If you’ve been using LinkedIn over the past several months, you’ve maybe noticed an uptick in solicitation to chime in on certain topics of expertise.

I won’t judge you for taking part. We’ve all been a little bored at work and taken personality or IQ tests, and the temptation of a social network is hard to resist. So, going on LinkedIn is at least not going to look as unprofessional as other websites.

You connect, and right there on the home page is a simple call to action. Only, this time LinkedIn is referring to you as an expert.

There’s a question begging for an answer. And, not just any answer. An answer from you. Because, you—yes you—are an expert. LinkedIn needs you.

So, you lean in and reset your glasses on the bridge of your nose and take a gander at the query:

What do you do if you want to maximize your success as a retail sales professional working remotely?

What?

People. I’m a teacher. Sure, I worked retail—20 YEARS AGO—but I am no expert in that topic. In fact, I wouldn’t even deem myself worthy of that label as a teacher.

Personally, I wrote this idea off as dumb when I saw it. It is clearly a way to get engagement numbers up, or something. Really, I couldn’t care less about the why. LinkedIn is one of the oddest social networks out there. It is greed and clout having a pissing contest. I have kept my account active, though, because it has helped me find jobs in the past.

This morning when I decided to take a look-see at LinkedIn, the post at the top of my feed was a notification that a colleague had contributed their expertise to one of these collaborative articles. The contribution was well-written. Perfect, in fact, especially for someone who does not speak that language fluently. Also, remember what I said about being asked questions about topics I am not an expert on? The retail thing? Think about that for a second when I tell you—and I mean no insult to this colleague—that the person answering that question is as close to an expert on the subject as I am to veterinary medicine.

At least LinkedIn got my attention, right?

What’s up with these Collaborative Articles?

In March 2023, Daniel Roth announced the launch of Collaborative Articles in a post titled Unlocking nearly 10 billion years worth of knowledge to help you tackle everyday work problems.

We are introducing collaborative articles — knowledge topics published by LinkedIn with insights and perspectives added by the LinkedIn community. These articles begin as AI-powered conversation starters, developed with our editorial team. Then, using LinkedIn’s Skills Graph, we match each article with relevant member experts who can contribute their lessons, anecdotes, and advice based on their professional experience.

And, that’s when the real magic happens: when professionals share real-life, specific advice by contributing their perspectives to the work questions we’re all facing every day. Because starting a conversation is harder than joining one, these collaborative articles make it easier for professionals to come together and add and improve ideas — which is how shared knowledge is created.

Of course, feedback is part of this, too. When you read the collaborative articles, you can react to the contributions by clicking the “insightful” reaction, helping your network and peers quickly find great insights. Through the articles, you’ll discover new people to follow who will keep you learning about topics key for your job and career. And to make sure that contributors are rewarded for giving their time and experience, they can earn a new Community Top Voice badge in their skill areas (e.g., “Top Sales Voice”) for adding their insights. You’ll be able to see the badge on profiles and next to contributions on the articles.

Allow me to TL;DR that for you:

We (LinkedIn, owned by Microsoft) had AI write some prompts, and you are going to provide the words and answers we need to write the articles (well, AI will do the writing).

Then other “experts” will up- or down-vote the article.

In exchange, we will give you magical internet points in the form of a “badge” for your profile.

Six months after launch, over a million contributions had been made. Collaborative articles became “the fastest growing traffic driver to LinkedIn”.

Wow. Good for them. Think of the shareholder value those one million contributions have made.

Just stop contributing

These sweet badges can be tempting, but think about it like this:

• Contributors are giving their time to a company for free and getting JPG’s in return.
• Contributors may not even be experts on the topic.
• Contributors could be using AI to write answers to a question asked by AI.
• Contributors could be poisoning the data by providing less than accurate answers and then voting on the legitimacy of the article.

The FAQ is filled with other tidbits, including the fact that it isn’t as easy to get those badges as you may have thought:

Contributions made to collaborative articles are public

(that is the default and only option)

You are automatically following skill pages created by LinkedIn

(you need to manually unfollow skills)

LinkedIn identifies members who are likely to be experts in a certain topic based on their work experience, skills proficiency, and prior engagement on the platform.

(have enough keywords on your profile, and you get to participate)

You must make three contributions in a specific skill to be eligible to earn the badge for that skill.
Once you’ve made three contributions, a progress tracker unlocks, showing your journey to earning the badge. If you’re below 50% of contributors, the tracker encourages you to continue making insightful contributions for improvement. If you’re above 50% but haven’t earned a badge yet, it displays your percentage standing to earn the badge.
Once you achieve a badge, the tracker motivates you to showcase your achievement.

(persuasive tech at its best: there are prompts, ability, and motivation)

Final thoughts

Never give too much information to a social network. LinkedIn already has a tonne of information about its users, and being LinkedIn famous is the type of millennial flex that Gen Z laughs at and Gen X doesn’t give a shit about.

Don’t provide free work for anything with the word “AI” in the description. A myriad of Chatbots have us writing prompts all day and the AI-hype-machine has somehow normalized the idea that “prompt engineer” is a job. If it is a job, why not reimburse us for our time?

Real experts and other “legitimate voices” are not jumping through hoops for a badge. They are probably too busy to bother with LinkedIn. If you are an expert in a field, consider creating a blog or a newsletter to engage with your audience. That way, you own the content you write instead of it being the property of a social network. You can also find ways to monetize your work without using ads or writing sponsored content.

Are you the future or are you the past?

[…] “Now Minny knows everything in all our school books.”

“Phew!” said Joe, wiping his forehead, “You know, that was hard work storing all that information in the machine. I didn’t realize there was so much to know. Maybe it’d just be easier to do our homework every day.”

“I don’t think so,” Danny said. “Sure, it was hard work. But now we’re free forever.”

The year was 1958. Before my time, but not before the time of common sense, forethinking, and Science Fiction. In Danny Dunn and the Homework Machine, by Jay Williams and Raymond Abrashkin, the titular Danny is up to shenanigans again. Along with his friends Joe and Irene, they begin using a computer (Miniac, or “Minny”) to cheat on their homework. What could ever go wrong?

For starters, Minny needs information—lots of information. And, the person feeding the information into Minny needs to understand the problem:

Professor Bullfinch shook his head. “No. It never can be Beethoven, Mrs. Dunn. No matter how intelligent the computer is, it is only a machine. It can solve problems in minutes that would take a man months to work out. But behind it there must be a human brain. It can never be a creator of music or of stories, or paintings, or ideas. It cannot even do our homework for us—we must do the homework. The machine can only help, as a textbook helps. It can only be a tool, as a typewriter is a tool.”

For decades now, writers have been telling us these fantastic tales of what the future could be. Asimov, Gibson, Stephenson, Wells, Leckie, Chambers, and an infinite number of other science and speculative fiction writers have filled our heads with wondrous and worrisome visions of what may come.

Well, is it coming or not? The future, I mean. It all seems a little dull, doesn’t it? Like watching a film after having read the entire plot on Wikipedia.

The latest trend—AI—feels like some never-ending remake of a reboot of a spin-off of past trends. As a layperson who tries to keep their finger on the pulse of trends like this, because I like technology, I can’t help but feel like we’ve been here before.

Generative AI, like ChatGPT, has been following a path set out by authors and experts for several years now. Like other trends (e.g. web3, blockchain, cryptocurrency, NFTs, etc.), it has grown to dangerous proportions and—keep in mind that I am just an observer—it is very likely that this bubble is going to “go pop.”

Follow for now and follow for this, ‘cause everybody follows for nothing at all

Everybody and their neighbour is on the AI train now. I’m thinking of a shape, the name is on the tip of my tongue. Imagine a situation where someone rich or powerful wants to be richer or more powerful. The only way to do that is to get some people who are just a little less rich or powerful than you to give you their money or their loyalty. This situation continues until there are a great number of not-so-rich or powerful people giving something to those who are a little richer or more powerful than them.

This is what has happened with AI.

Companies like OpenAI have been blown out of proportion and are being copied left, right, and centre. Average folks have been had. My students have been had! Here we are getting ChatGPT to do things for us, like our homework, and for what? For the benefit of OpenAI. We are giving our time to OpenAI’s ChatGPT. We are not employed my them, but our prompts are valuable information for them and their partners.

Every time we open our news feeds, we see story upon story about the apps (Copilot, Midjourney), models (GPT-4, Mistral), and a whole lot about the infrastructure (looking at you Nvidia and cloud-computing gang). As of writing, There’s An AI For That lists over 13,000 AIs for some 16,000 tasks (many with that super sweet .ai TLD). With all the talk of money, it’s no wonder so many exist:

The combined market value of Alphabet, Amazon and Microsoft has jumped by $2.5trn during the AI boom. Counted in dollars, that is less than three-quarters of the growth of the hardware layer, and barely a quarter in percentage terms. Yet compared with actual revenues that AI is expected to generate for the big-tech trio in the near term, this value creation far exceeds that in the other layers. It is 120 times the $20bn in revenue that generative AI is forecast to add to the cloud giants’ sales in 2024. The comparable ratio is about 40 for the hardware firms and around 30 for the model-makers.

Just how rich are businesses getting in the AI gold rush? in The Economist

Many of these AIs promise great savings to their clients. Some keep tabs on fast-food workers, like Hoptix.ai which promises to “Increase your restaurants net profit by 15% in 90 days,” and if you find that hard to believe, you haven’t seen the totally rad graphic they have on their landing page:

Not all employees like this idea. That isn’t surprising. Hoptix’s Riley makes a point on their website of how it can be used to reward employees. Surveillance in the workplace should not be taken lightly, however. If an employer trusts an app to decide who to reward, it probably isn’t hard for the app to convince them to dismiss an employee.

Can you explain just what you are?

Is the current form of AI intelligent? Is it even unbiased?

What do those words, ‘intelligent’ and ‘unbiased’ even mean?

Red flags have been going up from the start:

Timnit Gebru didn’t set out to work in AI. At Stanford, she studied electrical engineering—getting both a bachelor’s and a master’s in the field. Then she became interested in image analysis, getting her Ph.D. in computer vision. When she moved over to AI, though, it was immediately clear that there was something very wrong.

“There were no Black people—literally no Black people,” says Gebru, who was born and raised in Ethiopia. “I would go to academic conferences in AI, and I would see four or five Black people out of five, six, seven thousand people internationally.… I saw who was building the AI systems and their attitudes and their points of view. I saw what they were being used for, and I was like, ‘Oh, my God, we have a problem.’”

from These Women Tried to Warn Us About AI by Lorena O’Neil (Rolling Stone)

The people and companies behind the models have a lot in common:

• usually men in charge (OpenAI, Anthropic, Cohere, Hugging Face, etc.);
• usually US-based (Silicon Valley seems popular);
• and occasionally associated with names you’ve heard, but never realized had a foot in the AI door (Meta (Facebook) has LLaMA, Anthropic was founded by members of OpenAI, and most have received funding from Google, Nvidia, Amazon, AMD, Qualcomm, and other big tech names).

(Again I will underline that I am just observing.)

From my vantage point, these models and apps are all coming from the same type of people. That is a red flag. Again, I cannot blame a person for taking advantage of a trend and trying to earn a buck. There are plenty of ethical questions that can be raised, but that might be beyond the scope of this little blog post.

AI as it stands is not really that smart. Sure, it can spit out some words, but not in a very natural way. As a professor in a country where English is not the mother tongue, I have the immense pleasure of reading hundreds upon hundreds of papers written by non-native–speakers of English. Let me tell you one thing: any teacher or professor with at least a month of experience under their belt will immediately recognize a ChatGPT paper, report, or written activity.

(Occasionally, students are shocked by their grades. They point at their paper, steeling their nerves, and ask for feedback. A walk-through essay without a single bibliographical entry? Really?)

AI just isn’t that bright for the moment. Some of them can almost pass an IQ test. Not many, though, are particularly gifted when it comes to reason, just ask Sally about her brothers and sister.

Supernova goes pop

In January, the International Energy Agency (IEA) issued its forecast for global energy use over the next two years. Included for the first time were projections for electricity consumption associated with data centers, cryptocurrency, and artificial intelligence.

The IEA estimates that, added together, this usage represented almost 2 percent of global energy demand in 2022—and that demand for these uses could double by 2026, which would make it roughly equal to the amount of electricity used by the entire country of Japan.

AI already uses as much energy as a small country. It’s only the beginning. by Brian Calvert (Vox)

Things are heating up (in more ways that one). Climate-wise, this AI boom really could have chosen a better moment. And claiming that AI can solve the problem is exactly what would happen in a sci-fi novel before everything goes boom.

You know something is up when you see reports titled “How AI Can Speed-Up Climate Action” by the Boston Consulting Group and (*sigh*) Google. I for one am relieved to hear that a “responsible deployment of AI” could “[have] the potential to unlock insights that could help mitigate 5% to 10% of GHG emissions by 2030.” And we can all breathe a sigh of relief knowing, now, that “87% of executives believe AI has potential to address their climate challenges”

Malarky.

Rubbish.

Horseshit.

In the US, there is already evidence that the life of coal-fired power plants is being prolonged to meet the rising energy demands of AI. In just three years from now, AI servers could be consuming as much energy as Sweden does, separate research has found.

Much of this increased energy demand comes from the added complexity of AI operations – generating AI queries could require as much as 10 times the computing power as a regular online search. Training ChatGPT, the OpenAI system, can use as much energy as 120 US households over the course of a year, the report claims.

AI likely to increase energy use and accelerate climate misinformation – report by Oliver Milman (The Guardian)

Whether it is out of pure laziness, the desire to make a quick buck, or even the sincere hope to make things better, users of these tools have become complicit in the destruction of the planet in the same way Pepsi did with plastics.

The Supernova don’t stop

I don’t think the interest in AI will die down soon. But, “soon” can be interpreted in different ways.

It is time to look beyond this boom and to the next, because it is too late to be first in the door. The disruptors have gone and disrupted. Sam Altman has made it to the cover of magazines like so many other great disruptors (Elizabeth Holmes and Sam Bankman-Fried come to mind).

It is time that more of us simply refuse AI. KFC uses AI to track employees? Don’t eat at KFC. Some search engine gives you creepy AI results? Change search engines. You get the idea.

This is not ludditism. This is not refusing to let AI loose on finding cures for diseases (despite the harm it does to the planet). This is refusing AI the same way many of us refuse to use a f-cking QR code instead asking for a printed menu in a café or restaurant, or when we refuse to install an app on our phones when the website works fine and our phones have browsers.

AI just isn’t cool enough to be everywhere.

Non-web References

Powerman 5000. (1999). Supernova Goes Pop [Song]. On Tonight the Stars Revolt!. DreamWorks Records.

Williams, J. & Abrashkin, R.. (1959). Danny Dunn and the Homework Machine. Whittlesey House.

Earlier this week I came across a post on Mastodon:

Of course I had to read the article. I read reviews quite a bit. I know you shouldn’t trust them, but I do it anyway. The article shared in the post, How Google is killing independent sites like ours, does an excellent job explaining the situation and putting things into perspective:

1. Searching for reviews will net you poorly written articles and listicles.
2. The reviews appear on websites that many would deem trustworthy, like Popular Science or Rolling Stone.
3. Many of the sites break Google’s rules.
4. Google is doing nothing about it.

If you run a legitimate site providing expert reviews, you cannot stand up against the SEO of these publishing companies. The HouseFresh article uses the example of trying to find an air purifier, and I’m not about to repeat their experiment with other products; we all know what will happen.

The article also links to the very interesting How 16 Companies are Dominating the World’s Google Search Results (2023 Edition) from Detailed.com.

Look at this infographic!

The report notes:

The 16 companies in this report are behind at least 562 individual brands which get traffic from Google each day.

Combined, Semrush estimates they pick up around 3.7 billion clicks from the search engine each month. An average of 6.5 million monthly clicks per site.

What to do?

What… to… do?

You may be familiar with a browser extension called uBlacklist. It is a search filter for Google and other search engines. It removes results from the page based on a blocklist. The blocklists are .txt files. I can make a .txt file!

So I did.

In fact, I made 16 of them. And then I made a 17^th.

I put them here:

16 COMPANIES FILTERS

Now you too can subscribe to these lists, and if you see any mistakes let me know.

Just install the browser extension (see the link above) and copy the SUBSCRIBE links. The official document shows you how to do it.

Then search!

Before you ask, yes, this is just for fun and spite. Some of these sites were already on my list of avoidable sources: sites like Make Use Of and CNET that are notoriously bad at recommending anything other than the latest Samsung or Apple product.

While preparing these files, it surprised me just how many similar sites were in fact subsidiaries of the same parent company. It also surprised me that some of these massive media companies were unknown to me. Not once have I ever heard of Hearst or Recurrent or PMC. It was like an epiphany, the pieces fell into place, my eyes opened. Search results are crap because companies like these have been gaming the SEO system with their keywords and other shenanigans.

So, I will filter them out. I do wish the extension worked with Mojeek.