
This week’s #RBM reading begins with Zenith Data Systems, which turned Heathkit’s build-it-yourself heritage into a thriving computer business, although its Z-100 could run MS-DOS without actually being IBM PC compatible, a distinction customers presumably discovered at the least convenient moment. At Anthropic, coding agents are associated with rising productivity while junior engineers’ share of technical recruitment has fallen from 27% to 9%, raising the awkward question of how the next generation learns the trade when the machines are increasingly doing the groundwork. The New Yorker takes us inside ASML and TSMC, where the world’s most sophisticated chips depend on decades of obsessive engineering, international co-operation and an industrial ecosystem that cannot simply be downloaded from GitHub, regardless of how generous the README might be. Meanwhile, Ben Thompson argues that AI could accelerate the decline of the open internet, as security threats multiply, publishers lose traffic and people retreat into smaller, trusted communities running software built specifically for them. And finally, because apparently the technology industry hasn’t made relationships complicated enough, Charlie Jane Anders imagines two conscious cities falling in love, only to discover that surveillance, civic politics and a proposed transport partnership can ruin a perfectly good romance.
In this issue
5 piecesBradford Morgan White traces Zenith Data Systems’ emergence from radio manufacturing and Heathkit’s construction kits

In Abort Retry Fail, Bradford argues that Zenith successfully converted this heritage into institutional computing. The account follows Zenith’s 1979 Heath acquisition through the Z-100, citing government and university orders alongside reported revenue that reached $125 million in 1983 and roughly doubled the following year. The Z-100 used separate processors for CP/M and MS-DOS, requiring reboots to switch. Running MS-DOS did not ensure IBM PC compatibility, while disk formats restricted CP/M software access. Bradford describes a shift towards assembled machines that preserved Heathkit’s detailed documentation. Government and educational customers sustained growth, although the account closes with dependence on an ageing expansion standard and continuing compatibility constraints.
After reading this, I keep coming back to the Z-100’s documentation.... my understanding is that Heathkit’s inheritance allowed a useful separation: assembling the machine could become the manufacturer’s job while understanding it remained an option for the buyer, yet slling convenience needn’t require cultivating ignorance. The trick is where that distinction becomes tangible. I think that for an engineering team, I’d turn this into a design test; when a product becomes easier to use, does its explanatory material become optional or disappear? Those are quite different outcomes. A beginner should be able to get started without an apprenticeship in electronics. The same person might later want to diagnose a failure or change how the machine works. Documentation can leave that second door open without pushing everyone through it. I’d judge the move from Heathkit kits to assembled Zenith computers partly on whether it preserved that possibility. Treating the manual as disposable packaging would have thrown away a piece of the product. Factory assembly can save the customer labour; it doesn’t make their curiosity redundant.
Anthropic examine how intensive AI adoption changes employment.

Nathan Wilmers, Regina Catipon, Maxim Massenkoff and Peter McCrory examine how intensive AI adoption changes employment at Anthropic. Internal records and 17 interviews show rising output alongside a shrinking junior hiring share: juniors fell from 27% to 9% of technical recruits. Comparisons tracking individual employees associate coding-agent adoption with increased output, although employees chose whether to adopt these tools. The authors describe human attention becoming a constraint as employees supervise agents and hiring managers prioritise judgement. Anthropic’s workforce tripled over a year, with internal redeployment accompanying automation. The report proposes sharing productivity gains and monitoring threatened roles, while acknowledging incumbent protections exclude prospective hires. It presents organisational changes as signals for other employers, rather than a universally replicable model.
So, Anthropic’s interactive agents spend roughly 80% of session time waiting for people. That isn’t automatically waste: an approval may be necessary, and the person may be doing something more valuable. It does make human attention a sensible place to test the economics of an agent deployment. At Hitachi Digital we measure human intervention per successfully completed task, whilst each escalation should carry enough evidence for a decision and reach someone authorised to make it, a routine reporting correction and a proposed change to a production control system deserve different approval paths. Adding agents without designing those paths risks giving the same engineers a much longer queue. Overall, I think the true test should include inference costs, review time and rework, with acceptance criteria agreed before the pilot. I’d resist an agent-count target altogether. A useful commitment would be to complete a defined workload with less supervisory effort, while preserving the client’s safety and quality requirements. Nobody needs a faster way to fill an approval inbox.
Advanced chips are an extraordinary collective achievement sustained by international co-operation, specialised suppliers and accumulated practical knowledge

Gideon Lewis-Kraus visits to ASML and T.S.M.C., alongside interviews with engineers and executives, show how extreme-ultraviolet lithography and relentless manufacturing refinement make mass production possible. The reporting follows capabilities distributed across companies, from Zeiss’s precision mirrors to specialist machinists, which cannot be reproduced simply by acquiring equipment or blueprints. The article connects this interdependence to Taiwan’s strategic importance and American efforts to rebuild domestic chip production. T.S.M.C.’s Arizona expansion encountered gaps in contractor networks, experienced staff and clean-room laundry services. Former CHIPS Act official Mike Schmidt argues that public investment can seed these supporting industries over time. For computing and A.I., Gideon’s broader implication is that continued progress depends on maintaining international relationships that geopolitical rivalry and demands for self-sufficiency increasingly strain.
Fifteen years on the same nozzle is an unfashionable definition of a successful technology career. ASML physicist Jayson Stewart describes colleagues doing exactly that, while discussing projects extending beyond his likely retirement. I find that more revealing than the comparisons with rockets. A tiny component can contain a substantial portion of somebody’s working life. The achievement here seems to depend on people accepting a peculiar bargain: narrow your own field of attention so that the collective undertaking can become enormous. The article makes this feel surprisingly ambitious. You might never understand the whole machine, let alone become its celebrated inventor, yet your contribution can be indispensable. That unsettles the easy assumption that professional progress must eventually mean promotion away from hands-on work. I wouldn’t turn this into a sermon about employee loyalty. Such devotion also leaves organisations with a responsibility to train successors and keep the work worth doing. My admiration is for institutions capable of giving an obscure, difficult problem a home for decades. In an industry selling the next generation, it’s striking how much depends on someone being willing to stay with the last unresolved detail.
Ben Thompson argues that AI will accelerate the retreat from the open internet towards smaller, trusted groups

He connects blogging’s subscription shift and social networking’s move into group chats with emerging security threats. Drawing on recent vulnerabilities and his own applications, he predicts that cheap software creation will make private tools for individuals and teams increasingly attractive alternatives. Ben expects AI to intensify attacks on existing software while consuming publishers’ content without returning traffic. Although he anticipates stronger automated defences eventually, he considers independent public hosting increasingly untenable. His proposed response is software built for bounded communities, where lower development costs remove the need for mass audiences. He argues that these smaller markets could support previously uneconomic needs and desires.
Ben’s sports-viewing app makes the argument tangible... Imagine a tool that understands your peculiar combination of teams, time zones and family commitments. A product manager might reasonably reject that brief as too narrow to justify supporting. You could build it anyway. Just takes a few tokens...Yet I like the permission to be fussy. Suppose three colleagues need a tool that uses their own vocabulary and handles one deeply inconvenient exception. I’d be happy for it to remain unintelligible to everyone else. Generalising it would introduce work whose benefit those three people might never see. For that brief, refusing to grow would be a sensible design choice. I’d give these tools an expiry date as readily as a feature list. Retire one when keeping it working becomes more trouble than the original nuisance. A tool that earns its keep for a single season can still be a success. There needn’t be a pitch deck waiting behind every useful piece of code.
Charlie Jane Anders imagines neighbouring cities whose residents’ votes generate consciousness

Mainspring and St. Barnabas grow close through a proposed transport partnership, until Mainspring flirts with surveillance-driven Turnbull. Their relationship breaks down, then residents help repair it. The story explores collective identity and civic responsibility through a romance, rather than presenting research findings or technological predictions. Charlie Jane contrasts voluntary participation with Turnbull’s automatic monitoring and willingness to obstruct democratic decisions. The cities’ emotional lives affect public services, making their independence consequential for residents. A librarian and bartender connect civic feedback with reconciliation. For readers interested in urban technology, the setting raises questions about privacy, representation and the relationship between institutions and the people sustaining them.
This answers a question about love with an offer of “synergies”. It’s a wonderfully bleak chat-up line. I read the scene as a warning about being admired for your potential: praise can make you feel permanently unfinished, with the admirer conveniently appointed to supervise the improvements. There’s a particular cruelty in affection that always arrives in the future tense. You’ll be worth loving once you become more impressive, more like the version I’ve imagined. The promised reward keeps moving. An apparently generous offer to help can turn another person into a renovation project, complete with an indefinitely postponed completion date. That gives technological ambition a useful emotional test. I’d ask whether a proposed improvement leaves more room for the people who already live there, including their peculiar pleasures and their right to remain inconvenient. Imagine making a district easier to manage while gradually removing everything its residents enjoy about it. The resulting efficiency would be a peculiar achievement. A city can be worth loving before anyone has finished improving it.
My Week in AI
- How and when does AGI transform the economy? | institute.deepmind.com
- Chinese AI invasion gives enterprises a new way to control cost | aibusiness.com
- TokenEconomizer Experience-Driven Black-Box Cost Control for Coding Agents | github.com
- Building an agentic software factory? Start with the sensor | thenewstack.io
- OpenShell is the safe, private runtime for autonomous AI agents | github.com
- Gemini 4 Carbon our next frontier model | blog.google