The Path of LLMs: Will Open Source Catch Up to Proprietary Giants?

A recent online discussion delved into the growing concern that Large Language Models (LLMs), which are poised to become foundational technology, are predominantly proprietary. This contrasts sharply with many other core tools and technologies in the software industry—like Linux, Git, or Postgres—that are open source and freely available. The original poster expressed unease about a future where developing software might involve paying per token to proprietary LLM providers and questioned why this isn't a more widely voiced concern.

Differing Perspectives on Proprietary LLMs

The discussion revealed a range of viewpoints on this issue. Several participants drew historical parallels, suggesting that the current proprietary nature of leading LLMs might be a temporary phase:

• Search Server Analogy: One commenter recalled that enterprise search servers were once proprietary, packaged hardware/software solutions before open-source alternatives like Elasticsearch became prevalent. They suggested LLMs might follow a similar trajectory, with commoditization eventually leading to strong open-source options. A "Red Hat model" (open-source core with paid enterprise support) was proposed for LLMs.
• CAD Software and Chess Engines: Another analogy was made to CAD software, where free options, though initially less capable, have improved. More pointedly, the evolution of chess engines was cited, with the hope that an open-source LLM might emerge to become a dominant force, akin to Stockfish in the chess world, rendering proprietary models less compelling.

Conversely, some argued that proprietary systems are already integral to the tech landscape:

• Existing Proprietary Backbones: It was pointed out that many widely used and critical technologies—such as Windows, Microsoft Office, Google's search engine and services (Gmail, Maps), AWS, and iOS—are proprietary. From this perspective, LLMs are simply following an established pattern.

The State and Future of Open-Source LLMs

The viability and progress of open-source LLMs were key discussion points:

• Capability and Cost: While acknowledging the existence of open-source alternatives (like DeepSeek or open-weight models such as Llama), some noted they are often not yet as capable as the leading proprietary models (GPT-series, Claude, Gemini) or are very expensive to run at scale. A significant challenge is that major proprietary LLM providers might currently be selling inference at a loss, making it difficult for others to compete on price.
• Active User Efforts: Despite these challenges, some individuals are committed to open-source, with one commenter detailing their efforts to build a home rig capable of running large open models, prioritizing local access even if performance is slower.
• Is the Gap Closing?: There's optimism that the open-source community is making progress. One argument was that earlier proprietary models like GPT-3 are now comparable or surpassed by smaller, more recent open-source models. The idea that proprietary LLMs will indefinitely maintain a growing lead was questioned.
• The "Good Enough" Point: A perspective offered was that open-source LLMs might eventually reach a "good enough" level of performance for many tasks. At that point, the massive investment required for marginal gains in proprietary models might slow down, similar to how OpenStreetMap coexists with and, for many uses, rivals commercial map providers.

Broader Implications

The conversation also touched upon the economic sustainability of current LLM business models, with questions raised about whether companies like OpenAI are currently profitable or burning through investment capital. Some users view LLMs primarily as productivity tools, like a "faster Stack Overflow," rather than direct replacements for human programmers, and their primary concern is the value delivered, regardless of whether the model is open or closed source.

Ultimately, the discussion highlighted a tension between the open-source ethos prevalent in software development and the current, capital-intensive reality of cutting-edge LLM development. While concerns about a proprietary-dominated future are valid, historical trends and ongoing open-source efforts offer hope for a more diverse and accessible LLM landscape.