Software "agents" were a hype-y topic when I was a graduate student 25 years ago. I wrote one for a class. I feel like what's being called "agents" or "AI agents" these days are even less capable than what seemed possible a quarter of a century (1) ago when I was in school.
What I thought then is still true today: to make something like a software agent legitimately useful for a lot of people would require a large amount of low-level grunt work and non-technical work (2) of the sort that the typical Silicon Valley company is unwilling to do. (3) The technology is the absolute easiest part of this task. Throwing a Bigger Computer at the problem leaves all those other pieces of work undone. It's like putting a bigger engine in a car with no wheels, hoping that'll make the car go.
By the way #AI companies and VCs, I'm available for contract work and have done due diligence research before if you ever want to stop wasting everyone's time and money!
#AI #GenAI #GenerativeAI #LLM #agents #hype #SiliconValley #VentureCapital #dev #tech
(1) Which we've been told repeatedly is essentially infinite time in the tech world.
(2) Establishing semantic data standards and convincing a large enough number of people to implement them being an important component. LLMs do not magically develop protocols and solve all the ETL-style problems of translating among different ones. The Semantic Web didn't really stick for a lot of reasons, but one reason is that it's hard!
(3) Back when I was still in the startup world I was asked several times by VCs to tell them what I thought about some new startup that claimed to be able to magically clean and fuse data. I think they're still very keen on investing in this style of magic, because it requires an intense amount of human labor, but I think where companies landed was invisibilizing low-paid workers in other countries and pretending a computer did the work they did. Which has also been happening for well over a quarter of a century.
What I thought then is still true today: to make something like a software agent legitimately useful for a lot of people would require a large amount of low-level grunt work and non-technical work (2) of the sort that the typical Silicon Valley company is unwilling to do. (3) The technology is the absolute easiest part of this task. Throwing a Bigger Computer at the problem leaves all those other pieces of work undone. It's like putting a bigger engine in a car with no wheels, hoping that'll make the car go.
By the way #AI companies and VCs, I'm available for contract work and have done due diligence research before if you ever want to stop wasting everyone's time and money!
#AI #GenAI #GenerativeAI #LLM #agents #hype #SiliconValley #VentureCapital #dev #tech
(1) Which we've been told repeatedly is essentially infinite time in the tech world.
(2) Establishing semantic data standards and convincing a large enough number of people to implement them being an important component. LLMs do not magically develop protocols and solve all the ETL-style problems of translating among different ones. The Semantic Web didn't really stick for a lot of reasons, but one reason is that it's hard!
(3) Back when I was still in the startup world I was asked several times by VCs to tell them what I thought about some new startup that claimed to be able to magically clean and fuse data. I think they're still very keen on investing in this style of magic, because it requires an intense amount of human labor, but I think where companies landed was invisibilizing low-paid workers in other countries and pretending a computer did the work they did. Which has also been happening for well over a quarter of a century.