This is Our Lot
Gary Greenberg | Super Chickens, Givers, and Collective intelligence | Factoids | Elsewhere
Our minds, as glorious as they are, cannot tell us the future, nor save us from it. The uncertainty we live in right now is only a reminder that this is our lot.
| Gary Greenberg
Super Chickens, Givers, and Collective intelligence
I’ve made the (start of) a transition from X.com (formerly Twitter) to Bluesky, and because of the service’s ‘starter packs’ I’ve encountered new sources and the resources they share. (I am @stoweboyd.bsky.social if you’d like to connect.)
The most recent example is an article dazzlingly entitled Super chickens, givers, and collective intelligence: the importance of collaboration, teamwork, and mentorship in science, which was the 2024 American Society for Clinical Investigation Presidential Address by Benjamin Humphreys. I had not heard of Humphreys, or his work, or even the association he was addressing. But the ‘collective intelligence’ line caught my eye, and I’m a sucker for super chickens.
The talk is a ramble through the thoughts of a lifelong researcher, circling the topic or the role of collaboration in research. I will only zoom into a few points, but there is a lot to find in there.
I don’t want you to scroll ahead, so I will sum up the super chickens part before I lose you.
William Muir was an evolutionary biologist studying productivity, and he decided to look into chickens, since one measure of their productivity — egg production — is easy to measure. So he bred super chickens by selecting the best layers, and breeding them together. And after inbreeding those for a few generations, he reintroduced those fowl back into a flock of normal chickens, and bred them together for six generations. Then, as Humphreys summarizes:
At the end of the experiment, the average chickens were healthy, had fluffy feathers, and were producing more eggs than when they started. For the super chickens, however, there were only three left, the rest having been pecked to death. These super chickens had only been high producers by suppressing the productivity of the other chickens around them.
And the rank-and-file chickens figured that out, and killed off the greedy, domineering, hypercompetitive super chickens.
Humphreys goes on:
This raises the point that simply bringing together highly driven overachievers can be counterproductive because of the negative effects of hypercompetitiveness on a group’s dynamic. By contrast, emphasizing collaboration over individual excellence can result in greater productivity.
Humphreys then cites the work of Anita Williams Woolley and colleagues on the importance of social sensitivity in group productivity. As I wrote a few years ago, she and her team found that a teams collective intelligence was influenced quite strongly by the makeup of the group:
The research of Anita Wooley, Thomas Malone, Christopher Chabris, and various other contributors in recent years has shed light on these dynamics. In 2010, they set about to find out if there is a group ‘collective intelligence’ that makes teams work more effectively on solving complex problems, and they found that social sensitivity was the key.
In a New York Times article by Wooley, Malone, and Chabris, they wrote about their initial surprise when IQ and other attributes diverged from group effectiveness:
We next tried to define what characteristics distinguished the smarter teams from the rest, and we were a bit surprised by the answers we got. We gave each volunteer an individual I.Q. test, but teams with higher average I.Q.s didn’t score much higher on our collective intelligence tasks than did teams with lower average I.Q.s. Nor did teams with more extroverted people, or teams whose members reported feeling more motivated to contribute to their group’s success.
The ethnological approach — investigating what the better teams actually did — yielded three major differences from the other teams:
There was more balance in the smartest teams’ discussions, with less domination of discussion by a few members.
Their members scored higher on a test called ‘Reading the Mind In the Eyes’. This is a measure of ‘face reading’, but in this case the ability to judge mental state by viewing images of eyes with the rest of the face not shown.
The teams with more women outperformed the teams with more men. Full stop. This is not a support for diversity, since the effect is linear based on the number of women. This is likely because women are better at face reading — and deeper at theory of mind — than men are.
So, if you’d like to increase the performance of teams, add women, or, to be more precise, add people who are socially sensitive.
Humphreys also threads the needle with Adam Grant’s model of givers, takers, and matchers: givers always support others when asked, takers take and give nothing in return, and matchers help, but expect the favor to be returned, or they will cut off giving. He researched his three types over 30,000 people across professions and discovered these facts:
Takers are poor performers, and they poison the well. Even one in a group can sour the culture.
Matchers can become poor performers if takers are around, because they will dedicate themselves with getting even with them. (They will peck the super chickens to death, if they can.)
Givers are both the best and the worst. As Humphreys puts it:
[Givers] represent both the worst performers and the best performers in a bimodal distribution. Grant asks how we can foster organizations where more of the givers get to excel. He has three answers — first, protect givers from burnout. Second, promote a culture of generosity and help-seeking behavior, because this tends to encourage giving behavior. Finally, keep out the takers. In a group dynamic, even one taker has a disproportionately negative effect on the group, causing givers to shut down.
So it is takers that turn givers into the worst performers in a context where takers are allowed, and givers are the best performers in an environment that supports giving by excluding taking. Humphrey cites Margaret Heffernan’s characterization of this:
More successful groups show higher social connectedness to each other. What happens between people really matters and leads to social capital — the reliance and interdependency that builds trust. This is only built with time together, leading to openness and candor.
And all easily disrupted by bad actors.
See you on Bluesky.
Factoids
The impact of AI on ordinary workers is mostly negative.
Bryan Robinson reports on the findings of an Intel report making the rounds:
Despite 96% of C-suite executives expecting AI to boost productivity, the study reveals that, 77% of employees using AI say it has added to their workload and created challenges in achieving the expected productivity gains. Not only is AI increasing the workloads of full-time employees, it’s hampering productivity and contributing to employee burnout.
To add insult to injury, nearly half (47%) of employees using AI say they don’t know how to achieve the expected productivity gains their employers expect, and 40% feel their company is asking too much of them when it comes to AI.
The time and effort involved with dealing with AI bots adds overhead to workers efforts, accelerating productivity friction.
Will Lockett read the same report, and connects the dots with the failure of Amazon’s Just Walk Out grocery stores:
The idea was that facial-recognition cameras, shelf sensors, and AI would track what items a customer had taken, then charge their Amazon account once they left, negating any need for a cashier or self-checkout. This innovation was hailed as one of the first cases of AI directly replacing human workers and a way to lower the cost of operating a store. But, in reality, it wasn’t. A recent report found that over a thousand remote workers had to be hired to monitor the video feeds and verify 70% of customer purchases, as the AI consistently got it wrong. This amount of labour isn’t cheap, even if it is cheaply outsourced overseas, and Amazon’s Just Walk Out AI became significantly more expensive than simply hiring regular cashier staff. As such, Amazon has struggled to sell the system to third parties and has had to switch its own grocery stores to a fancy non-AI self-scan system instead.
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VCs aren’t making much money.
Average VC returns have been subpar. 50%+ of VCs return <1x distributed to paid-in capital (DPI). 👀 Average VCs represent high costs for poor / non-existent returns for investors. Most LPs would be better off in the S&P 500.
It’s a sucker’s game, apparently.
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Ireland looks wealthy in comparison to other European nations.
Fintan O’Toole points out ‘people don’t vote for statistics’ but he’s not talking about Harris’s loss to Trump. He’s referring to Irish voters:
Think of an American multinational — Apple, Pfizer, Meta, Microsoft, Intel, Boston Scientific — and the chances are that it is one of the thousand or so U.S. companies with an Irish operation. These companies spend more than $40 billion a year in a country with a population of just over five million. To put that in context, the stock of U.S. foreign direct investment in Ireland in 2022 stood at $574 billion, roughly triple the total in China and India put together.
But globalization has led to the same ills besetting other Western nations, like the U.S.:
Ireland’s public services and infrastructure significantly lag its warp-speed economy, creating a place that feels somehow both overdeveloped and underdeveloped. And on its own, the globalized market economy does not produce the public goods necessary for a decent quality of life. Ireland may be awash with money, but its young people can no longer afford to buy homes; sky-high rents are making it impossible for many to live in the main cities of Dublin, Cork and Galway; homelessness and child poverty have risen; access to health care is uneven and uncertain; public transport and public schools are often overcrowded; physical infrastructure is seriously inadequate; and the pace of transition to a carbon-free economy has been painfully slow.
The paradox of inequality.
Elsewhere
The Management Singularity
One of our great contributors, Henry Farrell, takes on the monster issue of AI’s impact on management. I haven’t fully read it, but expect some more detailed response later this week.
In 1965 - a little under 60 years ago - Herbert Simon1 assembled a few essays about AI into a little book, The Shape of Automation for Men and Management. It has three extremely useful lessons.
The first is how easy it is for people to get the future wrong, even when they are ridiculously brilliant. Simon was convinced that computers - 1960s computers! - could already “read, think, learn, create,” and that they would be able to do this at human level or better in just a few years. In his words: “duplicating the problem-solving and information-handling capabilities of the brain is not far off; it would be surprising if it were not accomplished in the next decade.” Obviously, that didn’t happen.2
The... Gargantuan mistakes.) So the cartoon computer serves as a mirror of our hopes and anxieties. We hope for a world free from poverty and excessive toil. We worry lest our role in society be abolished by social change. We prize our human uniqueness, and are anxious for the safety of our human freedoms.
Is there a single position in the AI debate today that is not aptly summarized by some popular cartoon of the early 1960s? Techno-optimism; fears about displaced labor; doomerism; algorithmic incompetence. They’re all mentioned somewhere in that paragraph. Even while the technologies change, the anxieties stay constant.
The third is the big one, and it’s suggested in the title. When we think about AI, we focus on its consequences for “men”3 but we should think about what it means for management too. Dreams and anxieties about the purportedly approaching Singularity are, for the most part, dreams and anxieties about the consequences of automation, and there are two big views out there.
Nobel laureate in Economic Sciences, and recipient of the Turing Award.
Farrell’s footnote: ‘Still Simon’s imagined future would make a great premise for an alternative reality novel - what would the world look like in 2024, if The Man in the Gray Flannel Suit had been super-empowered by technology?’
Farrell’s footnote: ‘Simon’s book was composed in the 1960s, and in the earliest bit of the 1960s before the sexual revolution really got going. You should absolutely replace “Men” with your own preferred gender or non-gender specific term as seems good to you.’
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The manicure economy
Jon Hilsenrath takes a different tack, looking into the increasing place in many sectors for highly face-to-face job categories, or what could be called intimate jobs:
Technology and globalisation have driven decades of job upheaval in developed economies, akin to the decimation of work for skilled shoe cobblers and handloom weavers during the industrial era. While that was happening, the economy created millions of service jobs for people with social skills. The rise of automated intelligence now threatens to shuffle the deck all over again.
Job loss [since 2000] did indeed turn out to be vast in many areas, perhaps even worse than the dismal scientists expected. Roughly half of all occupations that existed in the data set in 2000 experienced job loss over the subsequent two decades, with work for switchboard operators, filing clerks, transcriptionists, machine setters and many others collapsing.
Work demands an even more human touch as machines take jobs.
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