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Fixie.ai CEO: AI Will Lead to ‘The End of Programming’

Matt Welsh, CEO of Fixie.ai, has made the bold prediction that AI will lead to "the end of programming."...
Fixie.ai CEO: AI Will Lead to ‘The End of Programming’
Written by Staff
  • Matt Welsh, CEO of Fixie.ai, has made the bold prediction that AI will lead to “the end of programming.”

    Many companies are working to improve AI systems to the point where they can tackle complex problems, such as computer programming. While progress has been made, there are still significant limitations. Despite that, Welsh believes the time is coming when AI systems will revolutionize the software industry.

    Writing in January’s Communications of the ACM, Welsh makes the case that AI will ultimately replace software altogether, in most situations at least:

    I believe the conventional idea of “writing a program” is headed for extinction, and indeed, for all but very specialized applications, most software, as we know it, will be replaced by AI systems that are trained rather than programmed. In situations where one needs a “simple” program (after all, not everything should require a model of hundreds of billions of parameters running on a cluster of GPUs), those programs will, themselves, be generated by an AI rather than coded by hand.

    Welsh believes the definition of software engineers will fundamentally change, with an emphasis on AI training models:

    So I am not just talking about things like Github’s CoPilot replacing programmers.1 I am talking about replacing the entire concept of writing programs with training models. In the future, CS students are not going to need to learn such mundane skills as how to add a node to a binary tree or code in C++. That kind of education will be antiquated, like teaching engineering students how to use a slide rule.

    The engineers of the future will, in a few keystrokes, fire up an instance of a four-quintillion-parameter model that already encodes the full extent of human knowledge (and then some), ready to be given any task required of the machine. The bulk of the intellectual work of getting the machine to do what one wants will be about coming up with the right examples, the right training data, and the right ways to evaluate the training process. Suitably powerful models capable of generalizing via few-shot learning will require only a few good examples of the task to be performed. Massive, human-curated datasets will no longer be necessary in most cases, and most people “training” an AI model will not be running gradient descent loops in PyTorch, or anything like it. They will be teaching by example, and the machine will do the rest.

    Welsh’s predictions are certainly among the most optimistic regarding AI’s future. Nonetheless, at the pace with which the technology is improving, his predictions are certainly not outside the realm of possibliity.

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