payments

Robots paying each other for electricity, sensor access, or skills, enforced by smart contracts. A robot running low on battery could automatically negotiate with nearby charging stations. A cleaning robot could purchase navigation data from delivery robots that know the building layout.

The first prototypes exist—Frodobots' tokenized teleop games, Reborn's crowdsourced motion capture, PrismaX's decentralized control layer, OpenMind's trust fabric for autonomous teams.

The Network Effects Begin

The real magic happens when these systems start talking to each other. Just as the internet's value exploded when networks connected to other networks, the Physical Internet will emerge when robots begin sharing data, capabilities, and coordination.

Imagine:

Fleet Learning: Every robot's experience immediately benefits every other robot
Dynamic Task Allocation: Robots automatically redistributing work based on real-time capabilities and demand
Emergent Behaviors: Complex coordination arising from simple rules, like flocking birds or swarming bees

The Inflection Point

This feels like EVs in 2013: expensive, awkward, still niche—but with a clear path to scale. The infrastructure is being built. The talent is assembling. The capital is flowing.

Tesla went from 22,000 deliveries in 2013 to 1.8 million in 2023. The robotics curve could be even steeper because:

Software Leverage: Once solved, robotic behaviors can be copied instantly
Manufacturing Scale: Robotics companies don't need to build charging networks
Economic Pressure: Labor shortages create immediate demand

Rootics: The Operating System of the Physical World

The moment a humanoid nails the Physical Turing Test, adoption will spike. Costs will collapse. And the Physical Internet—robots moving through the real world with the same inevitability as packets over TCP/IP—will arrive faster than the consensus expects.

Bits rewired media, finance, and communication. Atoms are next.

The robotics stack—will be the operating system of the physical world. It will coordinate billions of physical agents, optimize resource allocation in real-time, and enable new forms of human-machine collaboration that we can barely imagine today.

The infrastructure is being laid. The protocols are being defined. The future is being built, one robot at a time.

What's Next?

The Physical Internet isn't science fiction—it's engineering. The pieces are coming together, and the acceleration is about to begin.

The question isn't whether this future will arrive, but who will build it and how quickly they can scale. The companies and communities that understand this transition will have the opportunity to shape the physical world for generations to come.

The internet ate software. Now it's coming for everything else.

Are you building in the robotics space? What coordination challenges do you see that crypto could solve? Share your thoughts on how the Physical Internet might reshape our world.

Remote Hiring In The Deepfake Era

Mikhail Liublin — Fri, 18 Jul 2025 23:54:10 +0000

Remote hiring has revolutionized global access to talent—but it’s also opened the door to sophisticated fraud. Here’s how one interview shattered our assumptions and changed our approach.

The Interview That Raised Alarms

We were hiring a developer through LinkedIn—standard process in 2025. Applications poured in, especially from Eastern Europe. On paper: impressive résumés, solid GitHub profiles, fluent English. Everything looked right.

But during interviews, the reality didn’t match the claims.

Candidates who identified as Eastern European showed clear signs of being from entirely different regions—mostly Asian descent, struggling with English, and no grasp of local geography or culture. Diversity isn’t the issue here; deception is.

Deepfake in a Dev Interview?

One candidate claimed to live in a city I knew well. Casual questions about the area revealed zero local knowledge. Then came the real shock: the candidate was using real-time face-swapping software to appear Eastern European on camera. The effect was subtle but noticeable—slight glitches, uncanny expressions, and delayed eye tracking.

We weren’t just dealing with résumé inflation. This was full-blown identity fabrication.

The Tech Behind the Scam

These weren’t isolated cases. Here’s what we’re now seeing:
• Face-swapping during live interviews using AI filters
• Profile farming: networks of fake LinkedIn/GitHub profiles with stolen identities
• Team-based deception: multiple “candidates” sharing playbooks and resources

The tools are public, cheap, and scarily effective.

Why This Matters

The risks are serious:
• Verification failure: Traditional checks can’t catch this
• Compliance issues: Hiring someone under a false identity creates legal and contractual vulnerabilities
• Security threats: A fake hire could become an insider risk
• Trust erosion: Honest global candidates face unfair scrutiny

How We Adapted Our Process

To combat this, we’ve overhauled our hiring pipeline:

✅ Enhanced Verification
• Multi-round interviews with different team members
• Ask deep questions about claimed locations and work history
• Verify education through official channels
• Use multiple video platforms to expose filters

🛡️ Technical Defenses
• High-res video calls to reduce masking effectiveness
• Watch for glitches, mismatched lighting, unnatural facial movements
• Add phone calls to verify voice consistency
• Ask candidates to screen share while coding

🌍 Cultural & Linguistic Checks
• Discuss local events, business norms, and day-to-day life
• Conduct technical Q&A in the claimed native language (where relevant)
• Engage native speakers to assist with evaluation

The Human Cost

The tragedy is that honest candidates—especially from fraud-prone regions—now face unjust suspicion. The actions of a few are making it harder for the many.

Moving Forward

Remote work is here to stay. But so is deception. Our choices now will define whether we remain open to global talent or retreat behind geographic firewalls.

Let’s do better:
• Train hiring teams to detect fraud without bias
• Share emerging fraud patterns across communities
• Build trust, but verify—intelligently and ethically

⸻

Have you seen similar fraud in your hiring process? How are you adapting? Let’s compare notes below.

Why GenAI Is Built for People, Not Corporations

Mikhail Liublin — Mon, 30 Jun 2025 02:58:07 +0000

For most of modern history, tech value flowed uphill. Big breakthroughs served big players — governments, multinationals, Wall Street. But GenAI flipped the value curve.

Now, the most powerful users of cutting-edge AI aren’t enterprises. They’re people — solo devs, indie hackers, creators, and two-person startups with no budget and no legal department.

🧠 GenAI changes who wins

Before GenAI:

Innovation	Early Winners
Electricity	Utilities & Factories
Internet	ISPs & Big Tech
Cloud computing	SaaS Giants

With GenAI:

Tool	Early Winners
GPT / Claude / Mistral	Indie builders, devs, writers
Open-source small models	Hackers, solopreneurs
Agent frameworks (AutoGen)	2-person startups

Generative AI doesn’t require a team, office, or VC funding to build with. All you need is an API key, curiosity, and some caffeine.

🚀 Individuals 10x. Corporations get compliance forms.

A solo builder gets superpowers. A big company gets procurement meetings.

Task	Solo Builder	Corporation
Launch AI tool	Weekend hack + Substack post	6-month roadmap + GRC checklist
Add chatbot	Drop API key + Zapier	Legal review + 3rd-party vendor
Automate workflows	GPT + Airtable + Make	ERP integration hell
Train staff	Just use ChatGPT	Custom LMS contracts

McKinsey reports only 1% of large enterprises consider themselves “AI mature.” Why? Bureaucracy, brand risk, internal resistance.

Meanwhile, solo builders just ship.

🧰 The GenAI-native tech stack is indie-first

Legacy enterprise stacks:

SAP
Salesforce
Manual workflows
“Digital transformation” projects

GenAI-native stack:

GPT-4o, Claude, Mixtral
LangChain / OpenDevin
Pinecone / Chroma
Vercel / Railway
Notion + Make + Zapier
Copilot or Replit Ghostwriter

This isn’t just cheaper — it’s faster, composable, and can be run by one person on a laptop.

🧑‍💼 One person = one department

AI lets one individual replace entire business functions.

Department	Replaced by
Customer support	GPT chatbot + vector search
Marketing	ChatGPT, Midjourney, Meta Ads
Legal	Claude + Legalese interpreter
QA	Code agents + self-healing test scripts
Sales	Voice AI + email agents
HR	Onboarding chatbots + payroll automations

A solo dev is no longer a lone wolf — they’re leading a team of AI agents working 24/7.

📈 Real-world proof: Tiny teams, big outcomes

Midjourney → ~$200M revenue, ~100 employees
Perplexity AI → 38 employees, 500M+ users
Dozens of AI-native startups launching with <10 staff and raising millions

Even Sam Altman predicts the rise of one-person unicorns. Carta shows over 35% of funded startups in 2024 had just one founder.

The edge isn’t capital anymore — it’s velocity + leverage.

🧨 Why corporations are falling behind

Company Size	Productivity Gain from GenAI
Solo Builder	10x–20x
Small Team	3x–5x
Enterprise	1.1x–1.3x (if adopted well)

Big companies are held back by:

Legacy systems
Siloed data
Legal & compliance risk
Risk-averse culture
IT bottlenecks

The same scale that once gave them power now creates drag.

🔮 What happens next?

Year	Prediction
2025	Agent-native micro-SaaS hits $10M ARR with 2-person team
2026	First one-person unicorn becomes reality
2027	Corporations start acquiring AI-native startups just to stay competitive

💡 What should you do?

You don’t need permission. You need a prompt, a keyboard, and a pain point you know intimately.

Start here:

Automate something you do every week
Build a niche tool powered by open models
Replace 1 task with an AI agent
Launch fast, iterate faster

The value curve is steep — but only at the start. This is your entry window.

🧭 Final thought

GenAI isn’t enterprise-first. It’s imagination-first.

It rewards clarity, speed, and experimentation.

That’s why the biggest winners won’t be companies. They’ll be people.

Jetpacks don’t work in committees.

About the author

Mikhail Liublin writes about AI-native creativity, future-of-work trends, and why the best tech stories today are being written by solo builders, not boardrooms.