Jan 2, 2019 · 26m · a16z

a16z Podcast | Automation, Jobs, & the Future of Work (and Income)

Tom Davenport · 8m spoken Sonal Chokshi · 8m spoken Julia Kirby · 8m spoken
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

In this episode of the a16z podcast, authors Tom Davenport and Julia Kirby explore how artificial intelligence is transforming knowledge work and job roles. They advocate for human-machine augmentation rather than replacement, proposing actionable career strategies, critiquing Universal Basic Income, and outlining how corporate management must evolve.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. The host holds 33.6% of the talking time here. How this is scored →

The host as informed peer 4.0 Guest teaching 3.2 Guest disagreement 1.8 The host pushing back 2.0
05100:0010:0020:002:10–6:18 · The host as informed peer 3/10 Knowledge Work and Job Automation Fears The host sets up the context surrounding job automation fears and previous podcast discussions on US labor realities. The guests reframe the host's inquiry about finding a 'higher ground' by explaining that automation impacts tasks rather than entire jobs, advocating instead for finding 'common ground'.6:18–14:03 · The host as informed peer 4/10 Actionable Strategies for Human Augmentation The host demonstrates knowledge of tech history by connecting the guests' ideas to Doug Engelbart's vision of augmented intelligence. The guests provide an extensive breakdown of five specific career strategies for working alongside AI.14:03–17:31 · The host as informed peer 5/10 Machine Creativity vs. Human Intuition and Craft The host pushes back against a rigid boundary between human and machine capabilities by introducing real-world examples of machine learning drug discovery, AI art exhibitions, and information networks in finance. The guests acknowledge these advances but argue that machine creativity remains far behind human craft and humor.17:31–23:18 · The host as informed peer 4/10 Universal Basic Income vs. Guaranteed Work The host brings up universal basic income in the context of job loss and offers wage insurance as an alternative framing. Julia Kirby forcefully rejects the core assumption connecting automation to widespread joblessness, insisting that work is vital to human dignity and identity.23:18–26:29 · The host as informed peer 4/10 The Evolution of Management Science and Corporate Strategy The host asks how management science will evolve, proposing a shift toward emotional intelligence over pure efficiency metrics. The guests agree with the host's premise and elaborate on how corporate strategy will pivot from engineering workflows to psychological engagement.2:10–6:18 · Guest teaching 4/10 Knowledge Work and Job Automation Fears The host sets up the context surrounding job automation fears and previous podcast discussions on US labor realities. The guests reframe the host's inquiry about finding a 'higher ground' by explaining that automation impacts tasks rather than entire jobs, advocating instead for finding 'common ground'.6:18–14:03 · Guest teaching 3/10 Actionable Strategies for Human Augmentation The host demonstrates knowledge of tech history by connecting the guests' ideas to Doug Engelbart's vision of augmented intelligence. The guests provide an extensive breakdown of five specific career strategies for working alongside AI.14:03–17:31 · Guest teaching 3/10 Machine Creativity vs. Human Intuition and Craft The host pushes back against a rigid boundary between human and machine capabilities by introducing real-world examples of machine learning drug discovery, AI art exhibitions, and information networks in finance. The guests acknowledge these advances but argue that machine creativity remains far behind human craft and humor.17:31–23:18 · Guest teaching 4/10 Universal Basic Income vs. Guaranteed Work The host brings up universal basic income in the context of job loss and offers wage insurance as an alternative framing. Julia Kirby forcefully rejects the core assumption connecting automation to widespread joblessness, insisting that work is vital to human dignity and identity.23:18–26:29 · Guest teaching 2/10 The Evolution of Management Science and Corporate Strategy The host asks how management science will evolve, proposing a shift toward emotional intelligence over pure efficiency metrics. The guests agree with the host's premise and elaborate on how corporate strategy will pivot from engineering workflows to psychological engagement.2:10–6:18 · Guest disagreement 1/10 Knowledge Work and Job Automation Fears The host sets up the context surrounding job automation fears and previous podcast discussions on US labor realities. The guests reframe the host's inquiry about finding a 'higher ground' by explaining that automation impacts tasks rather than entire jobs, advocating instead for finding 'common ground'.6:18–14:03 · Guest disagreement 1/10 Actionable Strategies for Human Augmentation The host demonstrates knowledge of tech history by connecting the guests' ideas to Doug Engelbart's vision of augmented intelligence. The guests provide an extensive breakdown of five specific career strategies for working alongside AI.14:03–17:31 · Guest disagreement 2/10 Machine Creativity vs. Human Intuition and Craft The host pushes back against a rigid boundary between human and machine capabilities by introducing real-world examples of machine learning drug discovery, AI art exhibitions, and information networks in finance. The guests acknowledge these advances but argue that machine creativity remains far behind human craft and humor.17:31–23:18 · Guest disagreement 4/10 Universal Basic Income vs. Guaranteed Work The host brings up universal basic income in the context of job loss and offers wage insurance as an alternative framing. Julia Kirby forcefully rejects the core assumption connecting automation to widespread joblessness, insisting that work is vital to human dignity and identity.23:18–26:29 · Guest disagreement 1/10 The Evolution of Management Science and Corporate Strategy The host asks how management science will evolve, proposing a shift toward emotional intelligence over pure efficiency metrics. The guests agree with the host's premise and elaborate on how corporate strategy will pivot from engineering workflows to psychological engagement.2:10–6:18 · The host pushing back 1/10 Knowledge Work and Job Automation Fears The host sets up the context surrounding job automation fears and previous podcast discussions on US labor realities. The guests reframe the host's inquiry about finding a 'higher ground' by explaining that automation impacts tasks rather than entire jobs, advocating instead for finding 'common ground'.6:18–14:03 · The host pushing back 1/10 Actionable Strategies for Human Augmentation The host demonstrates knowledge of tech history by connecting the guests' ideas to Doug Engelbart's vision of augmented intelligence. The guests provide an extensive breakdown of five specific career strategies for working alongside AI.14:03–17:31 · The host pushing back 4/10 Machine Creativity vs. Human Intuition and Craft The host pushes back against a rigid boundary between human and machine capabilities by introducing real-world examples of machine learning drug discovery, AI art exhibitions, and information networks in finance. The guests acknowledge these advances but argue that machine creativity remains far behind human craft and humor.17:31–23:18 · The host pushing back 3/10 Universal Basic Income vs. Guaranteed Work The host brings up universal basic income in the context of job loss and offers wage insurance as an alternative framing. Julia Kirby forcefully rejects the core assumption connecting automation to widespread joblessness, insisting that work is vital to human dignity and identity.23:18–26:29 · The host pushing back 1/10 The Evolution of Management Science and Corporate Strategy The host asks how management science will evolve, proposing a shift toward emotional intelligence over pure efficiency metrics. The guests agree with the host's premise and elaborate on how corporate strategy will pivot from engineering workflows to psychological engagement.

speaking balance: gold is the host, purple is the guest (3 minute bins)

0:00 · the host 64.3% · guest 35.7%0:00 · the host 64.3% · guest 35.7%3:00 · the host 18% · guest 82%3:00 · the host 18% · guest 82%6:00 · the host 37% · guest 63%6:00 · the host 37% · guest 63%9:00 · the host 0% · guest 100%9:00 · the host 0% · guest 100%12:00 · the host 32.2% · guest 67.8%12:00 · the host 32.2% · guest 67.8%15:00 · the host 57.4% · guest 42.6%15:00 · the host 57.4% · guest 42.6%18:00 · the host 15.2% · guest 84.8%18:00 · the host 15.2% · guest 84.8%21:00 · the host 47.3% · guest 52.7%21:00 · the host 47.3% · guest 52.7%24:00 · the host 31.2% · guest 68.8%24:00 · the host 31.2% · guest 68.8%
Sharpest disagreement ▶ 18:21 Direct rejection of the UBI premise

Julia Kirby flatly rejects the premise linking automation to universal basic income, labeling the narrative as unnecessary, depressing, and harmful to human identity.

Hardest push from the host ▶ 14:03 Host challenges assumptions on machine creativity

The host refuses to accept the neat separation between human creativity and machine logic, bringing up machine learning art and drug discovery as evidence of emerging AI creativity.

Biggest teaching moment ▶ 18:37 Reframing work as vital to human dignity

Julia Kirby re-educates the host on the psychological necessity of work, explaining that compensating people without employment ignores the fundamental role structured work plays in human self-worth.

The host holds their own ▶ 6:18 Host grounds context in Doug Engelbart's vision

The host demonstrates strong subject matter expertise by connecting the guest's thesis to Doug Engelbart's foundational concepts of intelligence augmentation and modern mobile hardware.

the scores for every segment, with the reasoning behind each
ChapterTopicThe host as informed peerGuest teachingGuest disagreementThe host pushing backWhy
Knowledge Work and Job Automation Fears 3411 The host sets up the context surrounding job automation fears and previous podcast discussions on US labor realities. The guests reframe the host's inquiry about finding a 'higher ground' by explaining that automation impacts tasks rather than entire jobs, advocating instead for finding 'common ground'.
Actionable Strategies for Human Augmentation 4311 The host demonstrates knowledge of tech history by connecting the guests' ideas to Doug Engelbart's vision of augmented intelligence. The guests provide an extensive breakdown of five specific career strategies for working alongside AI.
Machine Creativity vs. Human Intuition and Craft 5324 The host pushes back against a rigid boundary between human and machine capabilities by introducing real-world examples of machine learning drug discovery, AI art exhibitions, and information networks in finance. The guests acknowledge these advances but argue that machine creativity remains far behind human craft and humor.
Universal Basic Income vs. Guaranteed Work 4443 The host brings up universal basic income in the context of job loss and offers wage insurance as an alternative framing. Julia Kirby forcefully rejects the core assumption connecting automation to widespread joblessness, insisting that work is vital to human dignity and identity.
The Evolution of Management Science and Corporate Strategy 4211 The host asks how management science will evolve, proposing a shift toward emotional intelligence over pure efficiency metrics. The guests agree with the host's premise and elaborate on how corporate strategy will pivot from engineering workflows to psychological engagement.

Statements from this episode (13)

Assertion Not checkable as stated
Davenport: AI interest and corporate adoption hit an all-time high
“As you suggest, we've gone through various cycles in this space, and this is probably the most spring-like spring we've ever had in the sense of interest in the technology, the number of firms that are adopting it.”
Tom Davenport Jan 2, 2019 ▶ 0:54
Prediction Not checkable as stated
Davenport: Augmentation is the most likely path for AI in work
“I think that augmentation-oriented future we think is by far the most likely one for how AI travels through the occupational world.”
Tom Davenport Jan 2, 2019 ▶ 5:02
Assertion Not checkable as stated
Kirby: Workplace computers eliminate specific tasks, not entire jobs
“When computers move into a workplace, they never take anybody's entire job, that what they do is they take away certain tasks”
Julia Kirby Jan 2, 2019 ▶ 5:22
Prediction Not checkable as stated
Kirby: Smart machines will encroach upon every knowledge work job
“So the reality is that every knowledge work job is going to see this encroachment of smart machines into the workplace.”
Julia Kirby Jan 2, 2019 ▶ 5:57
Prediction Not checkable as stated
Davenport: Almost every tech vendor will add cognitive AI capabilities
“You know, big companies like IBM are hiring thousands of people to do this, and I think there's every reason to expect that as these technologies take hold, almost every vendor will have some level of cognitive capabilities, and a lot of people will need to be…”
Tom Davenport Jan 2, 2019 ▶ 10:33
Insight
Kirby: Focusing on newly discovered niche domains insulates workers from automation
“For instance, in scientific inquiry, you know, you're always looking for the thing that hasn't yet been discovered, and you're kind of going into narrower and narrower and narrower niches, and that is a very viable strategy for human work, because only after t…”
Julia Kirby Jan 2, 2019 ▶ 13:41
Prediction Not checkable as stated
Tom Davenport: Humans will prefer human-created art and humor over AI
“I think at some point there will probably be just a human preference for human created art and humor and so on, just because it's human.”
Tom Davenport Jan 2, 2019 ▶ 15:43
Prediction Not checkable as stated
Davenport: AI will struggle to read human psychological cues like bluffing
“The whole thing about looking in your opponent's eyes and figuring out whether they're bluffing or not. I think that's going to be tough for machine to do for a while.”
Tom Davenport Jan 2, 2019 ▶ 17:19
Opinion
Kirby: Linking basic income to automation is unnecessary and damaging
“So I would say, first of all, it's kind of an unnecessary link and it's sort of a damaging way of thinking about things.”
Julia Kirby Jan 2, 2019 ▶ 18:30
Opinion
Davenport: Society should offer guaranteed work rather than basic income
“Because of this belief in the importance of work for, you know, meaning and life satisfaction, we'd argue for guaranteed work, which would be compensated rather than guaranteed income alone.”
Tom Davenport Jan 2, 2019 ▶ 21:01
Assertion Contradicted
Davenport: Basic income experiment recipients mostly spend extra time watching TV
“But in the experiments thus far, it appears that instead of doing, you know, highly meaningful activities, people just watch more TV.”
Tom Davenport Jan 2, 2019 ▶ 21:29
Prediction Not checkable as stated
Davenport: Automation will bring outsourced corporate work back in-house
“Well, we believe that organizations will continue to Exist and in fact may bring more of their work back in house than they have had over the past couple of decades that automation may take back a fair amount of the work that was distributed through outsourcin…”
Tom Davenport Jan 2, 2019 ▶ 23:40
Insight
Davenport: Pure corporate automation triggers a race to the bottom in margins
“Pursuing automation tends to be kind of a race to the bottom in many ways. It sort of lowers everybody's cost, but it also lowers everybody's margins, and everybody ends up doing kind of similar, not so innovative things”
Tom Davenport Jan 2, 2019 ▶ 24:11
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