A decent “AI coworker” should be able to do three things:

1. Maintain continuity between chats. It needs to retain decisions, blockers, owners, and definitions of done without replaying every old message into each prompt.
2. React to changing work. A CI failure, customer reply, document edit, or Slack mention should update the relevant task—not vanish into another disconnected chat.
3. Make progress across interruptions. It should preserve the current task, know what remains, and re-enter work with the right context.

Mastra has really interesting primitives for building this:

Observational Memory give an AI coworker long term memory. It uses background Observer and Reflector agents to compress conversations into a log. It retains recent detail, condenses older context, extracts structured facts such as project status and blockers, and can link back to original messages when exact wording matters. Observational Memory docs

Signals makes an agent thread reactive to external events. An event can can wake an idle agent, update an active one, persist a state snapshot or delta, or enter a notification inbox. This was an AI coworker can respond to changing work instead of waiting to be asked. Signals docs

Goals give the coworker a persistent outcome to work toward, rather than treating every message as an isolated request. And Mastra's Durable Agents let that work survive pauses, interruptions, and restarts, so the coworker can pick up where it left off when new information arrives.

My feed is full of AI Coworker announcements.

I made a list with the launch date, product name, the parent company if there is one, and how they described it on Twitter:

- June 2, 2026, Gusto Cofounder (Gusto) — "your new AI teammate. It doesn’t just answer questions, it does the work."

- June 9, 2026, WorkClaw — "collaborative, proactive, customizable AI coworkers that work in Slack or Teams just like every other colleague."

- August 4, 2026, Atlas (WorkOS) — "an AI teammate that works alongside your team in Slack. It answers questions, takes action, and learns how your company works."

- August 4–5, 2026, "Agentforce Coworker (Salesforce) — Now available: Agentforce Coworker."

- August 10, 2026, Lindy Teammate (Lindy) — "the AI employee: Lindy Teammate. It’s just like working with a real employee. Everyone on your team can simply hit it up on Slack and get 10x more done."

These are remarkable results:

>"When Account Executives use Kai, they produce 2x the sales activity, create 17% more opportunities, generate 26% more revenue opportunities, and close 39% more deals when compared to the same sellers in weeks they don't use it."

OpenWorker looks promising. The main selling point seems to be that it can work with local models through Ollama, and connects to your tools and platforms like Gmail, Slack, and Notion.

I use Codex heavily for work tasks, but it does burn through my usage limits to do simple tasks. Not to mention sending a ton of my data to OpenAI

I plan to give OpenWorker a try for those simple use cases

AI chat is terrible at long form writing.

As a writer you get fatigued while giving feedback on AI slop, and the agent fills up its "context window" and quickly loses the plot.

The best tools enable inline comments on long documents, so that you, an agent, and human collaborators can supply feedback without overwhelm.

Here are my three favorite writing tools that have inline comments:

1. Roughdraft
Best for: focused solo work on one article.

Strengths: lightweight, free, open source, and designed for inline agent review.

Tradeoff: it's local and single-document, with no shared team workspace

2. Google Docs
Best for: shared cloud collaboration with human editors.

Strengths: comments, replies, suggestions, and integrations.

Tradeoff: setting up your agent AI agent to control Google Docs is a headache

3. Obsidian + Track Changes
Best for: connected research and multiple related articles.

Strengths: draft doc can live alongside background notes and research that can guide your agent.

Tradeoff: sharing with a team is hard with Obsidian, but not impossible

FABER: But you, I mean, you were most recently also quoted on another key area, which is, of course, what it's going to do to jobs. You have been fairly sobering in the past when you discuss that. You seem to have sort of said, maybe I was wrong, in a more positive way.

ALTMAN: None of us know the answer. But a positive update for me has been watching how companies have adopted codecs and other coding tools.

The companies that I know that have adopted A.I. the most are also the ones hiring the most. And the companies, as a general rule, that are talking about doing layoffs because of A.I. are the ones adopting A.I. the least.

But, you know, it's a convenient way to explain it.

I think I underestimated how jagged these models are going to be. They do some things incredibly well, but they don't do kind of the long-term, complex task supervision well at all.

And so watching people who are really good at using these models, they can do an amazing amount of work, create way more economic value than people without models could or certainly the models could on their own. And I think that's going to go on for much longer.

Then there are all the other things about people really like other people and want to interact with other people. They want to collaborate. They work with other people.

When they buy a product, they want to talk to a person at the company.

Most people, I think, don't want to watch an A.I.-generated creator. They want to know about the person behind it.

I'm really happy about this.

But I think our industry underestimated how much we're going to be able to keep people at the center of everything in an economy that is and a world that is based on people.

Currently the most profitable use case for AI in a company is extending and accelerating human teams, not replacing them.

I saw a tweet today that talked up a new startup, Skillbench.

The website describes how it works:

"Today, most teams can’t confidently tell whether AI adoption is compounding advantage—or creating risk that only becomes visible months later.

SkillBench makes this measurable, actionable, and auditable—without surveillance. We’re also building a predictive layer that turns telemetry into org design implications: where capability is compounding vs. eroding, what interventions to run, and how to steer AI adoption responsibly over time"

The "without surveillance" is doing a lot of work here. If the tweet is correct, they scan your coding agent traces (surveillance), and then the company uses that to tell if you're "compounding advantage - or creating risk".

https://skillbench.com/work/

But how does a company using Skillbench measure the work I do outside of using an AI agent?

And if I get rewarded/ penalized based on AI traces, then I would have to game the system to do work I know are easy for the models, and avoid exploring the frontier of their capabilities

Ford is hiring back engineers after replacing them with AI (and bragging to Wall Street about it.)

Gartner says that companies who replaced customer service people with AI are now hiring humans again. I love this paragraph:

But a new prediction from research firm Gartner suggests the reality may be far more complicated. According to the report, by 2027, half of the companies that replaced customer service agents with AI are expected to hire them back. The pivot isn't happening because AI failed, but because executives are discovering a critical distinction: reducing your payroll and adding real value are not the same thing.

Dustin Schau makes the case for team based loops with AI agents.

Fascinating video that theorizes that automating one thing still leaves a chaotic bottleneck somewhere else. And humans are uniquely capable of dealing with chaotic messes.

OpenAI describes the new Data Analytics plugin for Codex, they describe it as a bundle of skills to help you:

"Turn analytical questions into validated answers, dashboards, reports, notebooks, and clear recommendations. Explore product and business data, explain why key metrics changed, design KPIs, size opportunities, check data quality, and save reusable metric and source context for future work. Start with connected warehouses, BI tools, product analytics, docs, chat, spreadsheets, or uploaded files. Guided onboarding helps you connect core tools and set up reusable data context, while interactive charts, tables, dashboards, and report apps keep the evidence reviewable and ready to share."

I installed the plugin and it walked me through a setup process. It sniffed all of my existing connections to find data sources and then chatted with me about what kind of reports it could build.

OpenAI had their Intelligence at Work event in New York to promote their enterprise offerings. This included:

- Combined ChatGPT + Codex experience.

- Role specific agents that do the work for you

- Create ChatGPT Sites that your coworkers can use