Just finished north of 200 meetings in Europe with customers and technologists. The conversations were primarily around AI, common questions include:
1. Are there examples of organizations who have been able to demonstrate production level systems and do those developments show a return in lower cost, efficiency or better top line?
2. What do you think about agents? How will we discover, govern and stop agents if need be. Perhaps the biggest security concern ATM.
3. The frontier AI models are expensive, what's the business case at these token prices to embed AI in our customer facing products? Where will token prices be in the future.
4. What are the longer term implications of Mythos like models? Do we need to update cyber infrastructure or all IT infrastructure?
5. What do you think of Chinese opensource models? Are they secure and what is the downside of using them if they can be secured and they are cheaper?
The parts that surprised me were:
1. The pausing of Mythos and Fable 5 caused more consternation and concern in Europe both short term and raised longer term concerns on single model reliance or reliance or models not in ones control. I hadn't seen it from their POV.
2. Sovereignity which was always a topic and still is, is getting more nuanced - they want data residency, data localization and local resources, but there seems to be more willingness to accept global services on clouds. Classified systems continue to be an issue.
Net net - we need to ensure we continue to build trust both on our Frontier models and their consistent availability, we need to get the right economics in place and spend more time in Europe communicating and building presence if we want AI adoption to keep pace with the US.
I wonder if people who are using whispr flow or equivalent 24x7, for everything are working alone at home or some place?
Because if you work out of an office or a cafe, it becomes borderline embarrassing when you are talking to your machine for hours.
“don’t train your own model” is common ai advice. it's wrong. your token bill's the proof.
today, we’re excited to launch castform into open preview. castform is the easiest way for you to train your own model, on your own data.
open-weights models are performant and much cheaper. when trained on your task & proprietary data, they beat closed models. the thing standing between you and that was weeks of plumbing & years of ml expertise.
with castform, model training is as simple as prompt engineering. @castformai
bring your agent traces or raw corpora. castform turns it into training data, picks the right algorithmic recipes, manages gpus, and gives you an ide to watch and chat with your model as it learns.
see what you can build with castform👇
It's fascinating to see what was once undergrad's final year project for which I had to grind hard at late nights is now a prompt for claude code! We have come extremely far. Love it!
This was inevitable.
Every serious AI company is going to need a services layer eventually. Implementation just does not look the same from one customer to the next, especially in business.
Even prosumers increasingly want someone to drive the tool for them and hand back a finished result. The self-serve dream has limits.
We see this at @narrationbox every week. A lot of authors do not want to log in and learn a new platform. They want us to take the book and deliver a finished audiobook, production and all.
Going to be interesting to see which AI companies lean into this and which keep pretending it is a pure software business.
For engineers reading this: getting good at customer conversations is probably the most underrated career move right now.