A “moat” historically meant a physical method to reduce threats to a group intended to fit inside it. Take for example the large Buhen fortress on the banks of the Nile. Built by Pharaoh Senwosret III around 1860 BCE, it boasted a high-tech ten meter high wall next to a three meter deep moat to protect his markets against Nubians who were brave enough to fight against occupation and exploitation.

Complicated, I suppose, since being safe inside such a moat meant protection against threats, yet being outside was defined as being a threat.

Go inside and lose freedom, go outside and lose even more? Sounds like Facebook’s profit model can be traced back to stone tablets.

Anyway, in true Silicon Valley fashion of ignoring complex human science, technology companies have been expecting to survive an inherent inability to scale by relying on building primitive “moats” to prevent groups inside from escaping to more freedom.

Basically moats used to be defined as physically protecting markets from raids, and lately have been redefined as protecting online raiders from markets. “Digital moats” are framed for investors as a means to undermine market safety — profit from users enticed inside who then are denied any real option to exit outside.

Unregulated highly-centralized proprietary technology brands have modeled themselves as a rise of unrepresentative digital Pharoahs who are shamelessly attempting new forms of indentured servitude despite everything in history saying BAD IDEA, VERY BAD.

Now for some breaking news:

Google has been exposed by an alleged internal panic memo about profitability of future servitude, admitting “We Have No Moat, And Neither Does OpenAI”

While we’ve been squabbling, a third faction has been quietly eating our lunch. I’m talking, of course, about open source. Plainly put, they are lapping us. Things we consider “major open problems” are solved and in people’s hands today. […] Open-source models are faster, more customizable, more private, and pound-for-pound more capable. They are doing things with $100 and 13B params that we struggle with at $10M and 540B. And they are doing so in weeks, not months.

It’s absolutely clear this worry and fret from Google insiders comes down to several key issues. The following paragraph in particular caught my attention since it feels like I’ve been harping about this for at least a decade already:

Data quality scales better than data size
Many of these projects are saving time by training on small, highly curated datasets. This suggests there is some flexibility in data scaling laws. The existence of such datasets follows from the line of thinking in Data Doesn’t Do What You Think, and they are rapidly becoming the standard way to do training outside Google.

There has to be common sense about this. Anyone who thinks about thinking (let alone writing code) knows a minor change is more sustainable for scale than complete restarts. The final analysis is that learning improvements grow bigger faster and better through fine-tuning/stacking on low-cost consumer machines instead of completely rebuilding upon each change using giant industrial engines.

…the model can be cheaply kept up to date, without ever having to pay the cost of a full run.

You can scale a market of ideas better through a system designed for distributed linked knowledge with safety mechanisms, rather than planning to build a new castle wall every time a stall is changed or new one opened.

Building centralized “data lakes” was a hot profit ticket in 2012 that blew-up spectacularly just a few years later. I don’t think people realized social science theory like “fog of war” had told them not to do it, but they definitely should have walked away from “largest” thinking right then.

Instead?

OpenAI was born in 2015 on the sunset phase of a wrong model mindset. Fun fact: I once was approached and asked to be CISO for OpenAI. Guess why I immediately refused and instead went to work on massively distributed high-integrity models of data for AI (e.g. W3C Solid)?

…maintaining some of the largest models on the planet actually puts us at a disadvantage.

Yup. Basically confidentiality failures that California breach law SB1386 hinted at way back in 2003, let alone more recent attempts to stop integrity failures.

Tech giants have vowed many times to combat propaganda around elections, fake news about the COVID-19 vaccines, pornography and child exploitation, and hateful messaging targeting ethnic groups. But they have been unsuccessful, research and news events show.

Bad Pharaohs.

Can’t trust them, as the philosopher David Hume sagely warned in the 1700s.

To me the Google memo reads as if pulled out of a dusty folder: an old IBM fret that open communities running on Sun Microsystems (get it? a MICRO system) using wide-area networks to keep knowledge cheaply up to date… will be a problem for mainframe profitability that depends on monopoly-like exit barriers.

Exiting times, in other words, to be working with open source and standards to set people free. Oops, meant to say exciting times.

Same as it’s ever been.

There is often an assumption that operations should be large and centralized in order to be scalable, even though such thinking is provably backwards.

I suspect many try to justify such centralization due to cognitive bias, not to mention hedging benefits away from a community and into a just small number of hands.

People sooth fears through promotion of competition-driven reductions; simple “quick win” models (primarily helping themselves) are hitched to a stated need for defense, without transparency. They don’t expend effort on wiser, longer-term yet sustainable efforts of more interoperable, diverse and complex models that could account for wider benefits.

The latter models actually scale, while the former models give an impression of scale until they can’t.

What the former models do when struggling to scale is something perhaps right out of ancient history. Like what happens when a market outgrows the slowly-built stone walls of a “protective” monarchist’s control.

Pharoahs are history for a reason.

See what I did there? It worries me that too many people are forgetting almost nobody really has been able to tell what is true since… forever. I gave a great example of this in 2020: Abraham Lincoln.

A print of abolitionist U.S. President Abraham Lincoln was in fact a composite, a fake. Thomas Hicks had placed Lincoln’s unmistakable head on the distinguishable body of Andrew Jackson’s rabidly pro-slavery Vice President John Calhoun. A very intentionally political act.

The fakery went quietly along until Stefan Lorant, art director for London Picture Post magazine, noticed a very obvious key to unlock Hick’s puzzle — Lincoln’s mole was on the wrong side of his face.

Here’s a story about Gary Marcus, a renowned AI expert, basically ignoring all the context in the world:

His immediate concern is that the internet will be flooded with false photos, videos and text, and the average person will “not be able to know what is true anymore.”

Will be flooded?

That is literally what the Internet has done since its origin. The printing press flooded the average person. The radio flooded the average person. It’s not like the Internet, in true reinvention of the wheel fashion, grew in popularity because it came with an inherent truth filter.

The opposite, bad packets were always there for bad actors and — despite a huge amount of money invested for decades into “defenses” — many bad packets continue to flow.

Markets always are dangerous, deceptive places if left without systems of trust formed with morality (as philosopher David Hume explained rather clearly in the 1700s, perhaps too clearly given the church then chastised him for being a thinker/non-believer).

So where does our confidence and ability to move forward stem from? Starting a garden (pun not intended) requires a plan to assess and address, curate if you will, risky growth. We put speed limiters into use to ensure pivots, such as making a turn or changing lanes, won’t be terminal.

Historians, let alone philosophers and anthropologists, might even argue that having troubles with the truth has been the human condition across all communication methods for centuries if not longer. Does my “professor of the year” blog post from 2008 or the social construction of reality ring any bells?

Americans really should know exactly what to do, given it has such a long history of regulating speech with lots of censorship; from the awful gag rule to preserve slavery, or banning Black Americans from viewing Birth of a Nation, all the way to the cleverly named WWII Office of Censorship.

What’s that? You’ve never heard of the U.S. Office of Censorship, or read its important report from 1945 saying Americans are far better off because of their work?

This is me unsurprised. Let me put it another way. Study history when you want to curate a better future, especially if you want to regulate AI.

Not only study history to understand the true source of troubles brewing now, growing worse by the day… but also to learn where and how to find truth in an ocean of lies generated by flagrant charlatans (e.g. Tesla wouldn’t exist without fraud, as I presented in 2016).

If more people studied history for more time we could worry less about the general public having skills in finding truth. Elon Musk probably would be in jail. Sadly the number of people getting history degrees has been in decline, while the number of people killed by a Tesla skyrockets. Already 19 dead from Elon Musk spreading flagrant untruths about AI. See the problem?

The average person doesn’t know what is true, but they know who they trust; a resulting power distribution is known by them almost instinctively. They follow some path of ascertaining truism through family, groups, associations, “celebrity” etc. that provide them a sense of safety even when truth is absent. And few (in America especially) are encouraged to steep themselves in the kinds of thinking that break away from easy, routine and minimal judgment contexts.

Just one example of historians at work is a new book about finding truth in the average person’s sea of lies, called Myth America. It was sent to me by very prominent historians talking about how little everyone really knows right now, exposing truths against some very popular American falsehoods.

This book is great.

Yet who will have the time and mindset to read it calmly and ponder the future deeply when they’re just trying to earn enough to feed their kids and cover rising medical bills to stay out of debtor prison?

Also books are old technology so they are read with heaps of skepticism. People start by wondering whether to trust the authors, the editors and so forth. AI, as with any disruptive technology in history, throws that askew and strains power dynamics (why do you think printing presses were burned by 1830s American cancel culture?).

People carry bias into their uncertainty, which predictably disarms certain forms of caution/resistance during a disruption caused by new technology. They want to believe in something, swimming towards a newly fabricated reality and grasping onto things that appear to float.

It is similar to Advanced Fee Fraud working so well with email these days instead of paper letters. An attacker falsely promises great rewards later, a pitch about safety, if the target reader is willing (greedy) to believe in some immediate lies coming through their computer screen.

Thus the danger is not just in falsehoods, which surround us all the time our whole lives, but how old falsehoods get replaced with new falsehoods through a disruptive new medium of delivery: fear during rapid changes to the falsehoods believed.

What do you mean boys can’t wear pink, given it was a military tradition for decades? Who says girls aren’t good at computers when they literally invented programming and led the hardware and software teams where quality mattered most (e.g. Bletchly Park was over 60% women)?

This is best understood as a power shift process that invites radical even abrupt breaks depending on who tries to gain control over society, who can amass power and how!

Knowledge is poweful stuff; creation and curation of what people “know” is often thus political. How dare you prove the world is not flat, undermining the authority of those who told people untruths?

AI can very rapidly rotate on falsehoods like the world being flat, replacing known and stable ones with some new and very unstable, dangerous untruths. Much of this is like the stuff we should all study from way back in the late 1700s.

It’s exactly the kind of automated information explosion the world experienced during industrialization, leading people eventually into world wars. Here’s a falsehood that a lot of people believed as an example: fascism.

Old falsehoods during industrialization fell away (e.g. a strong man is a worker who doesn’t need breaks and healthcare) and were replaced with new falsehoods (e.g. a strong machine is a worker that doesn’t need strict quality control, careful human oversight and very narrow purpose).

The uncertainty of sudden changes in who or what to believe next (power) very clearly scares people, especially in environments unprepared to handle surges of discontent when falsehoods or even truths rotate.

Inability to address political discontent (whether based in things false or true) is why the French experienced a violent disruptive revolution yet Germany and England did not.

That’s why the more fundamental problem is how Americans can immediately develop methods for reaching a middle ground as a favored political position on technology, instead of only a left and right (divisive terms from the explosive French Revolution).

New falsehoods need new leadership through a deliberative and thoughtful process of change, handling the ever-present falsehoods people depend upon for a feeling of safety.

Without the U.S. political climate forming a strong alliance, something that can hold a middle ground, AI can easily accelerate polarization that historically presages a slide into hot war to handle transitions — political contests won by other means.

Right, Shakespeare?

The poet describes a relationship built on mutual deception that deceives neither party: the mistress claims constancy and the poet claims youth.

When my love swears that she is made of truth
I do believe her though I know she lies,
That she might think me some untutored youth,
Unlearnèd in the world’s false subtleties.
Thus vainly thinking that she thinks me young,
Although she knows my days are past the best,
Simply I credit her false-speaking tongue;
On both sides thus is simple truth suppressed.
But wherefore says she not she is unjust?
And wherefore say not I that I am old?
O, love’s best habit is in seeming trust,
And age in love loves not to have years told.
Therefore I lie with her and she with me,
And in our faults by lies we flattered be.