RPO remaining performance obligations (not on balance sheet) unless prepaid then its deferred revenue (balance sheet). When it’s consumed it is recognized as revenue.
That’s not the way it works. Google still recognizes the revenue. The cost of goods sold and gross profit are what get hit here but it’s no different than any other partner than can sell their product through the Google marketplace and take advantage of committed spend.
>> “As engineering capacity is no longer a bottleneck, some engineering teams are starting to experience product starvation”
This has always been a problem that Marty Cagan calls “feeding the beast”, but it’s much worse now. Do engineers need to become more cross-functional themselves as well as their product mgt, UX, and quality control counterparts?
> “Then you tried to ship real client work, and the dream fell apart.”
For me it was here. I have tried for months to get AI to write for me and have now fully embraced the fact that LLMs are not ready to do that for me. None of them. I’ve gone back to writing my own content and only using the LLM to brainstorm.
1000%. The expense of writing software was and still is high.
Whether it’s commercial software company or an internal team writing custom software, the return on that investment depends on many things outside of the code itself.
When I initially saw this I was excited because what I’m trying to solve for is visibility of both semi and fully autonomous and agents at runtime. Marble is an improvement over chat and Claude but it’s incremental IMO.
We’ve toyed with a meta agent style interface which you chat to do everything. Add tools, connect datasets, build agents, deploy and schedule them, build skills, etc.
But at some point agents have to have an interface for human approvals, validation, training, and tuning.
I guess it’s like a spreadsheet. Ultimately agents are part of custom apps which are each going to be as unique like snowflakes. So it’s difficult to have one standard abstracted runtime interface without feeling obtuse to a business user. I think what I’m describing and wanting are new app layer building blocks vs one interface to rule them all.
All of this said, I’m all for a better personal llm harness interface (which is what this is). So keep up the great work guys!
For clarity, inference is typically a COGS and therefore hits Gross Margin vs model training which would typically be in OpEx (where R&D lives) and would hit operating margin.
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