This thought provoking article from Ahmed on the current state of AI’s onslaught on the job market.

This is where I keep coming back to a phrase that has been rattling around my brain for the past month: out of distribution humans.

Most work lives in the fat middle of a bell curve. Tasks repeat with small variations. Most graduate schemes are built around that fact. You take reasonably bright people, give them a handbook and a mentor, and let them climb a well mapped gradient. Shared service centres, call centres, warehouses, junior consulting rotations, entry level software roles, even a lot of legal and accounting work, all sit in that comfortable hunk of the curve where yesterday’s data is a very good guide to tomorrow’s tasks.

Models feast on that part of the curve. That is what they are trained on: logs, emails, historical cases, recordings of someone else doing the job, code repositories, scanned documents. If your work looks a lot like a large pile of past episodes, it is a short hop from playing them back to imitating them. The central question for future labour markets is not whether you are clever or diligent in some absolute sense. It is whether what you do is ordinary enough for a model to learn or strange enough to fall through the gaps.

An out of distribution human, in my head, is someone whose job sits far enough in the tail of that curve that it does not currently compress into training data. Maybe they work with genuinely novel problems. Maybe they operate at small scales or in messy physical situations where we do not yet have enough sensors. Maybe they have taste that is not easily reduced to click logs. They are not safe; nothing is. They are simply late on the automation curve. The system needs them until it can watch them for long enough and in enough detail that it can flatten what they do into data.

This reminds me of Zara Zhang’s observation.

Swiss cheese always had holes in it. The holes from which Jerry would emerge. But some time back Swiss cheese started losing holes. This video by Tim Scott explains why and how they managed to get it back.

The bacteria that are responsible for making these holes produce propionate, acetate and carbon dioxide, and this carbon dioxide is produced in the cheese and aggregates all around impurities. The more the bacterium produces this carbon dioxide, it accumulates and builds these holes. These impurities capture the carbon dioxide, and then a bubble forms and grows.

They found that the milk was too clean, so we didn’t have any dust in it, and this was because we had closed milking systems, so the dust could not get into the milk. Everything improved the last decade, so the milking process is hermetically closed now. And then, in former times, in the barn, you had always this hay dust everywhere, and it came also into the milk. We tried different particles to put into the milk to see if the holes are growing again. Hay powder is the best one, and we really could see that the whole formation was dependent on the concentration of the hay powder.

Somehow this reminds me of a dialogue from Tron: Legacy.

The thing about perfection is that it is unknowable, it’s impossible, but its also right in front of us, all the time

John Steele highlighting the irony of how farming sea food strips the sea itself.

In the cold waters of the Pacific, the anchoveta once shimmered in swarms so vast that sailors described them as turning the sea into a river of quicksilver. They were small, unassuming fish, yet the abundance of the ocean rested upon their delicate bones. Seabirds wheeled overhead in their millions, sea lions and whales dove into their depths, and predatory fish rose through the blue to feed on them. In those shoals lived the vitality of the sea itself. But in our age, the anchoveta, along with sardines and menhaden, have been transformed from living threads in an ancient web into bags of meal and casks of oil. Ninety percent of the forage fish caught by human hands are not eaten by us but ground down to feed salmon being raised in the cold fjords of Norway and shrimp and fish in the tropical ponds of Southeast Asia.

It is one of the great ironies of our time. To farm the sea, we strip the sea. We take from the ocean’s foundation to build its surface anew, and in the process we imperil both.

But all is not lost. There are some innovative solutions in the horizon.

This comment by th explains how DEI is essentially an outreach program. This is on the news of Python Software Foundations’ decision to withdraw from $1.5 million proposal for US government grant program.

It seems like a number of the “DEI is anti-merit discrimination” messages in this thread are overlooking how DEI work usually works.

A relevant tweet from 2016 (https://x.com/jessicamckellar/status/737299461563502595):

> Hello from your @PyCon Diversity Chair. % PyCon talks by women: (2011: 1%), (2012: 7%), (2013: 15%), (2014/15: 33%), (2016: 40%). #pycon2016

Increased diversity in communities usually comes from active outreach work. PyCon’s talk selection process starts blinded.

If 300 people submit talks and 294 are men, then 98% of talks will likely be from men.

If 500 people submit talks and 394 are men, then ~79% will likely be by men.

Outreach to encourage folks to apply/join/run/etc. can make a big difference in the makeup of applicants and the makeup of the end results. Bucking the trend even during just one year can start a snowball effect that moves the needle further in future years.

The world doesn’t run on merit. Who you know, whether you’ve been invited in to the club, and whether you feel you belong all affect where you end up. So unusually homogenous communities (which feel hard for outsiders to break into) can arise even without deliberate discrimination.

Organizations like the PSF could choose to say “let’s avoid outreach work and simply accept the status quo forever”, but I would much rather see the Python community become more diverse and welcoming over time.