Are ad-driven business models bad?

There's been a lot of discussion that ad-driven business models are inherently exploitative and anti-consumer. I think that's both wrong and not a helpful way to look at how to fix the problems in the tech industry.

I think the problem with ad-driven models is that it's easy and tempting for executives to use short-term metrics and incentives like clicks or engagement. It's the wrong metric and incentives for teams. But I think the problem is more ignorance, or willful ignorance, of that issue.

In the short-term, for an ad-supported product, ad revenue and profitability does look like ad clicks. In the long-term, ad profitability looks like converting performing ads for advertisers over the lifetime of customers. Those are quite a bit different.

With subscription-driven models, it's more obvious that your metrics should be long-term. With ad-driven models, long-term metrics are harder to maintain, and many execs don't realize they need to. If execs let teams optimize for clicks, they eventually find those clicks have long-term costs as customers start leaving, but unfortunately it's quite costly to reverse the damage once you're far down this path.

In the long-term, I think you can improve the profitability of an ad-driven platform by making the content and ads work better for customers and advertisers (raising ad spend, increasing ad competition for the space, and reducing ad blindness) and by retaining customers longer (along with recruiting new customers). That looks a lot like the strategy for increasing the profitability of a subscription-driven platform. So I don't see much of a difference between ad-supported and subscription-supported business models other than the temptation for executives to inadvertently optimize for the wrong thing.

NATO on bots, sockpuppets, and shills manipulating social media

NATO has a new report, "Social Media Manipulation 2022/2023: Assessing the Ability of Social Media Companies to Combat Platform Manipulation".

Buying manipulation remains cheap ... The vast majority of the inauthentic engagement remained active across all social media platforms four weeks after purchasing.

[Scammers and foreign operatives are] exploiting flaws in platforms, and pose a structural threat to the integrity of platforms.

The fake engagement gets picked up and amplified by algorithms like trending, search ranking, and recommenders. That's why it is so effective. A thousand sockpuppets engage with something new in the first hour, then the algorithms think it is popular and show crap to more people.

I think there are a few questions to ask about this: Is it possible for social media platforms to stop their amplification of propaganda and scams? If it is possible but some of them don't, why not? Finally, is it in the best interest of the companies in the long-run to allow this manipulation of their platforms?

Too many metrics and the Otis Redding problem

The "Otis Redding problem" is "holding people, groups, or businesses to too many metrics: They can’t satisfy or even think about all of them at once."

The problem is not just that people don't really know what to do anymore. It's that many people, when faced with this, start doing things that reward themselves: "They end up doing what they want or the one or two things they believe are important or that will bring them rewards (regardless of senior management’s strategic intent)."

That quote is from Stanford Professor Bob Sutton's book Good Boss, Bad Boss, which somehow I hadn't read until recently. I've read all of Bob Sutton's other books too, they're all great reads.

This is just one tidbit from that book. There's lots more in there. On the Otis Redding problem, my read is that Bob's advice is to only pick a 2-3 simple, actionable metrics, but then frequently discuss whether they are achieving what you want and change them if they aren't.

By the way, the name the "Otis Redding problem" comes from the line in his song "Sitting on the Dock of the Bay" where he says, "Can’t do what ten people tell me to do, so I guess I’ll remain the same."

Superhuman AI in the game Go

For a few years now, AI achieved superhuman game playing abilities for Go.

It was quite a milestone for AI. When I was in graduate school, people used to joke that AI for Go was where careers go to die. The game has a massive search space, so had thwarted efforts for decades.

So AlphaGo and similar efforts that beat top-ranked Go players was a very big deal indeed when it happened back in 2016. But now, a amateur-level human player just beat a top-ranked AI at playing Go. He won 14 of 15 games.

Most of the reporting on this has been that the player used an exploit, one hole in the AI strategy, that will easily be closed. But I think this will be harder to fix than most people expect.

AlphaGo and similar techniques work by using deep learning to guide the game tree search, focusing it on moves used by experts. This result says you can't do that, that you need to consider more possible moves.

The human won here by doing moves the AI didn't expect, then exploiting the result. It's not that there is just one hole. It's that doing moves outside of what the AI expects, anything outside of what it has seen in the training data, can result in a bad playing by the AI, which can then be exploited by the human.

Solving that means considering more moves by the opponent, which explodes the game tree search, making the search massively exponential again. I suspect it's going to be hard to fix.

Huge numbers of fake accounts on Twitter

It seems like this should get more attention, "hundreds of thousands of counterfeit Twitter accounts set up by Russian propaganda and disinformation" that are "still active on social media today."

There has been widespread manipulation of social media, customer reviews, and trending, search ranker, and recommender algorithms using fake crowds.

All of these depend on wisdom of the crowds. They try to use what people do and like to help other people find things. But wisdom of the crowds doesn't work when the crowd isn't real.

Caroline Orr Bueno has some more details, writing that "this is the first we've heard of an ongoing campaign involving such a large number of accounts" and that it is clear this is at "a scale with the potential to mass-manipulate."

Orr Bueno also quotes former Twitter executive Yoel Roth as saying "it's all too cheap and all too easy." This is the core problem with misinformation and disinformation in the last decade.

If it is cheap, easy, and profitable to scam and manipulate using huge crowds of fake accounts, you will get huge numbers of fake accounts. The solution will have to be to make it more expensive, difficult, and unprofitable to scam and manipulate using fake accounts.

Details on personalized learning at Duolingo

There's a new, great, long article on how Duolingo's personalized learning algorithms work, "How Duolingo's AI learns what you need to learn".

An excerpt as a teaser:

When students are given material that’s too difficult, they often get frustrated and quit ... [Too] easy ... doesn’t challenge.

Duolingo uses AI to keep its learners squarely in the zone where they remain engaged but are still learning at the edge of their abilities.

Bloom’s 2-sigma problem ... [found that] average students who were individually tutored performed two standard deviations better than they would have in a classroom. That’s enough to raise a person’s test scores from the 50th percentile to the 98th

When Duolingo was launched in 2012 ... the goal was to make an easy-to-use online language tutor that could approximate that supercharging effect.

We'd like to create adaptive systems that respond to learners based not only on what they know but also on the teaching approaches that work best for them. What types of exercises does a learner really pay attention to? What exercises seem to make concepts click for them?

Great details on how Duolingo maximizes fun and learning while minimizing frustration and abandons, even when those goals are in conflict. Lots more in there, well worth reading.

Massive fake crowds for disinformation campaigns

The Guardian has a good article, "'Aims': the software for hire that can control 30,000 fake online profiles", on fake crowds faking popularity and consensus to manipulate opinion.

Misinformation and disinformation are the biggest problems on the internet right now. And it's never been cheaper and easier to do.

Note how it works. The fake accounts coordinate together to shout down others and create the appearance of agreement. It's like giving one person a megaphone. One person now has thousands of voices shouting in unison, dominating the conversation.

Propaganda is not free speech. One person should have one voice. It shouldn't be possible to buy more voices to add to yours. And algorithms like rankers and recommenders definitely shouldn't treat these as organic popularity and amplify them further.

The article is part of a much larger investigative report combining reporters from The Guardian, Le Monde, Der Spiegel, El Pais, and others. You can read much more starting from this article, "Revealed: the hacking and disinformation team meddling in elections".

How can enshittification happen?

Cory Doctorow has a great piece in Wired, "The ‘Enshittification’ of TikTok. Or how, exactly, platforms die." It's about that we regularly see companies make their product worse and worse until it hits a tipping point, then the company loses its customers and starts dying.

Enshittification eventually causes the company to die, so isn't in the best interest of the company. It's definitely not maximizing shareholder value or long-term profits. So why does it happen?

Cory Doctorow does have a bit on the why, but could use a lot more: "An enshittification strategy only succeeds if it is pursued in measured amounts ... For enshittification-addled companies, that balance is hard to strike ... Individual product managers, executives, and activist shareholders all give preference to quick returns at the cost of sustainability, and are in a race to see who can eat their seed-corn first."

That's not very satisfying though. I mean, the company dies. Execs are screwing up. Why does that happen? What can be done about it? That's the question I think needs answering.

Understanding exactly why enshittification happens is important to finding real, viable solutions. Is it purposeful or unintentional on the part of teams and company leaders? Is it inevitable or preventable? If you get the root cause wrong, you'll get the wrong solution.

My view is that enshittification is mostly unintentional. I think it's a result of A/B testing, mistakes in setting up incentives, and teams busily optimizing for what's right in front of them instead of keeping their eye on the prize.

I don't think executives intentionally drive companies into the ground. I think most execs and teams have no idea that this path they are going down will cause such long-term harm to the company. If most really don't want to destroy the company, that leads to different solutions.

Layoffs as a social contagion

Stanford Professor Jeffrey Pfeffer wrote about the recent layoffs at tech companies, saying that it hurts the company in the long-term, but CEOs can't avoid the pressure to join in.

[CEOs] know layoffs are harmful to company well-being, let alone the well-being of employees, and don’t accomplish much, but everybody is doing layoffs and their board is asking why they aren’t doing layoffs also.

The tech industry layoffs are basically an instance of social contagion, in which companies imitate what others are doing. If you look for reasons for why companies do layoffs, the reason is that everybody else is doing it ... Not particularly evidence-based.

Layoffs often do not increase stock prices, in part because layoffs can signal that a company is having difficulty. Layoffs do not increase productivity. Layoffs do not solve what is often the underlying problem, which is often an ineffective strategy ... A bad decision.

For more on the harm, please see my old 2009 post from the last time this happened, "Layoffs and tech layoffs".