日本語：生成AI規制の脅威とプロジェクトへの影響
Español：La Amenaza de Regular la Inteligencia Artificial — y lo que eso podría significar para bitBuyer

That bill… is it aimed right at bitBuyer?

In March 2025, the U.S. Congress began deliberations on a sweeping regulatory bill targeting generative AI. The list of offenses is long—and dramatic enough to grab headlines: election deepfakes that mimic political candidates’ voices, AI models trained on novels and illustrations without permission, and voice scams that impersonate real people with chilling accuracy.

Honestly? These issues are serious. I understand the societal need for regulation. I really do.

But still.

For projects like ours—bitBuyer, which views generative AI as a tool to break through the limitations of open-source development—the wind is beginning to shift in a way that feels… unsettling.

Training Data Regulations That Shake the Foundation of AI-Assisted Development

At every step of bitBuyer 0.8.1.a’s development, we’ve embraced generative AI—not just for coding assistance, but for architecture reviews, refining specifications, writing documentation, and even translating our GitHub Wiki. It’s how a solo developer manages the workload of an entire team.

But the new legislation in the U.S. proposes something quite concerning: clauses suggesting that AI models trained on datasets containing copyrighted material must first obtain permission from the rights holders.

If that logic spreads globally, tools like ChatGPT may face usage restrictions or be forced to “forget” entire chunks of their training data.

Now imagine this: the articles and code snippets we’ve voluntarily fed to ChatGPT—saying, “Here, use this as a reference”—could soon become legal landmines. If copyright checks begin to target even user-provided context, our collaborative development with AI may be pushed into a corner, restrained by fear and hesitation.

“Is bitBuyer’s Learning Model Safe?”—A Fair Question.

bitBuyer 0.8.1.a is designed as a fully online machine learning application. Each node continues to learn from live trading data in real time—and shares its learnings with the broader ecosystem through federated learning.

In other words, learning is the point.

That’s why the current wave of AI regulation—especially around “unauthorized training” and “transparency obligations for pre-trained models”—raises a few quiet but persistent concerns. Even if not explicitly targeted, the bitBuyer architecture might not remain untouched.

Worse, if regulatory schemes start shifting toward “pre-registration” or whitelist models, even locally-trained AI used by indie OSS developers could fall under scrutiny. For a project like ours—built on the idea of using AI to generate financial value that supports other open-source software—this would feel less like a guardrail and more like a moat being built around us.

Finance Meets AI: Where bitBuyer Starts Looking Risky

One more area to watch closely: AI regulation in the financial sector.

In the U.S., lawmakers are now facing the growing threats of deepfake fraud and AI-driven manipulation of credit scores. As a result, banks and securities firms using AI are being asked to ensure explainability, anti-discrimination, and bias auditing.

bitBuyer 0.8.1.a, of course, is currently framed as a personal tool—an aid for individuals to make trading decisions on their own.

But imagine this: if the tool becomes widely adopted and thousands of users start automating trades with the same algorithm, it might look—at least from the outside—like a decentralized AI making collective investment decisions.

And that… might just put bitBuyer within the range of financial AI regulations. Not today. But someday.

The Image-Generation AI Dilemma: Not a Regulatory Threat—Yet

Before we wrap up, here’s one last note—perhaps lower on the urgency scale, but still worth mentioning.

On the bitBuyer Project’s official site (yes, this one), we often attach eye-catching visuals or explanatory images to blog posts. Tools like DALL·E and Stable Diffusion have been immensely helpful in producing those.

But if future regulations start requiring explicit license disclosures for the datasets behind those images—or begin enforcing copyright similarity checks—that ease of use could vanish.

And let’s be honest: for small OSS teams (read: solo developers running on caffeine and goodwill), any additional step in publishing an article—verifying image sources, rewriting prompts, correcting flagged outputs—can be a quiet but persistent drag.

We just want to keep enjoying the current “lightweight” experience while it lasts.

Regulation Is Necessary. But Crushing Innovation Isn’t.

Yes, we need rules. Generative AI is already being used for fraud, impersonation, and political manipulation. Left unchecked, it risks undermining the very foundations of society.

But at the same time, we must not allow these regulations to snuff out the creative synergy now happening between individual developers and AI.
Instead of top-down mandates that restrict experimentation, we need frameworks that offer room for responsible independence—regulation with design space.

bitBuyer envisions a future where open-source software generates its own revenue and supports other OSS projects in return. It would be a shame to see such an ecosystem excluded from the legal playing field—not because of malice, but because the rules were written with only big players in mind.

Let’s hope that AI regulation becomes not a tightening noose—but a shared rulebook for peaceful coexistence.

Next: “The Cyberization Has Already Begun: A New Form of Intelligence Blending Generative AI and Humans”