From d0a4bec74254269661bf6dff7bd740df24c0a2bc Mon Sep 17 00:00:00 2001 From: Aleks Date: Mon, 18 May 2026 14:37:06 +0300 Subject: [PATCH] ingest: 55-of-employers-regret-ai-driven --- ...-05-18_55-of-employers-regret-ai-driven.md | 105 +++++++++++ ...f-employers-regret-ai-driven.transcript.md | 178 ++++++++++++++++++ 2 files changed, 283 insertions(+) create mode 100644 Business/Nate Corpus/2026-05-18_55-of-employers-regret-ai-driven.md create mode 100644 Business/Nate Corpus/2026-05-18_55-of-employers-regret-ai-driven.transcript.md diff --git a/Business/Nate Corpus/2026-05-18_55-of-employers-regret-ai-driven.md b/Business/Nate Corpus/2026-05-18_55-of-employers-regret-ai-driven.md new file mode 100644 index 0000000..cf33fb7 --- /dev/null +++ b/Business/Nate Corpus/2026-05-18_55-of-employers-regret-ai-driven.md @@ -0,0 +1,105 @@ +--- +title: "55% работодателей сожалеют об ИИ-увольнениях: агенты хороши в задачах, плохи в работах" +source: https://natesnewsletter.substack.com/p/55-of-employers-regret-ai-driven +author: "Nate Jones (Nate's Substack)" +published: 2026-03-21 +processed: 2026-05-18 +slug: 55-of-employers-regret-ai-driven +type: video +themes: + - "[[Agentic Workflow]]" + - "[[Evals]]" + - "[[Implementation Layer]]" + - "[[Audit Trails]]" +frameworks: + - "[[Contextual Stewardship]]" + - "[[Task-versus-Job Gap]]" +terminology: + - "[[Evals]]" + - "[[Agentic Workflow]]" + - "[[Implementation Layer]]" + - "[[Audit Trails]]" + - "[[Forward Deployed Engineer]]" + - "[[Harness]]" +--- + +# 55% работодателей сожалеют об ИИ-увольнениях + +## Центральный тезис + +Средняя продолжительность работы в ИТ — 18 месяцев–2 года. Средний запуск ИИ-агента — около 2 часов. Эти числа несопоставимы. Агенты хорошо справляются с **задачами** (tasks), но не умеют выполнять **работу** (jobs) — потому что не накапливают институциональный контекст. Чем мощнее агент, тем разрушительнее его «молчаливые» ошибки. + +> "A mediocre tool that fails obviously is annoying. A powerful tool that fails silently is dangerous." +> *Посредственный инструмент, который ломается очевидно — раздражает. Мощный инструмент, который ломается незаметно — опасен.* + +--- + +## Якорный кейс: Grigorev и продакшн-база + +Инженер Алексей Григорьев запустил ИИ-агент (через Cursor). Агент уничтожил 1,9 млн строк данных студентов вместе с бэкапами. Технических ошибок не было — каждое действие было локально корректным. Агент просто не знал, что перед ним боевая инфраструктура, а не временная копия. Знание об этом различии существовало только в голове инженера. + +Три новых исследования подтверждают: это паттерн, не случайность. + +--- + +## Ключевые фреймворки + +### [[Task-versus-Job Gap]] — Разрыв задача-работа + +Два бенчмарка на одних и тех же моделях дали кардинально разные результаты. Причина: один тестировал **задачи** (дискретные, без контекста), другой — **работу** (с накопленным контекстом, организационными зависимостями). Агенты побеждают в первом, проваливаются во втором. + +### [[Contextual Stewardship]] — Контекстное стюардство + +Новая роль человека в связке с агентами — не управление задачами, а **хранение и передача контекста**. Именно это умение рынок труда начинает оплачивать (Harvard, 62 млн работников). Лучшие люди должны писать [[Evals]], а не выполнять рутину. + +### Три промпта для закрытия разрыва + +| # | Название (EN) | Назначение | +|---|---------------|------------| +| 1 | Context gap audit | Аудит того, что агент не знает о вашем контексте | +| 2 | Eval writer for non-engineers | Написание [[Evals]] без навыков разработки | +| 3 | Decision documenter | Фиксация логики решений, которую агент никогда не узнает сам | + +> "The best tools we have for managing agent risk are human brains and human brains crafting evaluations. Not better prompts. Not bigger context windows." +> *Лучший инструмент управления риском агента — человеческий мозг и [[Evals]], которые он создаёт. Не лучшие промпты. Не большее контекстное окно.* + +--- + +## Терминология + +| RU | EN | Примечание | +|----|----|------------| +| Разрыв задача-работа | Task-versus-job gap | Центральный концепт статьи | +| Контекстное стюардство | Contextual stewardship | Новая роль человека рядом с агентом | +| Институциональный контекст | Institutional context | Знание, живущее только в головах людей | +| Стена вайб-кодинга | Vibe coding wall | Момент, где AI-разработка упирается в пределы промптинга | +| Аудит контекстного разрыва | Context gap audit | Промпт №1 для диагностики | +| [[Evals]] | Evals | Оценочные наборы; писать должны лучшие, не джуны | +| [[Audit Trails]] | Audit trails | Фиксация решений для передачи агентам | +| [[Agentic Workflow]] | Agentic workflow | Рабочий процесс с делегированием агентам | +| [[Implementation Layer]] | Implementation layer | Слой между моделью и реальным workflow | + +--- + +## Что использовать для нашего портфеля + +**Как AI-интегратор и [[Implementation Layer]]:** + +1. **Контекстное стюардство как продукт.** Клиент покупает не агента — он покупает систему передачи институционального контекста агентам. Это наша ценность: мы знаем, как описать «что живёт только в голове у инженера» в форме, понятной агенту. Это ближе к [[Forward Deployed Engineer]], чем к вендору ПО. + +2. **[[Evals]] как deliverable.** Вместо того чтобы писать [[Evals]] силами джунов, предлагать клиенту услугу: «ваши лучшие эксперты + наш [[Harness]] → корпоративный eval-набор». Это защищает от тихих отказов агентов. + +3. **[[Audit Trails]] как обязательный компонент.** Кейс Григорьева показывает: отсутствие фиксации контекста решений = риск уничтожения данных. Каждый [[Agentic Workflow]] в нашем [[Implementation Layer]] должен включать decision log. + +4. **PE-канал: фрейм «сожаление».** 55% работодателей сожалеют — значит, у них есть болевая точка для разговора. Заход: «Вы уже попробовали агентов. Что пошло не так?» → диагностика контекстного разрыва → наш [[Implementation Layer]]. + +--- + +## Открытые вопросы + +- Какие конкретно из наших клиентов уже столкнулись с «молчаливыми» отказами агентов? Есть ли у нас кейсы, аналогичные Grigorev? +- Как операционализировать «контекстное стюардство» в наш onboarding-процесс для новых клиентов? + +--- + +*Связанные заметки: [[Evals]], [[Agentic Workflow]], [[Implementation Layer]], [[Audit Trails]], [[Forward Deployed Engineer]], [[Harness]]* \ No newline at end of file diff --git a/Business/Nate Corpus/2026-05-18_55-of-employers-regret-ai-driven.transcript.md b/Business/Nate Corpus/2026-05-18_55-of-employers-regret-ai-driven.transcript.md new file mode 100644 index 0000000..f3af361 --- /dev/null +++ b/Business/Nate Corpus/2026-05-18_55-of-employers-regret-ai-driven.transcript.md @@ -0,0 +1,178 @@ +55% of employers regret AI-driven layoffs. The agents are good at tasks and terrible at jobs. 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Please upgrade. +125 +11 +9 +55% of employers regret AI-driven layoffs. The agents are good at tasks and terrible at jobs. Here's what that means for your team and the 3 prompts that close the gap. +The agents are getting better but the people deploying them? That's another story. +Nate +Mar 21, 2026 +∙ Paid +125 +11 +9 +Share +The average software job in America lasts somewhere between 18 months and two years. The average AI agent run lasts about two hours. Even if we lament how short job tenure has gotten, those two numbers are not comparable. And the people who really hold institutional context, who keep a business going, often stay four, five, six, seven years. Maybe longer. +That gap is one of the hardest problems in tech right now, and it is leading a lot of over-optimistic people astray. +Earlier this week I wrote about the +vibe coding wall +, the moment where individual builders who shipped real products with AI agents start hitting failures that better prompting can’t fix. The Grigorev story anchored that piece: a professional engineer whose agent wiped a production database because it didn’t know which infrastructure was real. But as I was writing it, I kept thinking about the bigger version of this problem. Because the wall isn’t just hitting solo builders. It’s hitting teams, departments, entire organizations. The same memory gap that causes an agent to delete a database is causing agents across legal, marketing, finance, and product to make decisions that are locally correct and organizationally catastrophic. +AI agents are getting extraordinarily good at doing work. They can write code, generate designs, close tickets, build financial models. The capability trajectory is real and accelerating. But they still have short-term memories over the arc of a real job, and they still lack common sense in weird, unpredictable ways. That combination, increasingly powerful but still brittle, means they are getting more destructive when they aren’t managed well. Not less. A mediocre tool that fails obviously is annoying. A powerful tool that fails silently is dangerous. +Two weeks ago, Alexey Grigorev’s AI coding agent wiped his production database. 1.9 million rows of student data, gone in seconds. The backups disappeared too. The agent never made a technical error. Every action was locally correct. It simply had no idea it was demolishing a live system, because the knowledge that distinguished real infrastructure from temporary copies existed only in the engineer’s head. Three new studies confirm this isn’t a fluke. It’s the pattern. And what the pattern reveals about the future of work, for engineers, for marketers, for lawyers, for anyone handing consequential tasks to an agent, is not what you’re hearing from most of the AI discourse. +Here’s what’s inside: +The task-versus-job gap. +Two benchmarks measuring the same AI models got wildly different results. The reason explains almost everything confusing about the current agent discourse. +What Cursor’s team actually did. +The part of the “AI built Excel from scratch” story that nobody talks about, and why it matters more than the demo. +The invisible skill the labor market is already paying for. +Harvard studied 62 million workers. The data doesn’t say what the headlines claim. +Why your best people should be writing evals, not your most junior. +The single highest-leverage investment most organizations are getting exactly backwards. +Contextual stewardship. +A framework for the human role that’s emerging whether we name it or not, and how to start building it this week. +Three prompts to start tonight. +A context gap audit, an eval writer for non-engineers, and a decision documenter that captures the reasoning your agents will never know on their own. +The best tools we have for managing agent risk are human brains and human brains crafting evaluations. Not better prompts. Not bigger context windows. Human judgment about what matters, what’s fragile, and what the AI doesn’t know it doesn’t know. Let me show you why. +Subscribers get all posts like these! +Subscribe +Listen to this episode with a 7-day free trial +Subscribe to +Nate’s Substack +to listen to this post and get 7 days of free access to the full post archives. +Start trial +Already a paid subscriber? +Sign in +Nate's Notebook +Welcome to my podcast! In these audio reviews of my newsletters, I am to break down complex AI topics in a way that's approachable and relatable. I want you to walk away with the confidence to leverage AI more effectively at home and at work! +Welcome to my podcast! In these audio reviews of my newsletters, I am to break down complex AI topics in a way that's approachable and relatable. I want you to walk away with the confidence to leverage AI more effectively at home and at work! +Subscribe +Listen on +Substack App +RSS Feed +Appears in episode +Nate +Recent Episodes +Executive Briefing: Stop asking if AI can do this. Start asking what shape the work is. +21 hrs ago +• +Nate +Exclusive: a conversation with Tibo from Codex on what your company has to become when the model can actually do the work +May 16 +• +Nate +The 2 prompts I'd run before any 2026 SaaS renewal (especially if you're deploying agents) +May 15 +• +Nate +Six things have to be true before AI changes a workflow. Most companies have built two. +May 14 +• +Nate +Your AI agent is rediscovering 85% of its context every run. Here's the architecture fix (+ Contract Spec, Failure Triage, and Stack ADR) +May 13 +• +Nate +Six layers your agent has to handle. Most products have only thought about two. + a responsibility-layer audit. +May 12 +• +Nate +You gave your AI agent real tools. 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A powerful tool that fails silently is dangerous. +Two weeks ago, Alexey Grigorev\u2019s AI coding agent wiped his production database. 1.9 million rows of student data, gone in seconds. The backups disappeared too. The agent never made a technical error. Every action was locally correct. It simply had no idea it was demolishing a live system, because the knowledge that distinguished real infrastructure from temporary copies existed only in the engineer\u2019s head. Three new studies confirm this isn\u2019t a fluke. It\u2019s the pattern. And what the pattern reveals about the future of work, for engineers, for marketers, for lawyers, for anyone handing consequential tasks to an agent, is not what you\u2019re hearing from most of the AI discourse. +Here\u2019s what\u2019s inside: +The task-versus-job gap. +Two benchmarks measuring the same AI models got wildly different results. 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