Hackers, AI, and the Battle Between Open-Source and Proprietary Systems

You know, back in the ‘80s, hackers weren’t these weird hooded guys with masks or the villains of James Bond movies who thought they were invincible! Hackers were people who tried to make things easier. Some even connect them to the famous Gordian Knot story—a legend that told of a complex knot that, if untangled, would grant its solver rule over all of Asia. Enter Alexander the Great, who hacked through it with a sword, fulfilling the prophecy in an unexpected way.

Hackers weren’t just breaking systems—they were choosing the best tools to get the job done. The best ones didn’t just brute-force their way through problems; they found the smartest, most efficient way forward. That mindset isn’t just about hacking—it’s how leaders should approach technology today.

Take Apple. Before Steve Jobs became the sole decision-maker, Apple was deeply tied to hacker culture, with Wozniak leading the charge. They built their early success on tinkering, sharing knowledge, and making things work in unconventional ways. But as Apple grew, it became a closed ecosystem where decisions were made for you. That shift—from open experimentation to controlled, polished systems—is the same trade-off we’re seeing in AI right now.

Different places, different systems, different rules. The best innovators don’t pick a side—they learn them all.

DeepSeek: The Bishop Entering the AI Game

Now, there’s another paradigm breaker entering the AI space—DeepSeek. What makes DeepSeek interesting isn’t just that it’s another AI model, but that it represents a major shift in how AI is structured.

AI is becoming just another tool in business, and leaders are going to have to make choices. Just like how teams decide whether to go all-in on Microsoft’s ecosystem, stick to lightweight tools, or just manage everything in spreadsheets, companies will have to decide whether to build on open-source AI like DeepSeek or stay locked into proprietary models like OpenAI and Gemini.

This decision isn’t just theoretical. Enterprise AI is only going to get bigger, and companies that don’t understand the trade-offs now are going to feel them later—whether it’s in pricing, flexibility, or control over their own data.

And of course, open-source vs. proprietary AI isn’t a simple good vs. bad debate.

Open-Source AI vs. Proprietary AI: The Trade-offs

DeepSeek is pushing open-source AI, and that has both major advantages and critical weaknesses.

Choosing between open-source and proprietary AI isn’t just a tech decision—it’s a strategy choice that defines innovation and security

🔹 Open-Source AI (DeepSeek, LLaMA, Mistral, etc.)

✅ Cheaper & More Accessible – Startups, developers, and researchers can experiment freely.

✅ Faster Innovation – The community iterates and improves the model constantly.

✅ Transparency & Flexibility – Users can tweak the model to fit their needs… unless you're asking DeepSeek about a certain square somewhere. Then it might suddenly forget how to process text.

❌ Quality Control Issues – No single entity ensures its reliability across all cases.

❌ Monetization Challenges – Sustainable funding for open AI is still a big question mark.

❌ Security & Ethical Risks – A completely open model can be used in unintended or harmful ways.

🔹 Proprietary AI (OpenAI, Google Gemini, Anthropic, etc.)

✅ Higher-Quality Outputs – Centralized optimization leads to more refined models.

✅ Safety & Ethical Standards – Stricter controls to prevent misuse.

✅ Sustainable Business Model – Reliable revenue means more long-term stability.

❌ Expensive & Restricted – Paywalls limit who can access the best tools.

❌ Opaque Black-Box Systems – Users don’t know exactly how decisions are made.

❌ Slower Innovation Cycle – One company controls all the development.

Strategically, leaders need to understand these trade-offs—whether they’re building AI tools, integrating AI into their business, or just trying to navigate the shifting landscape.

DeepSeek and the Future of AI Strategy

For proprietary AI companies, DeepSeek’s rise means they need to adapt or risk losing ground. Just like how Microsoft had to embrace open-source with GitHub, we might see more proprietary players adjusting their models to keep up.

Like Prometheus, open-source AI shares knowledge with the world—disrupting centralized power in the process.

For business leaders and tech decision-makers, the key question is when to leverage open-source vs. proprietary AI.

• If you need cost-effective, adaptable AI, open-source is probably your best bet.

• If you need reliability, security, and consistency, proprietary models might still have the edge.

I personally still use OpenAI’s models, but I’m watching closely to see how China’s "Middle Kingdom bishop" (DeepSeek) makes its next move. Centralized power has its benefits—just look at how Meituan successfully centralized entire industries under one app in China while the West struggled with individualistic business models. DeepSeek's approach could force a major shift in AI accessibility the same way.

At the same time, I’m skeptical about DeepSeek’s approach to free speech, but that’s also a cultural and regulatory difference that shapes how AI develops globally. Still, if DeepSeek can keep costs low while improving quality, it’s going to force a major shift in AI accessibility.

At the end of the day, competition fuels innovation. Whether it’s OpenAI, DeepSeek, or another emerging player, the rivalry between open-source and proprietary AI is what will keep pushing technology forward. As Mises put it, the market always adjusts. Companies that fight change too hard eventually fall behind—the ones that adapt are the ones that win.