Gruow · Writing

We’re building the tool we needed, because nothing kept track of what we’d agreed

2026-11-11T00:00:00.000Z

Some of the most useful software started as a fix for the people who built it. Basecamp began as 37signals’ way of running its own client projects. Slack began as the chat tool a games company built for its own team.

The pattern makes sense. When you build for yourself, you can’t fool yourself about whether it works. You feel the problem every week, and you know exactly when it’s gone.

That’s how we approach new products at Gruow. This is the story of the first one: a tool for the oldest problem in client work, remembering what was agreed.

How we stumbled on it

We’ve done work we never got paid for. Not because a client acted in bad faith, but because nobody could say quickly what had been agreed months earlier.

It usually starts small. A request arrives in an email or comes up on a call. It sounds reasonable, it’s quick, and saying no feels petty, so it gets done. A few more follow. Months later, neither side can say for sure whether that work was in the original scope, whether it was ever priced, or whether it was billed. The agreement is a PDF in someone’s inbox. The changes are spread across email threads, call notes and chat.

When we looked back at the jobs where this happened, the problem was never the conversation. It was the record. Clients forget what was agreed too, and they’re as frustrated as we are when the answer takes an afternoon of digging. Both sides were missing the same thing: one place that shows what was agreed and everything that has changed since.

It gets harder as more people are involved. When a job includes partners or contractors, there’s a second set of promises, and a change on one side touches the other.

It isn’t just us

Once we started looking, the same story turned up everywhere.

Most agencies lose at least $1,000 a month to work they never bill

Monthly loss to unbilled work, 273 US agency leaders, 2025: 87% lose $1,000 or more

Monthly loss to unbilled work
Monthly loss	Share of agencies
Less than $1,000, or not stated	13%
$1,000 to $5,000	57%
More than $5,000	30%

The first band is the remainder of respondents; the survey reports only the two larger bands. Source: Ignition survey of 273 US agency leaders, 2025, via Anchor and The Drum. Redrawn by Gruow.

In a 2025 survey of 273 US agency leaders, run by the billing company Ignition, only 1% said they bill for all out-of-scope work. 78% rarely or only sometimes charge for it. The money problem spreads from there: 71% said at least one in four invoices is paid late, and 63% called their cash flow unpredictable. Ignition sells tools for this problem, so treat its numbers as a strong signal rather than the final word.

The wider data points the same way. In the Project Management Institute’s 2018 survey, 52% of projects completed in the previous year had experienced scope creep, up from 43% five years earlier.

The most telling number comes from Karl Sakas, who advises agency owners. When a client asks for something extra and the agency replies “would you like me to estimate that?”, he finds about half of clients drop the request and 40% ask for the estimate. The last 10% say they thought it was already included. That 10% isn’t a pricing problem. It’s two people remembering the same agreement differently.

Why nothing on the market closes the gap

Before building anything, we read through reviews and forum threads about the tools agencies already use for proposals, contracts, billing and client portals. Plenty of good software exists. It just stops at the wrong moment.

Agreements freeze at signature. Proposal tools are built to get a document signed. After that, changing it is awkward. “Once a document is sent, you’re kind of stuck with it,” one PandaDoc user wrote. In Ignition, adjusting a signed proposal creates a linked copy the client has to accept again, and payments have to be switched back on by hand.

The all-in-one tools are heavy. Agency management platforms can track everything, but setup is the most common complaint across them. Some are hard to configure without paying for implementation.

Clients don’t log in. Portals assume the client will visit. Many won’t. “90% just didn’t use it and still asked everything by email,” one web designer wrote.

Small fixes haven’t stuck. At least nine small scope-creep tools have launched in the past few years, most with barely a comment on their launch posts. Across eight forum threads about scope creep, nobody named a change-order tool they used.

So agencies cope by hand. They absorb small changes to protect the relationship, add a 10 to 15% buffer to quotes, and keep a list of extras to raise later, in a spreadsheet or a shared doc.

Other industries solved this long ago. In construction, a change order is a normal, signed amendment to the original contract, priced before the work starts. Event-planning software has the same thing built in. Agency tools mostly don’t.

What we’re building

We’re building a shared record of what was agreed and everything that has changed since, which the studio and the client can both see. Its working name is Lobby.

We won’t go into how it works yet. The principles are simple:

The agreement stays live. It changes only when the client approves, and every version is kept.
It meets people where they already work. Most client conversations happen in email, so that’s where it starts.
It doesn’t replace your other tools. Project and accounting tools stay where they are.
It works for both sides. The client gets the same clear answer we do, without having to ask.

The data also showed us the hard part. 78% of agencies don’t reliably charge for extra work, often because raising it feels awkward. A better record alone won’t fix that. The tool has to make the right conversation easy to start, so we’re designing for that from the beginning.

How we’ll learn from it

Gruow is the first user. We’ll run our own client work through it from the start, and measure it the only way that counts: work that would have gone unbilled, and now doesn’t.

We’ll write about what we learn here, including what doesn’t work. If it doesn’t save us money, it won’t save anyone else’s. If it does, we’ll open it to other studios.

That’s the kind of innovation we believe in. Not a guess about someone else’s problem, but a fix for one we live with every week.

Sources

Anchor, on scope creep and agency cash flow (on a 2025 survey of 273 US agency leaders, also reported by The Drum, May 2025; includes Karl Sakas’s estimate figures)
Project Management Institute, Scope patrol (on PMI’s 2018 Pulse of the Profession)
Capterra, PandaDoc reviews
Ignition, How do I add, remove or change a service in an active proposal?
Capterra, Accelo reviews
r/webdesign, on client portals (snapshot mirror)
r/webdev, on absorbing small changes (snapshot mirror)
Indie Hackers, a scope-creep tool launch
Planning Pod, Creating a change order for a signed proposal

AI is taking the questions we used to ask each other

2026-10-30T00:00:00.000Z

Most of what people know at work never gets written down. It moves as questions. How do I do this? What does that acronym mean? Is this right? Each one is small, and each one carries a bit of know-how from one person to another.

That traffic is moving to chatbots. The questions still get answered, usually faster. What stops happening is everything else a question used to do: pass on context nobody documented, show who knows what, and build a little trust between two people.

The short version: AI is good at answers, and colleagues were never only answers.

The questions have moved

In an October 2026 Clutch survey of 669 US workers who use AI regularly, 45% said they go to AI first with a work question. 16% go to a coworker first, and 9% to their manager.

The questions that move first are the ones people would rather not ask out loud. 76% have asked AI something they’d be embarrassed to ask a colleague, and 44% turn to AI first after making a mistake. Of those who go to AI first, 72% say it’s for speed.

The shift isn’t limited to one country or one level. In ELMO’s survey of 1,068 Australian employees, run by YouGov in the first half of 2026, one in seven go to AI before a person. Among senior leaders and business owners it’s 26%. 28% of those who pick AI say they don’t want to bother a colleague.

The public version of the same habit is easier to measure. In a study published in PNAS Nexus, researchers compared Stack Overflow, the programmers’ question site, with similar sites where ChatGPT was restricted. Within six months of ChatGPT’s release, activity on Stack Overflow fell 25%. The posts that disappeared weren’t the weak ones: quality held steady, so good questions left too. By May 2025, the site’s monthly question count was back to where it was at launch in 2009.

What travelled with the question

A question to a colleague did more than get an answer. Four things went with it, and none of them come back from a chatbot.

Context nobody wrote down. The answer to “how do we do this here?” lives in people: what was tried before, which client hates what, why the process has that odd step. Workers know this. They trust AI more for writing (64%) and how-to questions (60%), but they trust coworkers more for company-specific knowledge (67%), and by a wide margin for judgment.

For judgment, feedback and career advice, workers trust coworkers more than AI

Share of workers who trust each source more, by kind of question (%)

Share of workers who trust each source more, by kind of question
Kind of question	Trust a coworker more	Trust AI more
Judgment calls	36%	11%
Feedback on work	38%	24%
Career advice	30%	19%

Source: Clutch survey of 669 US workers who use AI regularly, October 2026. Redrawn by Gruow.

Knowing who knows what. Every question asked out loud tells a team where the knowledge sits and where people are stuck. In the Clutch survey, 51% notice fewer quick questions and check-ins from teammates. 38% see teammates solving the same problem separately, and 57% say AI has created silos on their team. Most still say their relationships are fine: 91% call them unchanged or stronger. The cost shows up in the work before it shows up between people.

Standing with each other. People take questions to AI to avoid looking incompetent. The research says that fear is mostly misplaced. In experiments published in Management Science, Alison Wood Brooks, Francesca Gino and Maurice Schweitzer found that asking for advice on a hard task makes people seem more competent, not less, in the eyes of the person they ask. The embarrassing question was often a small win for both people.

An answer the next person can find. A good answer on Stack Overflow helped everyone who came after. An answer in a private chat helps one person, once. The public record that today’s AI tools learned from is getting thinner.

We’ve seen this shape before

Two earlier studies show what happens to a network when help stops coming from people.

When Microsoft moved its US staff to remote work in 2020, researchers tracked the email, chat, calls and meetings of more than 61,000 employees. Time spent collaborating across groups fell by about 25%. People talked more with their inner circle, added new contacts more slowly, and their networks grew more cut off from each other. The authors warned that teams like this may have a harder time finding and sharing new information.

AI coding tools show the same pattern. Researchers from Harvard Business School, GitHub and Microsoft followed open-source developers from 2022 to 2024, comparing those who got GitHub Copilot free with similar developers who didn’t. With Copilot, developers did 12% more coding and 25% less project management. They also worked with far fewer people: an average of 5 collaborators on public projects, against 22 without it.

Neither study is about chatbots replacing colleagues directly. Both point the same way: when a tool covers what another person used to, the circle of people you work with gets smaller.

Who loses most

Newer people. They have the most questions and the most to learn from the answers. In the Clutch survey, 33% have noticed newer employees getting less mentoring, and 76% worry they’re missing what colleagues would have taught them. In the Copilot study, the least experienced developers moved furthest from shared work, cutting project management by up to 27% against 14% for seasoned ones.

The people who used to be asked. Being asked is how an expert becomes known, and how they learn where others get stuck. Fewer questions means fewer chances for that person to teach, to be credited, and to spot a problem early.

So what do you do about it?

Not stop people asking AI. For a how-to question, a chatbot is often the better first stop: faster, always there, and no one feels judged. The aim is to keep the parts of asking that a chatbot can’t do.

Ask AI for the how, people for the why. The trust data already splits this way. Steps, wording and syntax go to AI. Judgment, feedback and “how do we do it here” go to a person.
Write down what a chatbot can’t know. Company context only reaches AI if someone records it. In the Clutch survey, 61% of workers would ask a company-aware AI tool before a coworker. That only helps if the context it draws on is current.
Share answers back. When AI solves a problem, post the fix where the team can see it. It costs a minute and stops the next person solving it alone.
Make asking cheap for new people. Name someone they can ask anything, and protect time for it. The questions they’re embarrassed to ask are the ones that teach them most.

The questions will keep moving to AI, and most of them should. What’s worth keeping is the habit of asking each other the ones that matter.

Sources

Clutch, AI coworkers are answering the questions we’re too embarrassed to ask (survey of 669 US workers, October 2026)
HRD, AI is starting to outrank managers as workers’ first stop for advice (ELMO Employee Sentiment Index, YouGov, 1,068 Australian employees, January to June 2026)
del Rio-Chanona, Laurentsyeva and Wachs, Large language models reduce public knowledge sharing on online Q&A platforms (PNAS Nexus, September 2024)
The Pragmatic Engineer, Stack Overflow is almost dead (May 2025, from Stack Exchange Data Explorer)
MIT Sloan Management Review, Does deciding to seek advice signal weakness? (on Brooks, Gino and Schweitzer, Management Science, 2015)
EurekAlert, The effects of remote work on collaboration among information workers (Yang, Holtz and others, Nature Human Behaviour, September 2021)
Harvard Business School Working Knowledge, Can AI help managers love their jobs again? (Hoffmann, Boysel, Nagle, Peng and Xu, Generative AI and the Nature of Work, January 2025)

The models are good enough. The gap is knowing where they fit.

2026-10-13T00:00:00.000Z

A line has been going around AI circles: you don’t need God to write your email. The joke is that today’s models are already far more capable than most of what people ask of them. A smarter model won’t fix a vague request.

The evidence backs the joke. Models now match experienced professionals on about half of real work tasks, and the price of using them has fallen more than 280-fold in two years. Yet most companies report little or nothing from AI.

The difference isn’t the model. It’s knowing where AI fits in the work, and building it in there. Ask it for small, generic things and you get small, generic results.

The models are good enough

In September 2025, OpenAI published GDPval, a test built from 1,320 real tasks across 44 occupations in nine industries. Professionals with an average of 14 years’ experience wrote the tasks, which produced real files: documents, spreadsheets and slide decks. Other experts graded the results. The best models’ work was rated as good as or better than the experts’ in nearly half the tasks, at about a hundredth of the time and cost.

The price has collapsed too. Stanford’s 2025 AI Index found that running a model as capable as the original ChatGPT cost $20 per million tokens in November 2022 and $0.07 by October 2024.

The GDPval tasks came with one important limit: each model got one attempt, with no back-and-forth and no background on the client or the company. That’s the gap this piece is about. Anthropic reached the same view from how businesses actually use its models. In its September 2025 Economic Index, it found that “capabilities seem to matter more than cost” in what companies deploy, and that using AI for complex tasks “might be constrained more by access to information than on underlying model capabilities.”

In plain terms: the model is ready. What it doesn’t have is your business.

Same tools, very different results

Almost every company now has access to the same models. The results couldn’t be further apart.

Only 5% of companies get value from AI at scale. 60% get little or none.

Share of companies by AI maturity, survey of 1,250 senior executives, 2025

Share of companies by AI maturity
Group	Share of companies
Little or no value	60%
Starting to see value	35%
Getting value at scale	5%

Source: BCG, The Widening AI Value Gap, September 2025. Redrawn by Gruow.

The consulting firm BCG surveyed 1,250 senior executives in 2025. The few companies getting value at scale reported 1.7 times the revenue growth of the rest and a 1.6 times higher operating margin.

McKinsey’s 2026 survey of 1,719 people in 97 countries found the same shape. Only about 6% are “high performers”, meaning AI accounts for at least 5% of their operating profit. 37% say AI has added to profit at all.

The clearest picture of generic use comes from Denmark. Economists Anders Humlum and Emilie Vestergaard linked survey answers from 25,000 workers in 11 occupations exposed to AI chatbots with official payroll records. Average time saved was 2.8%, and they found no measurable effect on earnings or hours worked. But where employers invested in training and their own tools, people used AI more and found new tasks for it. Access alone did little. Fit did the work.

Where it fits, and where it doesn’t

AI isn’t good at everything to the same degree, and the edge between what it does well and badly isn’t where people expect. Researchers from Harvard and BCG called this the jagged frontier, and tested it on 758 of BCG’s own consultants in 2023.

The same AI made consultants better on some tasks and worse on others

Consultants with GPT-4 against those without, 758 BCG consultants, 2023

Consultants with GPT-4 against those without
Measure	Change
Inside the frontier: quality of work	+40%
Inside the frontier: speed	+25%
Inside the frontier: tasks completed	+12%
Outside the frontier: correct answers	−19 percentage points

Source: Dell’Acqua and others, Harvard Business School and BCG, 2023, via HBS Working Knowledge. Redrawn by Gruow.

On a product-design task inside the frontier, consultants with GPT-4 did more work, faster, and better. On a business-analysis task just outside it, the same tool made them more likely to get the answer wrong, because it gave a confident, plausible answer that missed what the task needed. Same people, same model. The only thing that changed was the fit between the tool and the task.

Fit also depends on who’s using it. In a study of 5,179 customer support agents, Erik Brynjolfsson, Danielle Li and Lindsey Raymond found that an AI assistant raised issues resolved per hour by 14% on average, and by 34% for new and less-skilled agents. For the most experienced agents, it made almost no difference. The assistant had learned from the best agents’ conversations, so it passed their know-how to everyone else.

Knowing where AI fits means knowing both: which tasks sit inside the frontier, and which people gain most from help with them.

What it looks like when it fits

The companies with real results didn’t ask AI for small things. Each picked one costly, repeated piece of work, gave the model what it needed to do that work, and built it into how the work already ran.

Company	Where they fitted AI	What changed	Reported by
Duolingo	Course creation: one strong base course, adapted by AI for dozens of languages, checked by learning designers	148 new courses in about a year. The first 100 took about 12 years.	Company, April 2025
Walmart	Product catalog data, the details shoppers search and filter by	Over 850 million data points created or improved. Executives said it would have taken 100 times the staff.	Earnings call, August 2024
Morgan Stanley	Advisors’ research library and client meeting notes, tested against expert answers before rollout	Over 98% of advisor teams use it. Advisors’ access to the firm’s documents rose from 20% to 80%.	OpenAI case study
A Fortune 500 software company	Support agents’ replies, with suggestions drawn from its best agents’ conversations	14% more issues resolved per hour, 34% for newer agents	Brynjolfsson, Li and Raymond, independent study
Klarna	Customer service chat in 35+ languages	2.3 million conversations in the first month, two-thirds of all chats. Resolution time fell from 11 minutes to under 2.	Company, February 2024

Most of these figures come from the companies themselves, so they show the best case. Klarna also shows the limit. In May 2025, its CEO said the AI-first approach had led to “lower quality” service, and the company began hiring people again so customers could always reach a human. Knowing where AI fits includes knowing where it stops.

What the companies that get value do differently

Put the cases and the surveys side by side and the same pattern shows up.

Generic use stays small. Fitted use changes how the work runs.

Generic use anyone, any task, one chat at a time

Ask a chatbot a general question
Get a generic answer
Paste it into the work

Fitted use one costly workflow at a time

Pick a costly, repeated task
Give it your data and rules
Build it into existing tools
Test it against your best people
A person approves what matters

Measure the result, then adjust or move to the next task

The pattern behind the cases above. Drawn by Gruow.

They change the work, not just the tools. In McKinsey’s 2026 survey, nearly three-quarters of high performers had fundamentally redesigned workflows around AI. About a quarter of everyone else had.

They aim at the core of the business. BCG puts 70% of AI’s potential value in core functions such as sales, marketing, manufacturing, supply chain and pricing, and only 13% in IT. Walmart’s catalog and Duolingo’s courses are the product itself, not a side task.

They give the model what it lacks. A model knows the world, not your company. Morgan Stanley pointed its assistant at the firm’s own research library. The support tool in Brynjolfsson’s study learned from the company’s best agents. That’s the missing context Anthropic’s data points to.

They test before they trust. Morgan Stanley graded every use case against expert answers before rollout, and ran daily tests after. Advisors still review the meeting notes before they go to clients.

How to find where AI fits in your business

You don’t need Walmart’s scale for any of this. The same questions work for a five-person firm.

Start from the work, not the tool. List the tasks that happen often, follow rules, involve a lot of text or data, and keep someone waiting. That’s where the frontier usually runs in your favour.
Put a cost on each one. Hours a week, delays, errors, customers lost. Pick the one where a fix would matter most.
Write down what a good result looks like. Use real examples from your best work. These become the test.
Give the model your context. Your documents, your rules, your past answers. This is the step generic use skips, and the one the evidence says matters most.
Put it where the work already happens. In the inbox, the CRM, the catalog, the support queue. A tool people have to remember to open is a tool they stop using.
Keep a person on what matters, and measure. Compare before and after on the cost you named in step two. If it doesn’t move, change the fit or drop it.

The models will keep getting better, and that won’t close the gap on its own. The businesses that pull ahead won’t be the ones with the smartest model. They’ll be the ones that know their own work well enough to show the model where it fits.

Sources

The Decoder, OpenAI says top AI models are reaching expert territory on real-world knowledge work (on OpenAI’s GDPval, September 2025)
Stanford HAI, AI Index 2025: state of AI in 10 charts
Anthropic Economic Index, September 2025 report
BCG, AI leaders outpace laggards with revenue growth and cost savings (The Widening AI Value Gap, 1,250 executives, September 2025)
McKinsey, The state of AI (1,719 participants, 97 countries, August 2026)
Marginal Revolution, Large language models, small labor market effects (on Humlum and Vestergaard, 25,000 Danish workers, April 2025)
HBS Working Knowledge, Humans vs. machines: untangling the tasks AI can and can’t handle (Dell’Acqua and others, 758 BCG consultants, 2023)
NBER, Generative AI at Work (Brynjolfsson, Li and Raymond, 5,179 support agents; Quarterly Journal of Economics, 2025)
Duolingo, Duolingo launches 148 new language courses (April 2025)
Retail Dive, Walmart taps generative AI for 850 million product data points (August 2024)
OpenAI, Morgan Stanley case study (vendor)
The Decoder, Klarna says OpenAI’s AI agent does the work of 700 support agents (February 2024)
Entrepreneur, Klarna is hiring customer service agents after AI couldn’t cut it (May 2025)

Less thinking, more checking: the new core skill is judgment

2026-09-22T00:00:00.000Z

For most of working history, the hard part was producing the thing: the draft, the analysis, the code. AI now produces a decent first version of all three in seconds. The hard part has moved. It’s deciding whether that version is right.

That makes judgment the core skill of knowledge work: knowing what good looks like, spotting what’s wrong, and knowing when it matters. It’s also the skill that heavy AI use wears down fastest, unless people protect it on purpose.

None of this is new. People have worried about it for 2,400 years, and they were partly right.

We’ve worried about this before

In Plato’s Phaedrus, written around 370 BC, the god Theuth presents writing to the Egyptian king Thamus as a cure for forgetting. Thamus isn’t convinced. In Benjamin Jowett’s translation:

This discovery of yours will create forgetfulness in the learners’ souls, because they will not use their memories… they will be hearers of many things and will have learned nothing; they will appear to be omniscient and will generally know nothing; they will be tiresome company, having the show of wisdom without the reality.

Thamus was wrong that writing would end thinking. It moved thinking somewhere else. Once anyone could write things down, the valuable skill became reading well: knowing who wrote it, whether it holds up, and what it leaves out. But his last line still lands. “The show of wisdom without the reality” is a fair description of an AI answer that’s almost right.

In 1983, the engineering psychologist Lisanne Bainbridge made the same point about factories and power plants in a short paper called “Ironies of Automation”. When designers automate a process, she wrote, the human is left with two jobs: watching the system, and taking over when it fails. Those are the hardest jobs there are. People can’t keep close attention on a system where little happens for more than about half an hour. And the skills needed to take over fade from lack of use: “physical skills deteriorate when they are not used.”

Her central irony was that the more we automate, the more we depend on a skilled human to catch what the automation gets wrong, and the fewer chances that human gets to stay skilled. Swap the control room for a laptop and the paper reads like it was written this year.

Confident, and checking less

The research on AI at work keeps finding the same thing: the more people trust the tool, the less they check it.

Researchers from Microsoft and Carnegie Mellon surveyed 319 knowledge workers about 936 real tasks they’d done with AI. Those more confident in AI applied less critical thinking to its output. Those more confident in their own skills applied more. The thinking didn’t disappear; it changed jobs. Less went into finding information and solving problems from scratch, and more into verifying, adapting and overseeing what the AI produced. The catch, in the authors’ words: people “often refrain from critical thinking when they lack the skills to inspect, improve, and guide” what AI gives them.

At a larger scale, KPMG and the University of Melbourne surveyed more than 48,000 people in 47 countries. 66% said they rely on AI output without checking its accuracy, and 56% said they’ve made mistakes at work because of AI.

Developers are the furthest along, and they’re the most wary. In Stack Overflow’s 2025 survey of more than 49,000 developers, 84% use or plan to use AI tools, but only 33% trust their accuracy and 46% distrust it. Their top frustration, named by 66%, is answers that are “almost right, but not quite”.

Even our sense of whether AI is helping can be off. METR, a research group, ran a trial with 16 experienced open-source developers on 246 real tasks from their own projects.

Developers felt about 20% faster with AI. They were 19% slower.

Speed with AI against without it, 16 experienced developers on 246 real tasks, early 2025

Speed with AI against without it, for 16 experienced developers on 246 real tasks
Measure	Change in speed
Predicted before the tasks	24% faster
Estimated after the tasks	20% faster
Measured	19% slower

Source: METR, July 2025. Redrawn by Gruow.

The developers weren’t careless. They were experts on their own code, and they still couldn’t tell from the inside that the tool was slowing them down. Judgment isn’t only about the AI’s output. It’s also about judging our own work with it.

When nobody checks

The cost of skipped checking is now public, and it lands on people paid to be careful.

In 2025, Deloitte delivered a AU$440,000 review of a welfare compliance system to Australia’s Department of Employment and Workplace Relations. A University of Sydney academic, Christopher Rudge, found it cited reports that don’t exist and quoted a federal court judgment that says no such thing. Deloitte later disclosed it had used a GPT-4o-based tool, corrected more than a dozen references and footnotes, and agreed to refund the final payment. The department said the recommendations didn’t change. The trust did.

Courts see the same thing weekly. A database kept by the researcher Damien Charlotin had logged 1,058 US court cases involving AI-made errors such as invented case law by June 2026. About 40% involved lawyers, not people representing themselves.

The quieter cost is Bainbridge’s second irony: skill fading with disuse. A study in The Lancet Gastroenterology & Hepatology followed 19 experienced endoscopists in Poland, each with more than 2,000 colonoscopies behind them. After AI-assisted colonoscopy became routine, their rate of finding precancerous growths in procedures done without AI fell from 28.4% to 22.4% within months. The tool helped while it was on. The doctors got worse at the job when it was off.

What good judgment looks like

Checking AI isn’t proofreading. Reading for typos won’t catch a quote that was never said. Judgment means three things: knowing what good looks like in your field, knowing where AI tends to be wrong, and knowing what’s at stake if it is.

A few habits follow from the evidence:

Check in proportion to the stakes. A social post and a report to a government department don’t need the same review. Decide which work gets a second look before anyone starts, not after something goes wrong.
Check the claims, not the prose. AI writing reads well. The errors hide in names, numbers, dates, quotes and citations, which is exactly where Deloitte and the lawyers went wrong. Open every source the work rests on.
Keep doing some of the work yourself. The endoscopists lost skill in months. If a task matters, do it without AI often enough to stay able to judge it with AI.
Measure, don’t trust the feeling. The METR developers felt faster and weren’t. Time the work, count the fixes, and compare.
Build expertise before handing over. The people who checked AI most in the Microsoft study were the ones most confident in their own skills. Judgment rests on knowing the work, which is why how new people learn it matters more now, not less.

In the systems we build at Gruow, an agent can draft, sort and suggest, and a person approves anything that matters. That isn’t caution for its own sake. It’s where the judgment lives.

Thamus was right that a new tool changes what thinking is for, and wrong that it ends it. The skill now is telling the show of wisdom from the real thing.

Sources

Plato, Phaedrus, translated by Benjamin Jowett (MIT Internet Classics Archive)
Lisanne Bainbridge, Ironies of Automation (Automatica, 1983; summary with quotes by Adrian Colyer, The Morning Paper)
Microsoft Research and Carnegie Mellon, The impact of generative AI on critical thinking (319 knowledge workers, 2025, via THE Journal)
KPMG and University of Melbourne, Trust, attitudes and use of AI (48,000+ people, 47 countries, April 2025)
Stack Overflow Developer Survey 2025: AI (49,000+ developers)
METR, AI coding can make developers slower even if they feel faster (July 2025, via The Decoder)
The Register, Deloitte refunds Aussie government after AI fabrications slip into report (October 2025)
UNC Law Library, Handling hallucinations: a reflection on the last three years (on Damien Charlotin’s AI Hallucination Cases database, June 2026)
News-Medical, AI use in colonoscopy may weaken doctors’ detection skills over time (Budzyń and others, The Lancet Gastroenterology & Hepatology, August 2025)

The missing first rung: who trains the next senior?

2026-09-03T00:00:00.000Z

Every senior was once a junior. They learned by doing the work nobody else wanted: the first draft, the first pass at the research, the routine code, the meeting notes. Someone more experienced checked it and sent it back, and over a few years the junior became the person doing the checking.

That entry-level work is exactly what AI now does best. So companies are hiring fewer juniors, and the data shows it.

In the short run, that’s a saving. In the long run, it raises a question few companies are asking: if nobody starts on the first rung, who becomes the senior who can judge what AI produces?

The first rung is thinning

The clearest evidence comes from payroll records. Economists at Stanford’s Digital Economy Lab, led by Erik Brynjolfsson, track millions of US workers through ADP, the payroll company. By June 2026, employment of 22- to 25-year-olds in the jobs most exposed to AI was 19% below where it would have been had it kept pace with less-exposed peers. Experienced workers in the same jobs showed no such gap, and the divergence has widened since the team first reported it in August 2025.

Two details matter. The drop comes mostly from fewer hires, not layoffs: the juniors already inside are staying, but fewer new ones are let in. And it sits in jobs where AI substitutes for the work. Where AI mostly helps people do their work, employment is flat or rising.

A Harvard study of 62 million workers at 285,000 US firms points the same way. From early 2023, junior employment at companies adopting generative AI fell 7.7% relative to similar companies within six quarters, through slower hiring. Senior employment kept growing.

The pattern shows up in single industries too. In tech, new graduates made up 7% of hires at the 15 largest companies in 2024, according to the venture firm SignalFire, and new-graduate hiring there was down more than 50% from 2019. In UK accounting, the Big Four all took on fewer graduates.

The UK Big Four took on fewer graduates. KPMG cut its intake by almost a third.

Graduate intake, 2023 to the following year: 6,499 to 5,442 across the four firms

UK Big Four graduate intake
Firm	2023	Following year	Change
KPMG	1,399	942	−33%
Deloitte	1,700	1,400	−18%
EY	1,800	1,600	−11%
PwC	1,600	1,500	−6%

Source: The Telegraph, via Scottish Financial News, June 2025. Redrawn by Gruow.

AI isn’t the only cause. Higher interest rates, over-hiring during the pandemic and offshoring all play a part, and none of the Big Four firms tied their cuts to AI in their own words. But the payroll data compares exposed and less-exposed jobs over the same years, so those wider forces affect both sides. The gap that’s left lines up with AI.

Why the first rung mattered

Junior work was never only cheap labour. It was how expertise got passed on. Much of what seniors know can’t be written down: which details matter, what a client will push back on, when a number looks wrong. People pick that up by doing the work, getting it checked, and doing it again.

Surgery has already run this experiment. Matt Beane, a researcher now at the University of California, Santa Barbara, spent more than two years watching hundreds of robotic and traditional operations at five hospitals, and interviewed surgeons and trainees at 13 more. In open surgery, the senior surgeon needs a resident’s hands throughout, so residents learn by doing. With a surgical robot, the senior surgeon can operate alone. Residents were left clearing smoke at the bedside or watching from a second console, waiting for a turn.

Only a minority found a way through. They practised on simulators and recorded operations, and pushed themselves on hard work with little supervision, mostly out of sight. Beane called this “shadow learning”. Most residents struggled, and all of them were still certified to operate.

That’s the risk now in offices. When a tool lets the expert work alone, the novice loses their place in the work, and nobody notices, because the work still gets done. The cost shows up years later, when the people who should be ready to judge the work never had the chance to learn it.

The Harvard study found one more sign of this. At firms adopting AI, promotions of juniors into senior roles rose, while fewer new juniors came in behind them. The ladder isn’t disappearing. It’s getting shorter at the bottom.

AI can also be the best teacher juniors have had

The same technology that removes junior work can speed up junior learning, when it’s built for that.

In the study of 5,179 customer support agents by Brynjolfsson, Danielle Li and Lindsey Raymond, the AI assistant was trained on the conversations of the company’s best agents. It raised the output of new and less-skilled agents by 34%. Agents with two months on the job who used it performed as well as agents with six months who didn’t. In the researchers’ words, the tool spread “the potentially tacit knowledge of more able workers” to everyone else.

The Harvard and BCG experiment with consultants found the same tilt. Consultants who scored below the median before the study improved 43% with AI; those above it improved 17%.

So AI isn’t the problem for juniors in itself. Used well, it compresses years of learning into months. The problem is a company that uses AI to skip hiring juniors at all, and loses the people it would have taught.

Rebuilding the first rung

The answer isn’t to ban AI from junior work. It’s to redesign that work so people still learn from it.

Keep hiring juniors, on purpose. Maybe fewer, but planned rather than left to chance. With AI, a new hire can reach useful work in months instead of years, so each junior costs less to train than before.
Make checking the new apprenticeship. If AI writes the first draft, the junior’s job is to review it alongside a senior who explains what’s wrong and why. That’s where judgment gets built.
Turn your best people’s know-how into material. The support tool worked because it learned from the top agents. Record decisions, keep examples of good work, and explain the reasoning, so both juniors and AI tools can learn from it.
Leave room to struggle. Beane’s successful residents practised hard work just past their ability. Give juniors some tasks to do without AI, with someone to check them.
Count it as a cost. A company that stops hiring juniors is borrowing against its future seniors. Put that in the same plan as the savings.

When we build tools that take over routine work, we ask who used to learn from that work, and where they’ll learn now.

AI can do much of a junior’s work. It can’t become your next senior. Someone still has to.

Sources

Stanford Digital Economy Lab, Canaries in the coal mine? Six facts about the recent employment effects of AI (Brynjolfsson and others, ADP payroll data to June 2026, revised August 2026)
Hosseini and Lichtinger, Generative AI as seniority-biased technological change (Harvard, 62 million workers at 285,000 firms, October 2025 version)
SignalFire, State of Tech Talent Report 2025 (May 2025)
Scottish Financial News, Big Four slash graduate jobs as AI takes over entry-level tasks (on The Telegraph’s figures, June 2025)
STAT, Matt Beane on how robotic surgery changed surgical training (Matt Beane, January 2018)
NBER, Generative AI at Work (Brynjolfsson, Li and Raymond; Quarterly Journal of Economics, 2025)
Stanford HAI, Will generative AI make you more productive at work? (April 2023)
HBS Working Knowledge, Humans vs. machines: untangling the tasks AI can and can’t handle (Dell’Acqua and others, 2023)

Kyvera is ready

2026-08-15T00:00:00.000Z

Kyvera is launching, and we’re genuinely glad to finally say so.

If you’ve ever had to collect compliance documents across a complex corporate structure, you already know the problem it solves. KYC and KYB onboarding is one long chase. Requests scattered across email, the same documents asked for three times, nobody sure what’s outstanding, and an audit trail stitched together after the fact. It’s slow, it’s manual, and in a regulated business the cost of getting it wrong is steep.

Kyvera replaces the chase with something structured. You start from the org chart, and it builds a secure data room for every entity, works out which documents each one needs, and pulls what it can from official registers before anyone gets asked. From there it’s one request instead of ten, a live view of what’s missing for both sides, and a full audit trail that’s regulator-ready by default.

We built it heads down, alongside the clients who live inside this problem every day, which is the only way something like this gets built right. The best signal we could have hoped for came before launch: a long list of businesses already waiting for the doors to open.

This is exactly the kind of work the studio exists for. A genuine problem, in an industry our clients know from the inside, solved properly rather than patched over. We’re proud of it, and prouder of the people we built it with.

If compliance is part of your world, go see what it does.

Why we refocused

2026-07-30T00:00:00.000Z

For a while, Gruow did a bit of everything: websites, brand identities, product design, development, training, audits. If someone needed it and we could do it, we said yes.

That made sense at the start. Saying yes is how a small studio finds work and finds out what it’s good at. Over time, it cost more than it gave back.

What doing everything cost us

It was hard to explain. When someone asked what Gruow does, the honest answer was a list. A list is easy to agree with and hard to remember. Someone whose advice we trust put it plainly: there was no clear reason to pick us over anyone else.

It was hard to get good. Depth comes from doing the same kind of work again and again, and seeing what holds up. Every new kind of job meant starting close to the beginning.

It hid the work we do best. The work we kept coming back to was building things that make work run: tools, plugins, and small pieces of software that get two systems talking. That work was buried somewhere in the list.

What changed

AI changed what software can do for a business, and how quickly it can be built. The models are now good enough for most of what businesses would ask of them. The open question is where they fit in a company’s work, and how to build them in so the work actually gets easier.

That question doesn’t belong to strategy, design or code alone. It needs all three, on the same piece of work, by the same people. That’s what we want to be good at.

What we do now

Strategy, design and code, applied to three kinds of work:

Products: new software, built from the idea through to the first working version.
Internal tools: software a business runs on, built around how its people actually work.
Integrations: connections between the systems a business already has, including MCP servers that let AI tools work with its own data.

Strategy is always tied to something we build. We don’t make mobile apps or no-code workflows, and we don’t maintain systems we didn’t build.

Learning as we go

We don’t have this figured out, and nobody does yet. The tools change month to month, and so does what works.

So we research as we work: new models, new tools, and how teams actually take them up. We publish what we find here. Some of it shapes what we build. Some of it will turn out wrong, and we’ll say so when it does. Our own products, E10s and Hallful, are where we try ideas on ourselves first.

Focus doesn’t mean doing less. It means each piece of work teaches us something we use on the next.