Looks like we might be hitting the end of the #HypeCycle of #AI

https://youtu.be/acT_PSAZ7BQ?si=Qk8dlEyegdubFmqh

The three structural trends shaping the AI crisis in higher education

What do I think follows from these for what universities do under present conditions?

• We can’t lock in reliably until the post-crash/IPO pricing models are much clearer than they are now. Otherwise we’re embedding products for we can reasonably expect the prices to be ratcheted up a few years down the line.
• The prospect for securing the existing assessment system is extremely limited in the medium term and the long term. It’s not going to be possible to separate out technological practice from non-technological practice in the manner which assessment security presupposes. This means that we urgently need to begin working towards assessment reform.
• The manner in which we respond to the first two challenges will be shaped by the financial and political pressures the sector is under. A dash for productivity through automation risks locking in unreliable system and incurring much greater costs later, as well as further undermining assessment integrity in a way which accelerates the declining (perceived) value of our degrees. A failure to address assessment integrity (and to be seen to do so) furthermore hands ammunition to critics of the sector for whom ‘ChatGPT degrees’ will figure alongside ‘woke degrees’ as economc criticism fuses with culture war criticism.

Just got an email that made me a bit sad

"... we want to let you know that starting on June 9, these courses will be available exclusively on MIT Learn, MIT’s new AI-enabled online learning platform."

Really? You need to replace a working web site that delivers basic content with something "AI-enabled"?

So glad I'm on the tail end of my time in tech.

#AI #HypeCycle

Every few years, the industry rediscovers a familiar truth: complexity does not disappear, it relocates.

We rename patterns, rebuild the same structures with better tooling, then act surprised when the old trade-offs return. Sometimes they do improve. Often the real progress is simply that we have better ways to observe and contain failure.

History does not make you cynical.
It makes you harder to sell to.

#SoftwareHistory #Tech #SystemsThinking #SoftwareEngineering #HypeCycle #ByernNotes

Every trend follows the same choreography:
excitement, overuse, disappointment, blog posts, courses, certifications, rebranding.

The technology changes.
The language changes.
The choreography doesn’t.
The incentives rarely do.

Eventually, we rediscover restraint and call it a new paradigm.

#TechTrends #HypeCycle #SoftwareIndustry #MetaTech #ByernNotes

Every new tool promises to reduce complexity.
Most of them succeed by relocating it somewhere else and calling it abstraction.

Instead of fewer problems, you get problems with different failure modes, new vocabulary, and better marketing.
The system looks simpler until something goes wrong.

The bill always arrives.
It’s just rarely itemized.

#SoftwareArchitecture #Tooling #HypeCycle #EngineeringJudgment #TechCulture #ByernNotes

I’m not anti-cloud, anti-AI, or anti-modern tooling.
I am anti-unexamined defaults.

Every abstraction optimizes for something.
Cost, scale, speed, control, ownership, responsibility.
If you don’t know what a system optimizes for, you are probably paying for it somewhere else.

Skepticism is not negativity.
It’s how engineers stay employed.

#SoftwareArchitecture #TechSkepticism #HypeCycle #SystemsDesign #EngineeringJudgment #TechCulture #CriticalThinking #ByernNotes

Every few years the industry discovers a new way to rename “autocomplete” and call it a revolution.
This time it’s AI agents, last time it was microservices, before that SOA, before that CORBA.
Same problems, same trade-offs, better GPUs.
Still fun though!

#AI #HypeCycle #TechCulture #TechTrends #TechIndustry #SoftwareHistory #SoftwareEngineering