jsulz

I write. A lot. If practice made perfect, my writing would be pristine, but it’s a daily struggle. Be more direct, to the point, less fluff.

By knowing how to parse and convey the right information, a good writer can relieve and prevent a slew of headaches. They can herd disjointed efforts, give direction to projects, sell ideas, solidify shared purpose, document processes, and foster knowledge sharing.

Writing is challenging because it’s an attempt to cohere the incoherent. You’re never sure the words are good enough. Does it convey the point you’re trying to make? Is it grounded in truth?

I’m incredibly fortunate to work at a place like Hugging Face where around every virtual corner (hidden inside a maze of Slack channels, open source repositories, blog posts, and research papers) there is a brilliant mind waiting. It leaves me with a sense of awe and a healthy amount of imposter syndrome.

The science team in particular is always cooking up something that pushes the boundaries of what a team 10x their size is capable of. Thomas Wolf, the Chief Science Officer at Hugging Face, recently wrote a response to Dario Amodei’s Machines of Loving Grace, that once again showed me the outsized impact this small but mighty team has in the world of AI. The take is sweet and to the point and in direct contrast to much of the AGI hype in the industry.

I shared a controversial take the other day at an event and I decided to write it down in a longer format: I’m afraid AI won’t give us a “compressed 21st century”.

The Baader–Meinhof phenomenon is a cognitive bias that takes place after the introduction of a new concept wherein suddenly this concept appears everywhere you turn. I’ve been experiencing this with this essay as of late - seeing it crop up in pieces like Dario Amodei’s Machines of Loving Grace.

I’ve been re-reading it; trying to distill the core elements that capture my attention. The opening is a summation of the key point:

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.

Even today, most companies have difficulty finding people who know how to develop products and also understand AI, and I expect this shortage to grow.

This is especially true as modern machine learning and artificial intelligence algorithms become more complex. Decision trees are easy to understand. Reinforcement learning is intuitive. Deep learning architectures, on the other hand are obtuse.

Increasingly, I also expect strong product managers to be able to build prototypes for themselves. The demand for good AI Product Managers will be huge. In addition to growing AI Product Management as a discipline, perhaps some engineers will also end up doing more product management work.

This is reality. AI in its myriad of forms is here to stay. More and more, I’m thinking of a product team as less of one where each team member plays a specific, defined role and more of one where everyone does similar work but fills slightly different gaps. There are no product managers. There are no engineers. There is only delivering value to people anyway you can.

Wouldn’t that be wonderful?