05: Japanophilia and Greenhouses
Are we converging on design choices and their values far too early?
This week’s main thing
Three years in. You’ve been told the question is which vendor, which model, which roll-out plan. You’ve been told the work is deployment.
But step back for two seconds. We are in the first seconds of a much longer and more consequential transformation of our workplaces, our economies, and our societies. Unfortunately, though, the haste to get things done is deciding for us where this transformation will go (and there don’t seem to be many detours).
This week SAP CEO Christian Klein launched what he called the Autonomous Enterprise: “agents run the business and you can focus on what truly matters.” ServiceNow announced the same exact thing. We’re even at the predictable (and somewhat disappointing) point where IBM’s CEO study, published May 4, found that 76% of large organizations now have a Chief AI Officer. We’re speed running the last digital transformation playbook and spending very little time thinking critically.
In all of our enterprise products, there’s a set of principles already locked in:
Coordination between your people is overhead.
The unit of work is the task.
The destination is full autonomy.
For example, Google’s Memory Bank remembers a user’s expense habits. It does not remember who your team is or what they’re working on together. It can build on your last chat, but not on your existing relationships.
Now look at Japan. This week. PM Sanae Takaichi’s government stood up a cross-ministerial task force on AI workforce training. Microsoft’s Japan rollout runs through a partnership with the Japanese Electrical, Electronic and Information Union as training partner, not as a stakeholder to be managed around. The OECD finds AI users in Japanese AI-adopting companies expect AI to create more jobs than it removes.
Cleotilde Gonzalez, Anita Williams Woolley, and their co-authors at Carnegie Mellon, MIT, Illinois, and Harvard put the alternative in PNAS Nexus this spring. Organizations frame the issue as humans versus AI, they argue, but the better question is how to design teams “so AI expands what people can notice, remember, and reason through” while people provide “context, judgment, and accountability.”
What to say to your CEO this week. “Before we sign the next platform deal, I want a meeting on what we’re conceding by signing it. Not features. Not price. The decisions about how our people work together that are already inside the product. We’re three years in and we’re still acting like the question is which agent. The question is which assumptions we just locked ourselves into.” Then bring the assumption sheet (next section). It’s a thirty-minute exercise. Do it before the next platform pitch, not after.
This week’s move: write the assumption sheet before the vendor sheet
The next agent-platform pitch will come with the standard procurement diligence. Data residency. SSO. Audit logging. Pricing. SLAs. The procurement function does fine work on those. It can’t do the work upstream of them, because the assumptions are baked into the product before any clause in the contract gets negotiated.
So write the assumption sheet first. One page. Six rows. One column for the question.
Coordination. Does this platform treat the coordination between our people as overhead to be routed around, or as work worth preserving? Look for shared visibility, joint review, paired sessions. If they’re not in the product, the product has a position on whether that work matters.
Unit of work. Does the platform decompose work into discrete tasks with clean handoffs, or does it support two people puzzling over an ambiguous case for an hour? Make the vendor demo the second one. Watch carefully.
Destination. When the vendor describes the end state, is a human still in the workflow or escalated out of it? Note the language exactly. “Truly autonomous” is a different bet than “agents as part of the team.”
Relational persistence. Does the system remember a person, or only a workflow? Memory Bank remembers expense habits. Ask whether anything in the product remembers what your team is working on together, what’s changed since last quarter, who carries which expertise. The answer is almost always no. That’s a choice.
Naming. Is the AI described as a teammate, an assistant, a tool, a service? Each is a different theory of what humans are for. Pick the one your team can actually use.
Exit. If this turns out to embed assumptions you’d want to reverse later, what does reversing cost? Get the answer in writing.
Bring the sheet to the next platform review. The questions are obvious once you see them, and almost nobody is asking them. That’s the chief of staff’s specific contribution.
Top stories
SAP and ServiceNow launch competing “Autonomous Enterprise” visions in the same week. At SAP Sapphire on May 13, CEO Christian Klein unveiled the SAP Business AI Platform and Autonomous Suite, with the framing that “agents run the business and you can focus on what truly matters.” A week earlier at Knowledge 2026, ServiceNow announced an Autonomous Workforce of AI specialists spanning IT, CRM, HR, finance, legal, and procurement, partnered with Google Cloud and NVIDIA. The framing is the news: two of the largest enterprise software vendors converging on the same end-state language. Source
Gartner research finds AI-cited layoffs uncorrelated with AI ROI. Surveying 350 global executives at companies with over $1B in revenue, Gartner found that 80% of those piloting AI had reduced workforce. The cuts didn’t track with AI returns. Helen Poitevin, VP analyst, told Fortune the layoffs appear to be “a kind of one-time exercise” rather than what produces full value. Corroborated by NBER Working Paper 34984 (Atlanta Fed authors, n=750 corporate executives, March 2026), which projects aggregate employment decline from AI in 2026 at under 0.4%. Source
IBM finds 76% of organizations now have a Chief AI Officer, up from 26% in 2025. The IBM Institute for Business Value’s 2026 CEO Study, conducted with Oxford Economics across 2,000 CEOs in 33 countries, documents the fastest C-suite role expansion of the era. 83% of CEOs say AI success depends more on people’s adoption than on the technology itself. Independent CHRO research from Gartner corroborates that 78% of CHROs agree workflows and roles must change to capture AI value. Source
Meta employees distribute protest flyers against mouse-tracking AI training program. On May 12, flyers appeared in meeting rooms, near vending machines, and on restroom dispensers at multiple US Meta offices, denouncing the Model Capability Initiative as the “Employee Data Extraction Factory.” The software records mouse movements, keystrokes, and screenshots to train AI agents. A petition cites the National Labor Relations Act. UK colleagues are organizing with United Tech and Allied Workers. The flyers landed seven days before Meta’s planned May 20 cut of 8,000 roles. Source
Shopify CEO Tobi Lütke describes a different deployment pattern. In a May 11 post, Lütke described River, Shopify’s internal coding agent, which operates only in public Slack channels. 5,938 employees used it across 4,450 channels in 30 days, with merge rate climbing from 36% to 77% over two months through collective learning. Lütke called the design a Lehrwerkstatt, a teaching workshop: “The whole shop floor is the classroom. You learn by being near the work.” Source
Last time around: the divergence of 1985
October 1985, Cambridge, Massachusetts. The MIT International Motor Vehicle Program begins a five-year study across fifteen countries, led by James Womack, Daniel Jones, and Daniel Roos. The provocation is simple: Japanese auto plants are producing vehicles with half the defects and half the labor hours of US plants, using the same machines and many of the same components.
The answer, when The Machine That Changed the World publishes in 1990: Toyota and its peers had built a different theory of work into the same equipment. Andon cords let any worker stop the line. Quality was built in upstream, not inspected at the end. The worker was seen as a source of improvement, not a source of variance. Same machines, different assumptions, different cars.
US manufacturers had access to the same equipment. They chose differently, year after year, until the gap had compounded for a decade and the choice became fixed.
Manufacturing is not cognitive work, and Japanese employment in 1985 was a different beast. But the lesson translates: when a new technology arrives, the assumptions in how it gets deployed are at least as consequential as the technology itself.
From the frontier
In Longmont, Colorado, a 367 square foot greenhouse at 5,090 feet of elevation has spent the last few weeks running an AI planner called Iris alongside a $5 ESP32 microcontroller. Iris writes bounded climate tactics: setpoint biases, mist limits, venting posture. The ESP32 owns the relays on a five-second cycle, with the safety logic local on the chip. This is the assumption sheet built into hardware. The owner publishes everything: plans, scorecards, costs, stress hours, water use, failures, and the exact list of tunables the AI is allowed to change. You can go and look at the live Grafana panels right now. Source
Potpourri
From someone doing it. Amy Hall, dean of nursing at Franciscan Missionaries of Our Lady University, told Axios this week that “nurses need a stronger voice in which tools are adopted.” She was responding to the Elsevier Clinician of the Future 2026: Nurses Edition report, released around International Nurses Day on May 12, surveying 692 nurses and 2,065 doctors across 118 countries. The headline: 41% of nurses said their views are rarely or never adequately represented in their organization’s decision-making, against 19% of doctors saying the same. Source
From the weirder edge of the week. At the SFER IK museum in Tulum, an installation called The Bat Cloud invites you to ask a bat a question. The wooded area around the museum has been seeded with water, fruits, and insects, and an AI oracle trained on bat vocalizations answers your question with the help of the bats’ actual responses to the environment. Source