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Season One Fall 2026 · Free

AI is taking over.
Now what?

A show about who we are when the machines get good — not the org chart, the person. The failures, the slop, and the very personal question of what is left.

The AI show for people still figuring it out.

Erkang Zheng
Erkang Zheng
Mystery guest
Jake Sorofman
Jake Sorofman

Two hosts, one open chair · Premieres fall 2026

The Programme

Three items · no filler

Separately, and not part of the above: a few companies bring us inside to do this work with them. That one is paid, hands-on and deliberately small — what it involves.

From the Hosts

Why we are doing this

One of us spent twenty years in cybersecurity, which is a long time to build a reputation on noticing things other people miss. A few months ago a phishing email got past him — not all the way; it was flagged inside the hour — but far enough that he had already forwarded it to finance with a note that said please process this. Twenty years, and it still got through. That is not a story about a tool failing him. It is a story about him.

The other of us made a living a different way, for thirty years: taking a room full of tangled opinions and conflicting data and turning it into three clear sentences nobody else in the room could quite produce. That was the job. Most days, that was the whole value of him. Then, on a flight with nothing better to do, he fed a model the same kind of brief he would normally bill for — out of something between curiosity and dread — and forty seconds later had a draft that was not his best work, but was close enough that a client would have been happy to get it. Thirty years of a specific skill, matched to about ninety percent, almost instantly, by anyone with a laptop and a connection.

That is the show. Not the one where a confident man in a vest explains from a stage that everything is about to change. We have both sat through that one. This is the other version: two people who built careers on a particular kind of competence, watching it stop being scarce, and working out in real time what that leaves them.

Here is the part that never makes the keynote. The machines are not coming for you — they are coming for the boring bits. The formatting. The status update nobody read. The fourth version of the same deck. Which sounds like good news, until you notice what is underneath it. If the thing you were actually proud of — the vigilance, the prose, the analysis, the taste — turns out to be the boring bit to a machine, then the question stops being how do I manage this and starts being what was I actually for.

So we are spending a season with people living it, not forecasting it. The team that deleted its ticketing system and did not die. The manager who discovered half her job was forwarding things. The people who fed a machine everything they had, got back gorgeous, confident slop, and had to work out on the Tuesday after not just what to do with it, but what it meant about them.

We will be wrong on tape. We are counting on it. Come along — it is a better story than the headlines suggest.

EZ & JS

One answer we keep arriving at

01 What should be done? Of everything possible, what matters, in what order, and what should we refuse to do at all?
02 Why does it matter? The intent, the context, the definition of success. What does “good” look like?
03 Who — or what — does it? Which work goes to which capable hands, human or machine, and with how much rope?
04 How do we know it’s good? Review, verify, measure, correct. Close the loop.

Early Word

On the book

Greater output of work is not the same as better performance.

Tim Sackett Tim Sackett Author, TA and HR expert

Provocative, practical, and extremely timely.

Dr. Aleksandr Yampolskiy Dr. Aleksandr Yampolskiy CEO & Co-Founder, SecurityScorecard

His message is infused with guidance — and, most importantly, hope.

Joel Szabat Joel Szabat Vice Chairman, Amtrak and Founder, International Leadership Foundation

I read the entire book yesterday on the flight. Incredibly good flow.

Joe Aurilia Joe Aurilia COO, Peak Projects Contributed to chapters 7 and 17, and advises this site

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Correspondence

Questions from the crew
What does “everyone is a manager now” actually mean?
Management was never about having direct reports. Strip away the org charts and the job is four questions: what should be done, why does it matter, who or what does it, and how do we know it is good. For two centuries most people never had to answer those, because most people commanded no resources beyond their own two hands. Then every knowledge worker acquired a workforce of agents. The questions came with it.
Is AI going to take my job?
It is coming for the boring part of your job — the drafting, the coding, the summarizing, the formatting. In two hundred years of automation, machines have never once taken the deciding: what to make, whether it matters, who does it, and what happens next. That part has a name we have been using all along, and it is management. The looms took the weaving. They never took the taste.
Why is producing more with AI not working?
Because volume was never your bottleneck — coordination was. And because everyone else got the same machine on the same Tuesday. When a whole market doubles its output into a fixed pool of attention, attention stops trading in volume at all and starts trading in trust and taste. Scarcity did not disappear. It moved.
What is Work, After AI?
Work, After AI is a show and a book from Erkang Zheng and Jake Sorofman about what happens to people when the machines get good at the part we were proud of. The podcast talks to operators who actually changed how their teams work, and the book “Everyone Is a Manager Now” makes the argument in 100 pages. Both are free. Separately, a small practice takes the same work inside companies that ask for it.

It always seems impossible until it’s done. Then it’s just what everyone does.

Everyone Is a Manager Now, chapter nineteen · Read it free