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.
Feels like we’re continuing to trend toward deterministic workflows which may actually be okay in 90% of cases. Reality is there’s a lot of unnecessary token burn happening right now. Simple market dynamics will solve that, i.e., when token cost subsidies begin to fade away and we face the true cost of agent applications.
And I think it’s less about letting agents modify the product source. That’s more of a platform capability which should also be a requirement for certain types of use cases. All comes back to listening to and / or innovating for customers.
“Their relationship with the software is one of pure dependency, and when the software doesn’t do what they need, they just… live with it”
Or, more likely, they churn off the product.
The SaaS platforms that will survive are busy RIGHT NOW revamping their APIs, implementing oauth, and generally reorganizing their products to be discovered and manipulated by agents. Failing in this effort will ultimately result in the demise of any given platform. This goes for larger SaaS companies, too, it’ll just take longer.
Good engineers are way more important than they’ve ever been and the job market tells the story. Engineering job posts are up 10% year over year. The work is changing but that’s what happens when a new technology wave comes ashore. Don’t give up, ride the new wave. You’re uniquely qualified.
> The downside is that the dryRun-flag pollutes the code a bit. In all the major phases, I need to check if the flag is set, and only print the action that will be taken, but not actually doing it.
I think we are both saying similar things here (believe it or not). Sales leaders turnover with surprising frequency - 18-24 months. About the time the sales team tells you what they “want” and you fine tune it, they will be gone. The next person will come in and probably scrap 50-75% of what the prior leader did. New requirements.
Meanwhile, besides functionality, you’ll want/need to plug in the latest and greatest go to market tools for marketing -and demand gen. But… that’ll be a custom effort, too.
Along the way you’ll also realize that you’re missing out on the most common practices in the industry because you built some idiosyncratic tool that only is relevant to your company.
History may very well prove me wrong, but I think you’re underestimating the expertise that underlies these products and platforms. It’s not just code, and the costs of getting it wrong are more than just an engineer’s time. When you waste time in GTM the impacts on the business and valuation are not linear, they’re exponential.
I wish I could upvote this more than once. The author gets it, you have to sell outcomes. Not features. Seems like every open source company that doesn’t market an outcome to buyers will face a similar threat. And this particular go to market strategy was “brittle” before AI.
It’s sort of difficult to understand why this is even a question - LLM-based / judgment dependent workflows vs script-based / deterministic workflows.
In mapping out the problems that need to be solved with internal workflows, it’s wise to clarify where probabilistic judgments are helpful / required vs. not upfront. If the process is fixed and requires determinism why not just write scripts (code-gen’ed, of course).
My gmail address is [email protected]. From time to time (and for years) I get someone else’s at [email protected]. I thought that a Gmail account that was first.last@gmail also allowed for email sent to firstlast@gmail (no period) to reach my inbox as well.
I’ve received some sensitive/PII content over the years.
I’ve wondered if this person has access to any of my information?
Not necessarily related to this post, but wonder why and how this could happen.