Episode 241 â July 2, 2026 â Available at read.fluxcollective.org/p/241
Contributors to this issue: Neel Mehta, Boris Smus, Erika Rice Scherpelz, Dart Lindsley, MK
Additional insights from: Ade Oshineye, Ben Mathes, Jasen Robillard, Jon Lebensold, Justin Quimby, Lisie Lillianfeld, Robinson Eaton, Spencer Pitman, Stefano Mazzocchi, Wesley Beary, and the rest of the FLUX Collective
Weâre a ragtag band of systems thinkers who have been dedicating our early mornings to finding new lenses to help you make sense of the complex world we live in. This newsletter is a collection of patterns weâve noticed in recent weeks.
âNot everything that is faced can be changed, but nothing can be changed until it is faced.â
â James Baldwin (1962)
đŻđ Point, donât steer
Given a task to build a treehouse, would you rather have dozens of pages of detailed schematics or a simple one pager outlining what someone should feel when they are in the treehouse?
Well, like many things, it depends. Clarity is about the goal (why this treehouse matters, what good looks like for this treehouse). Meanwhile, detail focuses on what to do and how to do it (how do we build this treehouse?). The trap for many is that detail often feels like it increases clarity, but often the opposite happens. When the intent is buried under a hundred specifics, people lose the plot. They build exactly what was described without understanding whether or not it solves the problem.
The military tradition of mission command tells officers the objective and the constraints and leaves the how to the people closest to the fighting. Its opposite, detailed command, scripts the steps from the top. Mission command wins more often not because soldiers are smarter than generals, but because they see the situation on the ground.
Detail is not always the enemy. Sometimes, a single centralized logic needs to be scaled to more than one personâs worth of execution. If an engineering system is having an outage, the incident commander needs to be able to quickly make the calls and have someone execute them. Thereâs no time to give high level goals and iterate for alignment. A startup three weeks from insolvency is in a similar situation. These situations tend to appear when the stakes are existential and time is scarce.
But when youâre not in those situations, detailed mode becomes a liability. Every decision routes through one person, so the people around them stop exercising judgment. Which means that now the leader has to stay involved, reducing judgment even more. Itâs a feedback loop that drops an organizationâs throughput to that of the bottleneck. And worse, the leader might not realize that theyâre the bottleneck. Instead, they blame it on the lack of autonomy of those around them.
Detail can be valuable, but we need to figure out when and how to present it. Interface design calls this progressive disclosure. First, set the clear goal. Reveal detail on demand, when someone hits a wall. A new hire will need a lot of it; tenured engineers, almost none. The detail arrives as an answer to a real question, not a preemptive script.
As we provide guidance, we can think âdoes this guidance provide clarity? or does it add detail?â And if it adds detail, âWas this detail requested?â We might find ourselves realizing that clarity is, in fact, the harder job.
(Credit to Matt Manela for inspiring this piece.)
đŁïžđ© Signposts
Clues that point to where our changing world might lead us.
đđ± Wealth managers are moving upmarket, leaving non-millionaires to AI
A senior partner at McKinsey observed that âmass-affluentâ clients, defined as those with liquid assets between $100,000 and $1 million, can now get âprivate-banking quality from AI,â meaning that financial advisors who focused on standardized advice for the mass market are now a lot less valuable. Instead, he predicted that wealth managers will shift to serving even richer clients, since those people often have emotional needs (such as which child will get the largest inheritance) that require a human touch. Banks such as Citigroup have actually stepped up hiring for wealth advisors, but the roles are shifting now that internal AI tools can do a lot of basic portfolio analysis. The McKinsey partner predicted that firms would need to start hiring for AI experts, âbehavioral data scientists, personalization architects, and human-in-the-loop oversight professionals,â all hybrid roles that didnât exist in wealth management just a few years ago.
đđ° Influencers promoting a prediction market âwonâ millions on a fake betting site
A duo of exposĂ©s from Politico and the Wall Street Journal found that the prediction market startup Polymarket paid influencers to post videos of themselves winning hundreds of thousands of dollars (or more) betting on things like what a politician would say in a speech. It turned out those bets had never actually been placed, and if youâd actually placed the hundred-plus bets featured in the ads, youâd have lost $166,000. It turned out that Polymarket devs had built a fake lookalike website, âpoiymarket.comâ (with an âiâ in place of the âlâ), for influencers to post these mock trades. Whatâs more, Polymarketâs CMO used his personal PayPal account to send over $2.5 million to political influencers, most of whom never disclosed that they were doing a paid partnership.
đđ» A dead-simple electric truck will sell for $25,000
The average new car in the USA sells for $50,000; even used cars sell for upwards of $25,000. But an EV startup called Slate has revealed a bare-bones electric pickup truck that will sell for just $24,950 (not counting fees or taxes). The base model has hand-crank windows, basic gray paint, no infotainment system, and just 205 miles of range, but itâs modular and allows buyers to add things like roof racks, upgraded headlights, or a back section that turns it into an SUV. While the US is indeed lacking in affordable EVs, it remains to be seen if thereâs market interest for a low-end pickup truck in a country where the average new pickup sells for over $66,000.
đđłïž An NYC politician was arrested for forgery over AI-generated photos
A politician from Queens who ran for state assembly last year was arrested for forgery after it was revealed that heâd used AI to create fake endorsements, photos, and news articles. He made a social media post claiming the endorsement of the Queens Jewish Alliance, using their logo on a realistic looking (but AI-generated) endorsement sheet; he also swapped his face into a picture of a prominent local politician shaking a manâs hand, making it appear that that politician supported him. In the complaint documents, the Queens District Attorney added that this man had deepfaked photos of his opponent and generated AI videos âappearing to show endorsements from a police precinct and an elementary school.â
đâł Worth your time
Some especially insightful pieces weâve read, watched, and listened to recently.
Your AI Is Not a Tool (The Convivial Society) â Applies a McLuhanist lens to argue that AI, like most modern digital technologies, isnât really a tool. Tools exist outside of us and channel certain skills, and they can be picked up and put down at will. But AI is an environment that envelops us and shapes us; you canât escape it or choose not to engage. This line of thinking refutes the clichĂ© that âtechnology is just a tool, and what matters is what we do with it.â
Why Flying Within Africa is Harder Than Flying Out of It (Proxima Imperium) â Argues that the lack of competition (and resulting high prices) of intra-African flight routes is due not to corruption but to a collective action problem. Countries signed agreements to allow airlines to freely cross national borders, which would stimulate competition and drive down prices, but each individual country has an incentive to defect and lock down its airspace to prop up its (often struggling) state-owned flag carrier. Thus, good treaties like the Yamoussoukro Decision are enacted de jure, but everyone slow-walks the implementation, leaving the plans as good as useless.
AI Isnât Management. Try Explaining That to Matthew Prince. (Programmable Mutter) â Political scientist Henry Farrell examines a tech CEOâs âself-congratulatory op-edâ where he talked about growing his business while laying off 20% of its employees; the essay cited the writings of famed management consultant Peter Drucker to support its argument that middle managers (âmeasurersâ) can be eliminated. Farrell argues that Prince misread Drucker; Drucker says that technology can and should be used to automate menial work and free employees up to make higher-level judgments and strategic decisions. This vision would actually lead to more mid-level management, not less, and Drucker would say itâs a good thing because it helps develop humansâ capacity and leads them to self-actualization.
Iranâs Ultimate Banned Book (Amir Ahmadi Arian) â Examines Iranian writer Sadeq Hedayatâs 1936 masterpiece The Blind Owl, which holds the rare distinction of being banned both before and after the Iranian revolution, surviving in a form similar to Soviet samizdat. The hallucinatory, opium-fueled novel follows a pen-case painter who descends into a delirious oblivion, ultimately transforming into the very Quran-reciting old man he despises. Hedayat deeply loved his homeland yet routinely reviled it in letters as âStinkistan.â Named for a bird that portends catastrophe rather than wisdom in Persian culture, the novel offers an absolute, uncompromising pessimism.
đđ
Lens of the week
Introducing new ways to see the world and new tools to add to your mental arsenal.
This weekâs lens: prognosis vs. diagnosis.
âYouâll be fine in two weeks,â is a reassuring thing for a doctor to say, but it leaves out whatâs actually wrong with you. It is a prognosis â a prediction about where you end up. It says nothing about the diagnosis, which is whatâs actually causing that outcome.
Physicians used to be excellent prognosticians and useless diagnosticians â and not that long ago, either. This is at first surprising, since the future is naturally less certain than the present. In 1850 a skilled doctor could often tell you whether youâd survive a fever, without the faintest idea of its source â or worse, with certainty that the fever was caused by humoral imbalance, not bacterial infection.
The two reduce different kinds of uncertainty. Since a prognosis forecasts the outcome, a good one is confident, specific, and correct (often enough). Diagnosis goes after the cause and can identify levers, directing us toward how to intervene.
Sometimes we mistake the prognosis for the diagnosis. âThis project will slip three weeksâ tells us something valuable, but it doesnât say why. Maybe a handoff broke. Maybe nobody owned it. You can become a brilliant forecaster of dysfunction and remain completely unable to treat it or, even worse, you can end up treating the symptoms. Adding more status meetings or more staffing to a late project may very well be the blood letting of the organizational world. Bring me the leeches!
Next time somethingâs wrong, ask: Do I need a prognosis? diagnosis? both? (But, unlike with germs, when youâre working with humans, remember to engage in connection before correction.)
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