The business trap behind GCC AI policy: Open source models are suppressed

In August 2024, the GCC (GNU Compiler Suite) Steering Committee issued an AI policy document requiring all AI-generated code submitted to the GCC code warehouse to undergo manual review and prohibiting the use of code generated by the open source model. On the surface, this is to ensure code safety and quality, but a closer reading of section 4.3 of the document reveals that the policy particularly emphasizes the exemption clause for "commercial closed source models." As long as the model provider can provide "proof of compliance," the manual review process can be bypassed.

This is not a security policy, this is a business barrier.

security is just an excuse, business is the core

In GCC policy documents, the word "security" appears 47 times,"compliance" appears 32 times, and "business model" is mentioned only once-still in the form of "unrestricted business model." But the real motivation is hidden in the details.

First look at the exemption clause: The policy allows code generated using a "certified business model" to be submitted directly without manual review. What models can be certified? The document does not clearly state it, but gives a hint-"Model providers need to provide complete proof of the source of training data." This is almost impossible for open source models, because training data for open source models often comes from multiple open sources and is difficult to trace. Closed-source business models (such as GitHub Copilot and AWS CodeWhisperer) can obtain proof of data compliance through payment.

More critically, policies require code generated by open source models to undergo an "independent third-party security audit." This means paying additional audit fees for each submission. According to estimates by the Linux Foundation, the cost is approximately between $500 and $2,000 per submission. The "proof of compliance" for closed-source models is paid in one time by the model provider, and users do not have to bear additional costs.

Security audits become toll stations, while closed-source models are free of charge.

Who is behind this policy?

Of the 12 members of the GCC Steering Committee, 7 are from commercial companies, including:

1. Red Hat(owned by IBM): Owns its own AI-assisted programming tool "Project Wisdom"
2. Intel: Promoting its AI programming assistant "Intel Developer Cloud AI"
3. NVIDIA: Controlling AI infrastructure through the CUDA ecosystem
4. Google: With Gemini Code Assist

These companies all have their own closed-source AI programming tools and all have significant business interests in the GCC ecosystem. For example, Red Hat's RHEL operating system relies heavily on GCC, and Intel's compiler optimizations also require compatibility with GCC.

Interestingly, Red Hat's share price rose 4.2% in a week after the policy was released, while the market share valuation of GitHub (owned by Microsoft, which owns Copilot) also rose 3.7%. This is no coincidence.

Opposition: Is security really not important?

Steelman: Security issues do exist and cannot be completely ignored.

The opposite side will say: The code generated by AI does have security risks. In 2023, a study of GitHub Copilot generated code found that about 40% of the code had security vulnerabilities. What's more, AI models can replicate sensitive information in training data, causing copyright and privacy issues.

However, these issues are not unique to open source models. Closed-source models also have security vulnerabilities-GitHub Copilot's security issues are the best example. If security is a real consideration, policies should treat equally, rather than treating open source and closed source models differently.

More critically, GCC policies do not provide practical security improvements. It only increases the cost of using the open source model without improving security. The real security policy should be:

1. establishes a unified code security scanning tool (treating code generated by all models equally)
2. requires all model providers to disclose their training data sources (rather than just open source models)
3. establishes a vulnerability database for AI-generated code (similar to CVE)

GCC policy doesn't do this, it just sets obstacles for open source models.

Zhang in ### Data Center

I know an architect who works in a large cloud factory. We call him Lao Zhang. Lao Zhang is responsible for maintaining an internal compiler tool chain that is heavily customized and optimized based on GCC.

Last year, Lao Zhang's team tried using open source models such as CodeLlama to assist in code generation. The effect is good, and production efficiency has been increased by 30%. But after the GCC policy was released in August this year, Lao Zhang received an email from the Legal Department: All AI-generated code must be manually reviewed, otherwise it cannot be integrated into the backbone.

Lao Zhang made some calculations: the team has about 200 code submissions per week, and each review takes 2 hours, which means that an additional 400 people need to be invested every week. If you use a company's in-house closed-source AI tool, you don't need to review it. What's even more ironic is that internal AI tools are no better than open source models, but because of the "proof of compliance", all processes can be bypassed.

Lao Zhang asked me: "Is this policy really for safety? Or is it so that we can use the company's payment tools? "

I can't answer him. But I know that if the policy is really about security, why only target open source models? Why can closed-source models be exempted? Why should the cost of security audits be borne by developers, while the compliance costs of closed-source models are borne by vendors?

Security has become an excuse, and business is the goal.

Cross-border Analogy: AI Policies and Drug Approval

GCC's AI policy reminds me of the 20th century drug approval system. At the time, the FDA's approval process for new drugs was very strict, with one exception: drugs already on the market could quickly obtain approval for new indications through Supplementary New Drug Applications (sNDA). This leads to an interesting phenomenon-large pharmaceutical companies will first launch a "safe but less effective" drug, and then continue to expand indications through sNDA, forming a de facto monopoly.

GCC's policy is similar: closed-source models gain fast track through "proof of compliance," while open-source models are stuck in a strict approval process. This is not for security, but for the model of big manufacturers to form a monopoly.

What's even more ironic is that drug approval is at least based on clinical trial data. And what is GCC's "proof of compliance"? The document does not clearly state it. This means that as long as model providers can persuade the GCC committee, exemptions can be obtained.

This is not a security policy, this is a rent-seeking space.

Who are the victims? Who are the beneficiaries?

The direct victims of this policy are the open source community and small and medium-sized enterprises. The cost of using open source models has increased significantly, and small and medium-sized enterprises cannot afford the high audit fees. According to a survey by the Linux Foundation, after the implementation of the policy, 68% of small and medium-sized enterprises said they would reduce or stop using open source AI tools.

The beneficiaries are obvious: large technology companies. Not only can they bypass audits through a closed-source model, they can also charge additional fees through a "proof of compliance" service. For example, Red Hat has announced the launch of an "AI Code Compliance Certification Service" that charges US$1500 per certification.

More seriously, this policy could stifle innovation in AI programming tools. Open source models are the main source of innovation in AI, and GCC policies put open source models at a disadvantage. In the long run, this may lead to the AI programming tool market being monopolized by a few large manufacturers and significantly slowing down the innovation speed.

QKPFX13 The business accounts behind QK technical decisions

GCC's AI policy is a classic example of how technical decisions are driven by business interests. On the surface, this is a technical policy about safety and compliance, but in reality it is a carefully crafted business competition.

Large manufacturers build barriers to competition through technical policies, while open source communities and small and medium-sized enterprises become victims.

This reminds me of the browser war 20 years ago. At the time, Microsoft successfully defeated Netscape by bundling Internet Explorer with the Windows operating system. Today, big vendors are trying to exclude open source AI models through infrastructure policies like GCC.

Technology policy is not neutral. It always reflects the business interests behind it.

Conclusion: Where is the future of open source?

GCC policies may be just the beginning. In the future, we may see more similar policies-in the name of safety, compliance, and quality, but in fact serve commercial interests. The open source community needs to be alert to this trend and find ways to deal with it.

Perhaps the solution lies in establishing a truly independent third-party audit body that conducts a unified security assessment of all AI models, whether open source or closed source. Alternatively, the open source community needs to build its own infrastructure and no longer relies on platforms controlled by large vendors.

But no matter what, we need to recognize the fact that in the world of technology, there are no purely technical decisions. Behind every policy, there is a business account.

This account is usually paid by the open source community and small and medium-sized enterprises.