Bradford Church

As the CEO of Coworker, a lot of people asked me this week what I think of Claude Cowork... Firstly, the name is an inspired choice – we've drafted a lil letter to let @AnthropicAI know we think so too! That aside: 1. Claude Code's great, but being great at understanding structured code ≠ being great at understanding the messy, unstructured ‘code’ that is company context. Claude Cowork is a wrapper on Claude Code: give it tools, time, and ungodly token use and it'll hack at work tasks. But ask basic things like 'what happened in our all-hands last week' and it spends 2 mins trawling Jira (!?), Notion, and Drive before admitting defeat. The problem is it's the same 'tool maximalist' approach that @OpenAI and Claude enterprise use. Neither work great. Understanding company context is brutal. Conflicting/outdated info, weird data structures, people disagreeing... An agent tool RAGing through that minefield gets blown up by errors and irrelevant data - all the stuff that's already broken enterprise AI trust. Agents do better when they've done their homework. When we connect to company data, we run a stupid amount of models in the background constantly generating a dense context graph. This 1. gives agents the right context quickly, and 2. info on how to operate: 'how does this company structure Salesforce', 'who works on what', 'which conflicting source is correct.' 2. Speed matters. We moved away from the Claude Code architecture b/c we found business users are hyper sensitive to speed (<10 secs outputs vs meandering through tools). 3. The consensus view that foundational players ultimately win enterprise AI is wrong. We're big fans of Anthropic, but the 'Claude Code built Claude Cowork in 1.5 weeks' brag is actually a bearish indicator for how seriously they're taking this category. Their 'prep my day' can't yet connect to calendar, email or Slack. 'Organize my desktop' as a hero use case lacks user understanding– there's a reason no-one's done this since 2002: work happens in the cloud, not your machine. It’s the same reason ChatGPT Enterprise is subpar and why @glean dominates the space despite being 'just fine.' There's a fundamental incentive misalignment between foundational labs and companies. Most companies are hedging across model providers. They want systems that maintain context across agent infrastructure with the flexibility to swap models. That's a path to commoditization for foundational, and an opportunity for 'neutral' operating systems to manage context and route workloads to the ‘cheapest cost per successful task’ model (many will be open-source). After all, you wouldn't let your electricity provider control your thermostat. True superintelligence will emerge when companies can deploy lightning fast agent swarms with shared, learned context across enterprise data. No platform is close to that today. But our team at @coworkerapp is squarely focused on the exact enterprise neurosurgery that'll deliver it.

Introducing: Deep Work by @coworkerapp Start any project 80% complete with AI that has deep company context and memory. Check it out: coworker.ai/deep-work

@PalmerLuckey @usevillage built a synthetic data layer called “organizational memory” (OM1) to solve this. RAG is dead (if it was ever alive).

What is currently the best solution for turning a large collections of personal notes and chats into an LLM-interrogable dataset? It seems like most off-the-shelf options can't keep context at scale.

These agents pre-synthesize and summarize data so that responses are richer and full of context.

How can our results and question synthesis top Glean or other enterprise search tools? The word "agentic" makes me want to die inside (overused). But the reason Village's version of search is better is bc we've architected AI agents into the data management process.

[Day 1/5] Why we didn't take vacay this year: Village is your new 10x enterprise search. Last week was our biggest launch ever. Here's why (part 1/5). Village is now your new 10x enterprise search solution. 1. Find anything across any app

Forget about AGI for a moment. LLMs are the modern equivalent of early text based command interfaces. They’re a big improvement on punch cards, but a far cry from the graphical interfaces that changed personal computing. What’s our GUI? Voice? Or something more abstract?

Companies spend $30k per employee/year on meetings. It's insane. We built an AI that eliminates 50% of them👇

Companies are addicted to meetings. We looked at the data: it's a seriously unhealthy addiction 👇

The Interchain @BuildersProgram is an initiative to support teams and early stages projects building on the #Interchain Stack. Today, we are thrilled to announce the program's 2023 Q1 participants cohort. Read & discover the new teams entering #Cosmos medium.com/the-interchain…

If you’re a CEO, don’t find yourself on the scaffold–reach out to us if you’re interested.

We are here to make the platform black box transparent, trustful, and less expensive. We’re here to make users, for the first time, aligned with the long-term success of a business.

I’ve worked for almost a decade at platform companies like Uber, and have interviewed 100+ CEOs & founders in the space. My biggest takeaway? Deep down, all platform execs are afraid of being dragged to the guillotine by their users. But it doesn’t have to be this way. 💀👇