This podcast touches on AI.
asserted
podcast → touch → AI
My fiancé works at Anthropic.
asserted
fiancé → work → Anthropic
Three months ago, we began our podcast miniseries by asking how likely artificial intelligence is to take large numbers of jobs.
asserted
intelligence → begin → jobs
Our first guest, Box CEO Aaron Levie, argued that mass disruption would be highly unlikely.
asserted
disruption → argue → ?
And several subsequent guests, from Amazon Web Services CEO Matt Garman to labor economist Kathryn Ann Edwards, expressed similar skepticism about a labor wipeout.
asserted
guests → express → wipeout
The argument has significant emotional appeal — who doesn’t want to believe that technology will create more jobs than it eliminates?
asserted
it → have → jobs
And yet I try to apply extra skepticism whenever anyone tells me what I want to hear.
asserted
I → try → what
Which is why my ears perked up over the past 14 episodes when founders like Wabi’s Eugenia Kuyda told me that she was no longer hiring junior engineers, and Replit CEO Amjad Masad told me the company had begun to replace big enterprise software contracts with home-coded alternatives.
asserted
company → perk → alternatives
For our final episode, I wanted to speak with someone who has considered the problem from all the major perspectives we’ve covered here from the start: operator, software builder, and civic leader.
asserted
we → want → start
And that wish led me to Clara Shih.
asserted
wish → lead → Shih
Shih spent the past two decades building software for some of the world’s biggest tech firms.
asserted
Shih → spend → firms
After early stints at Google and Salesforce, she founded and ran Hearsay Systems for a decade before returning to Salesforce to run Service Cloud.
asserted
she → found → Cloud
In 2023 she was named CEO of Salesforce AI, where she led the launch of the company’s agent platform, Agentforce.
asserted
she → name → platform
The next year she moved to Meta to build and run its business AI group, making agents that now answer customer messages for businesses on WhatsApp, Messenger and Instagram.
asserted
that → move → WhatsApp
It was while working at Meta last fall that Shih saw something that altered the course of her career.
asserted
that → work → career
Thanks to the AI agents that the company had recently deployed, a product development process that once required user researchers, designers, product managers, and three kinds of engineers could be reduced into one or two people and a prototype.
uncertain
that → deploy → people
Shih noticed that the agents were making similar strides in marketing, distribution, and privacy review.
asserted
agents → notice → marketing
And so soon she started taking down entry-level job postings, she told me, because she no longer felt that she needed them.
asserted
she → start → them
There remain significant limits to what agents can do — see Katie Paul’s account in Reuters this week of how Mark Zuckerberg’s plan to cut as much as 60 percent of the company this year due to AI efficiencies was derailed by (among other things) underperforming agents.
asserted
plan → remain → agents
Still, Shih told me, the experience radicalized her.
asserted
experience → tell → her
This spring, she left Meta (though she remains a senior advisor) and started the New Work Foundation, a nonprofit, along with a consumer brand called Dear CC that delivers tools and advice to entry-level workers.
asserted
that → leave → workers
Everything the organization makes is free: a podcast in which hiring managers explain what they’re looking for, a data tool called Field Report that shows the AI exposure of different majors and occupations, and a new mentoring app called Game Plan that matches rejected job applicants with peers and a mentor to make the search faster and less lonely.
asserted
search → make → peers
Most of the builders I spoke to for this series told me that jobs would be fine — they would just be different.
asserted
they → speak → me
Shih is willing to say that the optimistic story she once told herself about her own products — that automating the rote work would free customer support workers for higher-order tasks — has "primarily not been true.
asserted
automating → say → tasks
In our conversation, she lays out a three-way taxonomy for how AI reshapes a job, borrowed from MIT economist David Autor; predicts that one in five corporate roles is "especially going to be challenged"; explains why she disagrees with her nonprofit advisor Andrew Yang about universal basic income; and offers a hot take about who will actually do the legal, marketing, and accounting work of the future.
asserted
who → lay → future
Here's our conversation, lightly edited for clarity and length.
asserted
conversation → edit → clarity
Thanks to everyone who listened to the Platformer podcast over the past quarter.
asserted
who → listen → quarter
With this edition, it comes to a close.
asserted
it → come → close
You've said that last fall, when you were still at Meta, you watched AI agents match and then beat some of your best people on real tasks, and that you felt “radicalized” in that moment when you saw it working.
asserted
it → say → moment
Can you take us into that room with you?
asserted
you → take → you
What was the task, and what did you see?
asserted
you → see → what
It feels like just yesterday.
asserted
It → feel → yesterday
It started off in our product design and product development.
asserted
It → start → design
We saw all those steps collapse into one or two people being able to ideate in a room, generate the prototype with vibe coding, test it with real users as well as simulated users, and then have a much leaner team of people build that into production.
asserted
team → see → production
Seeing is believing, and in that moment, I just imagined this amplifying across the economy.
asserted
this → believe → economy
What you're saying is that seemingly overnight, it was as if that entire stack could be handled by a person or two.
uncertain
stack → say → person
Where did your mind go from there?
asserted
mind → go → ?
What did you start to think this would mean, both for the company you were at and the broader economy?
asserted
you → start → company
Shih: Once you start seeing this pattern — and of course, at a place like Meta, you're under extreme pressure to deliver — you start thinking about how you can apply this to other areas, other bottlenecks, other business processes to help us go faster.
asserted
us → start → areas
Of course, you've got humans in the loop, experts reviewing the final output.
asserted
you → get → output
…and 126 more, not listed.
The Aporia
