Mozilla has a close relationship with Google, as most of Firefox’s revenue comes from the agreement keeping Google as the browser’s default search engine. However, the search giant is now officially a monopoly, and a future court decision could have an unprecedented impact on Mozilla’s ability to keep things “business as usual.”

United States District Judge Amit Mehta found Google guilty of building a monopolistic position in web search. The Mountain View corporation spent billions of dollars becoming the leading search provider for computing platforms and web browsers on PC and mobile devices.

Most of the $21 billion spent went to Apple in exchange for setting Google as the default search engine on iPhone, iPad, and Mac systems. The judge will now need to decide on a penalty for the company’s actions, including the potential of forcing Google to stop payments to its search “partners completely,” which could have dire consequences for smaller companies like Mozilla.

Its most recent financials show Mozilla gets $510 million out of its $593 million in total revenue from its Google partnership. This precarious financial position is a side effect of its deal with Alphabet, which made Google the search engine default for newer Firefox installations.

The open-source web browser has experienced a steady market share decline over the past few years. Meanwhile, Mozilla management was paid millions to develop a new “vision” of a theoretical future with AI chatbots. Mozilla Corporation, the wholly owned subsidiary of Mozilla Foundation managing Firefox development, could find itself in a severe struggle for revenue if Google’s money suddenly dried up.

  • @brucethemoose
    link
    English
    2
    edit-2
    1 month ago

    Mozilla management was paid millions to develop a new “vision” of a theoretical future with AI chatbots

    Is this llamafile?

    The thing about LLMs is that no one knows how to write the ultra low level optimizations/runtimes, so they port others (llamafile largely borrows from llama.cpp AFAIK, albeit with some major contributions from their own devs).

    Performance is insanely critical because they’re so darn hard to run, and new models/features come out weekly which no sane dev can keep up with without massive critical mass (like HF Transformers, mainly, with llama.cpp barely keeping up with some major jank).

    So… I’m not sure what Mozilla is thinking here. They don’t have many of those kind of devs, they don’t have a GPU farm, they’re not even contributing to promising webassembly projects like mlc-llm. They’re just one of a bazillion companies that was ordered to get into AI with no real purpose or advantage. And while Gemma 2B may be the “first” model that’s kinda OK on average PCs, we’re still a long way away from easy mass local deployment.

    Anyway, what I’m getting at is that I’m a local LLM tinkerer, and I’ve never touched or even looked at anything from Mozilla. The community would have if anything of theirs was super useful.

    • @[email protected]
      link
      fedilink
      English
      11 month ago

      From what I’ve heard the general idea is to run AI search on your browsing history, which is a very useful feature. I’m not deep into AI tech at all but to me it looks like that would involve local finetuning, ingesting all that history during inference sounds like a bad idea. It also wouldn’t be necessary to generate stuff, only answer “Can you find that article about how nature makes blue feathers” and it’s going to spit out previously-read links that match that kind of thing. Also, tl;dr-bot it.

      Oh and there’s already AI, as in ML, in firefox, in the form of machine translation. Language detection seems to be built-in, translating requires downloading a model per language pair, 16M parameters. Trained on workstations with 8GPUs. Which is all to say: You don’t need gigantic GPU farms if you aren’t training gazillion parameter models on the whole internet.

      • @brucethemoose
        link
        English
        11 month ago

        It shoudn’t be finetuning, if anything it should be RAG with an embeddings model + regular inference.

        This is kinda cool, but it still doesn’t seem to justify bogging down a machine with a huge LLM. And I am speaking as a massive local LLM enthusiast who uses them every day.