Along similar lines, the economist John Kenneth Galbraith has a book on financial bubbles and irrational crowd mentalities in financial market over the centuries: A Short History of Financial Euphoria [1]. Short, approachable, and interesting.
Garfield Minus Garfield is a site dedicated to removing Garfield from the Garfield comic strips in order to reveal the existential angst of a certain young Mr. Jon Arbuckle. It is a journey deep into the mind of an isolated young everyman as he fights a losing battle against loneliness and depression in a quiet American suburb.
The world is too much with us; late and soon,
Getting and spending, we lay waste our powers;—
Little we see in Nature that is ours;
We have given our hearts away, a sordid boon!
This Sea that bares her bosom to the moon;
The winds that will be howling at all hours,
And are up-gathered now like sleeping flowers;
For this, for everything, we are out of tune;
It moves us not. Great God! I’d rather be
A Pagan suckled in a creed outworn;
So might I, standing on this pleasant lea,
Have glimpses that would make me less forlorn;
Have sight of Proteus rising from the sea;
Or hear old Triton blow his wreathèd horn.
Sometimes you don't know what needs to be built until you build it. These end-to-end prototypes are how to enhance your understanding and develop deeper intuition about possibilities, where risks lie, etc.
Much of my career has been spent in teams at companies with products that are undergoing the transition from "hip app built by scrappy team" to "profitable, reliable software" and it is painful. Going from something where you have 5 people who know all the ins and outs and can fix serious bugs or ship features in a few days to something that has easy clean boundaries to scale to 100 engineers of a wide range of familiarities with the tech, the problem domain, skill levels, and opinions is just really hard. I am not convinced yet that AI will solve the problem, and I am also unsure it doesn't risk making it worse (at least in the short term)
“””
This perspective is crucial. Scale is the great equalizer / demoralizer, scale of the org and scale of the systems. Systems become complex quickly, and verifiability of correctness and function becomes harder. Companies that built from day with AI and have AI influencing them as they scale, where does complexity begin to run up against the limitations of AI and cause regression? Or if all goes well, amplification?
The more verifiable the domain the better suited. We see similar reports of benefits from advanced mathematics research from Terrence Tao, granted some reports seem to amount to very few knew some data existed that was relevant to the proof, but the LLM had it in its training corpus. Still, verifiably correct domains are well-suited.
So the concept formal verification is as relevant as ever, and when building interconnected programs the complexity rises and verifiability becomes more difficult.
Fair enough! I do like how others are framing this is as "write less code" -- if Rue makes one think more and more about the code that finally makes it to the production, that can be a real win.
"Growth" of those being led is a key concept it seems, which I would think is really only possible when the leader doesn't do everything by themselves as a die-hard servant, but utilizes the "leadership" part to help subordinates learn to lead themselves.
Granted this realm of ideas can be a gray-area, but it seems like servant leadership as presented by the author here does not incorporate the concept of growing those that they lead -- as indicated by the fact they have self-invented a new "buzzword" which actually seems to be involve the behaviors as laid out by servant leadership -- am I missing something?
Essentially, what information are they privy to that public is not? What asymmetry exists (timing, un-public information)? Is there any way for the public to be nearly as informed? What are they trading on? Upcoming funding changes (more money here -> buy, less money there --> sell)? COVID impact stands out.
Where is AI actually selling and doing well? What's a good resource for these numbers? What are the smaller scale use-cases where AI is selling well?
I am generally curious, because LLMs, VLMs, generative AI, advances are proving useful, but the societal impact scale and at this the desired rate is not revealing itself.
[1] https://www.goodreads.com/en/book/show/270746.A_Short_Histor...