Francois Chollet is leaving Google
A prominent Google AI engineer behind the Keras deep learning library is leaving the company to start a new venture, prompting debate over the future of TensorFlow, the rise of PyTorch and JAX, and how widely Keras is still used in production. Commenters revisit Google’s 2018 decision to fold Keras into TensorFlow, contrast big-tech bureaucracy with startup opportunities, and explore the engineer’s other major project, the ARC-AGI benchmark, including whether recent gains on it meaningfully advance progress toward general intelligence or merely “game” the test with synthetic data and large language models.
Departure and career plans
- The Keras creator is leaving Google to start a new company with a friend; no move to another major lab.
- They remain US‑based for now, but are positive about the AI scene in Paris.
- Some see Google’s blog farewell as unusual and possibly a “soft launch” for the new venture.
Keras, TensorFlow, PyTorch, JAX
- Many recall early Keras (on Theano/TF1) as transformative: easy, Pythonic, and critical to deep learning’s “takeoff,” especially vs Theano, Caffe, Torch7.
- Common criticism: the abstraction was “too easy for basics, too hard for custom work” (custom losses, RNN variants, bespoke training loops), pushing researchers toward raw TensorFlow and then PyTorch.
- PyTorch is widely seen as the current default: better flexibility, LLM tooling, multi‑GPU support, performance, ecosystem, and community momentum.
- JAX is praised as powerful yet under‑appreciated; there are claims Google uses it heavily internally and that TensorFlow is losing ground.
Multi‑backend Keras and production use
- Several see the 2018–2019 folding of Keras into TensorFlow as the moment Keras “died,” and believe it accelerated PyTorch adoption.
- The Keras author clarifies they did not decide that merger; it was a higher‑level TensorFlow leadership decision and, in hindsight, likely a mistake.
- Keras is now standalone and multi‑backend again (TF, JAX, PyTorch), with explicit emphasis on backward compatibility and “progressive disclosure of complexity.”
- Users report Keras still running reliably in production (often since ~2018–2019) for vision and recommendation workloads; others report heavy technical debt and migration to PyTorch.
- The author lists many large companies using Keras; skeptics counter that this reflects legacy rather than current research leadership.
ARC‑AGI benchmark and AI progress
- Extensive debate around the ARC benchmark and a recent $1M prize:
- Some describe strong results (e.g., systems using GPT‑4‑generated synthetic tasks) as “gaming” a benchmark that was supposed to resist brute‑force and big‑data memorization.
- Others argue this is legitimate progress in problem‑solving and test‑time fine‑tuning, not a hack.
- Concerns are raised that human baselines measured via Mechanical Turk underestimate motivated human performance.
- The organizer plans ARC 2 with tasks that are harder to brute‑force yet similar human difficulty, and sees ARC as a high‑leverage path toward AGI‑relevant research.
- They expect ARC to be solved within a few years, see that solution as a stepping stone (not AGI itself), and maintain skepticism about an “intelligence explosion,” citing diminishing returns and the need to separate intelligence from autonomy.
Google culture, hierarchy, and AI startups
- Multiple commenters portray Google as comfortable but bureaucratic, where higher‑level decisions (e.g., around TensorFlow/Keras) can override project creators and dampen ambition.
- Others defend large hierarchies as necessary for 100k+‑employee firms and note that senior engineering levels typically have broad systems experience.
- There is broader discussion of AI startups: some claim top researchers can easily raise ~$100M; others argue that’s insufficient to sustain a competitive foundation‑model company, so future startups will likely focus on specialization and post‑training rather than training new general LLMs from scratch.