“It might be down to the time of year; it’s always quieter in the summer months but it feels a bit different right now.
Firstly, it feels like there has been a BIG pause because of ChatGPT and other LLMs. It feels like people are still getting their heads round what they can do, their effectiveness, quality, etc. And when they do look at it, they don’t ‘get’ how they’ll use it.” —Andrew Jacobs 2024-08-09
I have witnessed this same malaise in the business world for the past year. If it’s not an AI initiative, it does not get any attention. The bad and the ugly aspects of this new flavour of machine learning are dominating the IT sector and all it touches. Here are some recent examples shared in our community of practice.
From Burnout to Balance: AI-Enhanced Work Models
However, this new technology [generative AI] has not yet fully delivered on this productivity promise: Nearly half (47%) of employees using AI say they have no idea how to achieve the productivity gains their employers expect, and 77% say these tools have actually decreased their productivity and added to their workload.
AI models are being blocked from fresh data — except the trash
As AI crawlers are increasingly blocked from high-quality news sites, they’ll turn to low-quality sites and fill up on garbage and misinformation — and that’s what they’ll be spitting out.
AI-obsessed bosses are about to get a rude awakening
The railway mania of the 1840s left behind new infrastructure. The fibre boom of the 1990s connected the world. There was barely a blip before those assets were being used again.
But spending $1 trillion on data centres will look very foolish in a few year’s time when chips are four generations more powerful. This is capital incineration on a vast scale. Fear of losing out has driven the industry insane.
The rude awakening cannot come soon enough, so we can address the many complex challenges facing all organizations today. The reversal described in the image below may be just around the corner.
I looked at Gen AI using McLuhan’s media tetrad, which states that every medium:
- extends a human property,
- obsolesces the previous medium (& often makes it a luxury good),
- retrieves a much older medium, &
- reverses its properties when pushed to its limits
Generative Al
EXTENDS each voice & thought — mimics creativity
OBSOLESCES human thinking & writing — bullshit by design
RETRIEVES 19th century imperialism — Neo-feudalism
REVERSES into one more bubble burst — collapsed data-centres
Ooo Harold – appreciate your applying and sharing via McLuhan’s tetrad , esp. your “RETRIEVES” is provocative.
Thanks, Shirley! I see that the current AI arms race is all about Big Tech and that small players (the peasants) don’t stand a chance.
You have articulated what I have been struggling to say elegantly.
AI for the sake of AI will only bring in more trouble than we can handle. When I look at any technology, here is what I think about:
1. What significant problem does my business currently face? How can I either eliminate it, solve it or make it irrelevant?
2. What significant opportunity is the business missing out due to technological progress and our lack of expertise on this? This requires applied imagination to answer well.
3. What workarounds (that exists within my business) can I eliminate by deploying new technology?
I also created a model for identifying problems worth solving – SCOWL:
S – Strategic Deployment that helps us execute the strategy better.
C – Cracks in Information Flow. Aim is to get the right info to the right person in the right form at the right time
O – Overwhelmed: Deploy technology to remove overwhelm wherever we see in our business
W – Workarounds: Deploy technology to remove workarounds in our business processes.
L – Listed Projects: Deploy what your businesses asks you to deploy.
More on this @ https://www.linkedin.com/video/live/urn:li:ugcPost:6919193363845955584/
Thanks for this.
Thanks, Mukesh. I like your SCOWL model!
Deploy technology to remove workarounds! Hah! Good luck with that. The human spirit will always require workarounds with anything they are presented – even AI.
I think it’s still good to identify and understand workarounds as potential performance improvement measures, especially if the workaround is due to some stupid rule.