Fun Friday: The Race for AI Creative Works Control

In April of 2020, William and I wrote in the Cutter Business Journal an article entitled Moving Forward in 2020: Technology Investment in ML, AI, and Big Data. We focused on three areas: surveillance (monetization), entertainment (stickiness), and whitespace opportunities (climate, energy, transportation). This statement bears emphasis:

Instead of moving from technology to key customers with an abstracted total addressable market (TAM), we must instead quantify artificial intelligence (AI) and machine learning (ML) benefits where they specifically fit within business strategies across segment industries. By using axiomatic impacts, the fuzziness of how to incorporate AI, ML, and big data into an industry can be used as a check on traditional investment assumptions.

[For additional information on this article, please see AI, ML, and Big Data: Functional Groups That Catch the Investor’s Eye (6 May 2020, Cutter Business Technology Advisor).]

But one might be puzzled as to where generative AI tools such as ChatGPT or Dall-E fit in the AI landscape and why we should care about AI art, AI news and press releases, AI homework and essays — even threatened AI music like what’s talked about in 1984 by Orwell.

The reality is these areas utilize easily crawled content available everywhere lying around in the Internet attic. It also takes tremendous computing power to conduct ML and process this data effectively into some kind of appearance of sensible output. Hence, these tools will remain in the corporate hands of the creators no matter what they claim about “open source” — it’s simply too difficult for anyone but a giant corporate entity to support the huge costs involved. So this is about monetization and stickiness. Large companies are willing and able to pay the cloud costs if the customer gets dependent on using their tools. Flashback to the tool-centric sell of the 1980s, Silicon Valley style. All we need is an AI version of Dr. Dobbs Journal and we’re all set.

Previous attempts at generative AI have usually focused on small ML datasets, leading to laughable and biased results. Now companies are looking at the shift at Google in particular from ads to AI, along with Microsoft and FaceBook. Everyone believes they are in a race and frantically trying to catch up before all that sweet sweet money is locked up by one of them.

But is there really any need to “catch up”? Is this a real trend, or just an illusion? Google made its fortune on categorizing every web page on the Internet. It had plenty of rivals back then. I was fond of Altavista myself. But there was also everything from Ask Jeeves to Yahoo. 

Now Google and Microsoft are analyzing the contents of these big pots of data with ML But it’s not just for analyzing. It’s for creating content. Music. Art. News. Opinion. And you need an awful lot of processing power to handle all that data. So it’s now a Big Guy Game.

One of the approaches to eliminating bias is to use ML to process more and more data. The bigger the data pot, the less the bias and error. Well, that’s the assumption, anyway. But it’s a dubious assumption given the pots analyzed are often variations on the theme. Most search categorization is based on recent pages and not deep page analysis. Google is no Linkpendium.

All this, oddly, reminds me a bit of the UK mad cow fiasco, where their agricultural industry essentially cultivated prions by feeding animals dead animals. Like Curie purified radium from pitchblend, the animals who died of this disease were then processed and fed to other animals. And since prions, like radium, persist after processing, the prions were concentrated and made it up the food chain into humans.

So in like kind the tools themselves are feeding back into the ML feedlot and being consumed again. It may take longer than a few days, but we will be back to the same problems in terms of bias and error.

However, the gimmick of having a “machine” write your essay or news blurb is very tempting. Heck, AIs are claimed to take medical exams or pass a law class or handle software programming better than people.

But being a doctor or attorney or a software engineer is much more than book learning, as anyone who’s done the job will tell you.

And of course, there is now backlash from various groups who value their creative works and are not interested in rapidly generated pale imitations polluting their space and pushing them out. They didn’t consent to have their works pulled into a training set and used by anyone. Imitation is neither sincere or flattering, and is even legally actionable if protected by copyright, trade secret, or patent. It’s not “fair use” when you suck it all in and paraphrase it slightly differently.

This isn’t new. William and I ran into this in the old days with our 386BSD code. We were happy to let people use it and modify it — what is code if not usable and modifiable? But we asked that provinance be maintained in the copyright itself by leaving the names of the authors. And we had entire modules of code that were written denovo in the days when kernel design meant new ideas. It was an amazing creative time for us.

So I remember how shocked I was when an engineer at Oracle asked me about tfork() and threading, since a Linux book he had talked about it but he could find nothing in the Linux source code. I pulled out our 386BSD Kernel book and showed him that it was novel work done for 386BSD and would not work in Linux. Upon discussion it turned out that book just “paraphrased” many of our chapters without even considering that Linux did not incorporate much of our work because it was a very different architectural design. It misled software designers — but I’m sure it sold a heck of a lot more books than we did by turning “386bsd” to “linux”. So it is today, but a heck of a lot easier for the talentless, the craven, and the criminal to steal.

Now many software designers are upset because their source code depositories are used as the models for automated coding, and they don’t like that one bit. And I don’t blame them.

We lived it. And it was a primary reason why the 386BSD project was terminated. Too many trolls and opportunists ready to take any new work and paraphrase it. So get ready to see this happen again in music, art, news, and yes, software. The age of mediocrity is upon us.

1984, here we come…

Fun Friday: Old Style SV and the Great DOD Financial Fallback

Silicon Valley tech has long run on DOD and related monies. From established companies like IBM and Raytheon to think-tanks like SRI and Xerox PARC to startups, it was understood that national security was a lucrative opportunity. War, whether hot or cold, is good for business, if you’re the right kind of business.

Symmetric Computer Systems, founded by William Jolitz in 1982, was one such company. Unix, TCP/IP and networking software, and processor technologies were all considered controlled technologies subject to export restrictions. Heck, when William and I took a 375 computer to Germany for a USENIX conference, we had to have the appropriate license to carry one with us — and I had to take special courses in how to apply for and maintain such licenses. I also had nice conversations with the various national laboratories and security agencies who loved our little systems. Easy to transport, hardy, and enough processing power to handle any immediate intelligence and scientific need. William understood his customer. And his primary customer was not conventional.

But things changed here in the Valley. Companies exported their technologies to cheaper shores. We stopped making semiconductors. We stopped building computers. By the time I had co-founded InterProphet, an inventor of low-latency TCP chip technology we pioneered, few VCs would consider a hardware or semiconductor investment at all. Everyone talked about their “China Strategy” and their Guangdong manufacturing as if that was the only key to success (it wasn’t). Even the US government was asking for RFPs for technologies which were expected to be built, not by US firms, but by Huawai. Technology was viewed as trivial, of no value except to pass on as a bribe to build inexpensive trinkets to bored Americans.

Silicon Valley transmuted from one that invented technology to one that concerned itself with “unicorns”, aka fantasy companies devoid of financial fundamentals. One of my investors in InterProphet, Dan Lynch of Cybercash and Interop fame, warned William and me of the folly of nailing down the technology too soon, before investment was secured. Once the market cap was determined, you were no longer a fantasy. Sometimes it was better to not build the product, but just talk about it. This was an accurate assessment of the situation over the last twenty years. Unfortunately, it was one concession William and I could never make. We loved to build products. We loved to make things. We loved to create.

So we retired from the game. It was sweet while it lasted.

This little meditation does have a point. And it has to do with the change in the world economy with 1) the pandemic and 2) the war in Ukraine. Bear with me.

The pandemic of 2020-2022 led to a sea change in the structure of work. In companies where people could work remotely, such as software development, there was little impact of the disease on the workforce. People could isolate, maintain their income stream, and produce. Technology companies were winners.

For the majority of businesses, people could not easily isolate. Schools were shut down and remote learning introduced (a boon to the Zooms of the world). But remote learning on such a scale had never been done, and its effectiveness was poor. Children isolated from their teachers, peers and supports did not do as well as college-educated professionals using Slack and cloud tools. Stores and entertainment venues such as movie theaters were closed down. Medical offices limited visits to reduce the threat of contagion. People died in isolation wards of the disease, separated from their families. It was a tragic and depressing time.

Given so many lost their jobs due to the pandemic through no fault of their own, extensive government funding was used to essentially pay people to stay home while a vaccine was developed. Remarkably, within one year after Covid struck our shores, a vaccine was made available. This will go down as one of the most amazing scientific achievements of our time.

But as the threat of contagion has eased, the ability to get tech workers back in the office has stalled. The frustration of the CEO and investment class towards these “entitled” workers has been venomous and extreme as they continue to look on their now empty business properties. And as government supports for the consumer class have ended, so too has the artificial consumer boom. And heck, if those tech workers won’t come to the office to bend the knee to management, well, let’s fire them.

Silicon Valley is a small place. One jumps off the cliff, all jump off the cliff.

The second impact (this is beginning to sound like Evangelion) was the war in Ukraine launched by Russia. This war had an immense unexpected effect on Silicon Valley investment for a simple reason — much of the funds channeled through crypto and currency schemes dried up when the EU, the UK and the US agreed to impose sanctions on Russia and its many collaborators. And while VCs and its unicorns did not advertise it, the underpinnings of many financial deals were based on money flows that have existed since the Cold War and were now interdicted. Whomp whomp, indeed.

It’s taken about a year for these investments to collapse. The collateral damage: job losses, startup failures, venture funds with negative outlook, and eventually clawbacks from angry customers, investors, and LLCs. We live in interesting times.

We are now embarking on a new Cold War, with Russia, China and the lesser lights (Iran, N. Korea, and so forth). There is no longer a demand for a China strategy or Russian oligarch monies. And this is a win for startups involved in technologies which have a basis in national security, just like Symmetric Computer Systems started with 40 years ago. What was old is new again.

So what should we be looking at in well-positioned investments?

Strategic investments in battery technologies, carbon capture, and energy storage are still strong contenders for long-term investment. AI technologies focused on separating the wheat from the chaff for meaningful intelligence, both business and governmental. Alternative satellite creation and maintenance, especially for low-latency non-interceptible communications — something I was solicited and wrote an RFP for about twenty years ago — will be a growing opportunity. And good old-fashioned secure software, hardware and networking is always a safe bet in bad times as a lucrative acquisition by a big guy.

Avoid broad consumer gimmicks like AI journalists, unless you’re a VC that can sustain the hype for at least six months. The lack of sustainable dark funds and non-protected IPR (AI can’t hold patents or copyright) given the current Cold War climate makes it problematic until everyone figures out how to get around the currency strictures. SPACs are nothing but trouble (hey, Palantir, how’s it going?). And the launch side of satellites is locked up for now.