Woops, I think my point got lost in my pre coffee thoughts: People are going to have to learn how to build agents at the personal and team levels - being an end user won’t be enough. Those that refuse to learn, will put their jobs at risk (eventually).
Unless companies make bespoke software tools or SaaS programs for different industries that use agentic AI, which I think is likely since there are many industry-specific software tools made externally in B2B contexts, AI and predating AI. There’s already paid AI tools everywhere of debateable levels of value and usefulness.
Non-programmer employees being expected to build agents may happen, but there’s too much of a value proposition for external companies to forgo it. And there’s also a possibility of companies bringing in contract programmers who develop those agents, or using in-house IT departments.
We’re currently in a really awkward early adoption phase where end-users are being given open-ended LLM and agent tools to just play around with, and with or without a crash, I do see the industry shift toward better software integration where the end-users won’t have to adopt this makeshift developer role. And then the adjustment period would be simple as AI tools start to look more like regular software that’s quick to pick up with minimal risk of skills gaps or being left behind.
I could be wrong, of course, but that’s basically my read on the situation.
Agentic AI is interesting because it has the opportunity to create competitive advantage that other companies cannot replicate. And that’s the promise. Yeah, you can pay someone to build a custom agent, but it’s $50k+ plus maintenance for one agent - and it just doesn’t scale. It’s way cheaper to train up your team who can build many agents.
Companies that delay or avoid training their teams are in for a bad surprise when a new company built around the technology enters the market. It will be one an existential problem that will result in job loss (think Amazon vs brick and mortar).
My general recommendation for workers is to get trained up on agent building - then go get a job at a company investing in their people, because those are the places most likely to survive.
Of course all this will collapse when the bills come due and people have to pay the actual cost of LLM “tokens”. As users of Microsoft’s Copilot found out not long ago.
Woops, I think my point got lost in my pre coffee thoughts: People are going to have to learn how to build agents at the personal and team levels - being an end user won’t be enough. Those that refuse to learn, will put their jobs at risk (eventually).
Unless companies make bespoke software tools or SaaS programs for different industries that use agentic AI, which I think is likely since there are many industry-specific software tools made externally in B2B contexts, AI and predating AI. There’s already paid AI tools everywhere of debateable levels of value and usefulness.
Non-programmer employees being expected to build agents may happen, but there’s too much of a value proposition for external companies to forgo it. And there’s also a possibility of companies bringing in contract programmers who develop those agents, or using in-house IT departments.
We’re currently in a really awkward early adoption phase where end-users are being given open-ended LLM and agent tools to just play around with, and with or without a crash, I do see the industry shift toward better software integration where the end-users won’t have to adopt this makeshift developer role. And then the adjustment period would be simple as AI tools start to look more like regular software that’s quick to pick up with minimal risk of skills gaps or being left behind.
I could be wrong, of course, but that’s basically my read on the situation.
Agentic AI is interesting because it has the opportunity to create competitive advantage that other companies cannot replicate. And that’s the promise. Yeah, you can pay someone to build a custom agent, but it’s $50k+ plus maintenance for one agent - and it just doesn’t scale. It’s way cheaper to train up your team who can build many agents.
Companies that delay or avoid training their teams are in for a bad surprise when a new company built around the technology enters the market. It will be one an existential problem that will result in job loss (think Amazon vs brick and mortar).
My general recommendation for workers is to get trained up on agent building - then go get a job at a company investing in their people, because those are the places most likely to survive.
Of course all this will collapse when the bills come due and people have to pay the actual cost of LLM “tokens”. As users of Microsoft’s Copilot found out not long ago.
I mean, you can put caps on token usage at the user level. Also, you can test while building so you have an estimated cost per run.
When you look at the costs involved, putting caps on means you run out of tokens in your first morning of use per month.
The sticker shock for the Copilot users was a couple orders of magnitude.
We capped everyone at $50/month. You can see how many tokens you use per task my typing /cost. Then you can manage it.
I’m not sure I can justify those other people’s poor/stupid behavior.
And thank you for challenging me on this. If you’re right, there are definitely possible futures to be prepared for.
Oh my goodness yes. I recommend both stockpiling toilet paper and learning how to build agents. That way you won’t be wrong. 😉