Mark Carder

Another week on the road meeting with a couple dozen IT and AI leaders from large enterprises across banking, media, retail, healthcare, consulting, tech, and sports, to discuss agents in the enterprise. Some quick takeaways: * Clear that we’re moving from chat era of AI to agents that use tools, process data, and start to execute real work in the enterprise. Complementing this, enterprises are often evolving from “let a thousand flowers bloom” approach to adoption to targeted automation efforts applied to specific areas of work and workflow. * Change management still will remain one of the biggest topics for enterprises. Most workflows aren’t setup to just drop agents directly in, and enterprises will need a ton of help to drive these efforts (both internally and from partners). One company has a head of AI in every business unit that roles up to a central team, just to keep all the functions coordinated. * Tokenmaxxing! Most companies operate with very strict OpEx budgets get locked in for the year ahead, so they’re going through very real trade-off discussions right now on how to budget for tokens. One company recently had an idea for a “shark tank” style way of pitching for compute budget. Others are trying to figure out how to ration compute to the best use-cases internally through some hierarchy of needs (my words not theirs). * Fixing fragmented and legacy systems remain a huge priority right now. Most enterprises are dealing with decades of either on-prem systems or systems they moved to the cloud but that still haven’t been modernized in any meaningful way. This means agents can’t easily tap into these data sources in a unified way yet, so companies are focused on how they modernize these. * Most companies are *not* talking about replacing jobs due to agents. The major use-cases for agents are things that the company wasn’t able to do before or couldn’t prioritize. Software upgrades, automating back office processes that were constraining other workflows, processing large amounts of documents to get new business or client insights, and so on. More emphasis on ways to make money vs. cut costs. * Headless software dominated my conversations. Enterprises need to be able to ensure all of their software works across any set of agents they choose. They will kick out vendors that don’t make this technically or economically easy. * Clear sense that it can be hard to standardize on anything right now given how fast things are moving. Blessing and a curse of the innovation curve right now - no one wants to get stuck in a paradigm that locks them into the wrong architecture. One other result of this is that companies realize they’re in a multi-agent world, which means that interoperability becomes paramount across systems. * Unanimous sense that everyone is working more than ever before. AI is not causing anyone to do less work right now, and similar to Silicon Valley people feel their teams are the busiest they’ve ever been. One final meta observation not called out explicitly. It seems that despite Silicon Valley’s sense that AI has made hard things easy, the most powerful ways to use agents is more “technical” than prior eras of software. Skills, MCP, CLIs, etc. may be simple concepts for tech, but in the real world these are all esoteric concepts that will require technical people to help bring to life in the enterprise. This both means diffusion will take real work and time, but also everyone’s estimation of engineering jobs is totally off. Engineers may not be “writing” software, but they will certainly be the ones to setup and operate the systems that actually automate most work in the enterprise.

Be honest

Hmm 😒 🤔?¿

Either everyone on Twitter is right about the future of AI, or we’re observing a fantastic mass psychosis event.

The opportunity to onboard "normal people" to the latest AI is much bigger than I originally thought. Honestly, $100k+ per month feels low. In a high income city, it could be a $10m+ business. To validate it, I tried to stand up an AI Assistant by myself (as a tech novice). It was painful. Here's what I did: 0. Phoned a tech friend to get basic steps 1. Bought a Mac Mini 2. Created a Claude Developer Account 3a. Factory reset old iPhone 3b. Created a new phone line for iPhone 4. Created a new email address 5. Created a new iCloud account 6. Used EasyClaw for setup That took me about six hours, but I had a functional AI assistant by the end of it. It was fun feeling like an idiot. I like being an embarrassing beginner. A bunch of pain points to solve (for anyone who wants to build in this space): - I had no idea what I needed. I called a friend and annoyed him for an hour to figure that out in the first place. People don't know what they don't know. - I don't know what "Terminal" is and had never used it. Running commands there was totally foreign to me and I made dumb mistakes (like thinking it wasn't working because I was typing my password and it wasn't showing). - I really wanted to understand security and how to keep this new unit completely walled off from my other systems. It took me a while to make sure I was doing all of that properly and how to maintain that integrity going forward. - I had no understanding of tokens, usage limits, and how to think about that usage going forward. - Connections and integrations of tools (like iMessage) were not intuitive at all. - No understanding of best practices for prompting, training, etc. The ongoing improvements would be great as a recurring stream after the initial deployment. Those are just my initial reactions off the top of my head. I'm sure I'll have more as I continue to play with it. P.S. We're definitely living in the future. I got my AI Assistant to text my wife that I was coming down for dinner. She rolled her eyes at me when I got downstairs. THE FUTURE PEOPLE!