Almost Timely News: 🗞️ The Broken Bargain of Big Tech (2026-05-03)

We owe Big Tech nothing.

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What’s On My Mind: The Broken Bargain of Big Tech

This week, something that’s been burrowed into my mind ever since I heard it.

A couple of weeks ago, SparkToro founder Rand Fishkin said this on LinkedIn:

“For 20 years, I was a staunch proponent of “white hat” only. Now? Now that Google and the rest of Big Tech have proven themselves to be far, far more evil than any black hat SEO ever was? I say: screw ‘em. As long as it doesn’t hurt you/your business, you have no responsibility to be ethical toward giant corporations that would never return the favor.”

This has stuck with me for days now, turning it over in my head. He’s not wrong. From ingesting massive amounts of data without consent or compensation to willfully causing mental health harm to declaring that it’s too inconvenient to stop human trafficking, Rand’s point is reinforced by the piles and piles of lawsuits surrounding Big Tech that they’ve lost.

Disclaimer and Warning: To be clear, since there’s room for misinterpretation here, neither Rand nor I advocate doing anything illegal or harmful to your business. Do not break laws.

Part 1: The Broken Bargain

Let’s talk about the bargain that big tech companies, particularly social media and search companies, had with us, the marketers and the people. The bargain for the longest time was, “you give us your data and we’ll send you traffic, or customers”, or something along those lines. There was give and take. We gave our data, they gave us business. In the early days of the web, we would allow companies like Google to index our content in exchange for referrals. Over time, in the last 25 years, that bargain has gotten steadily worse.

Part of it is because of completely irresponsible use of their technology on the part of big tech companies. Companies like Meta, for example, allowed their system to be abused by election interference teams in 2016, the Cambridge Analytica scandal. They ended up using that as an excuse to continue turning the screws on all of us over the years.

Anyone who has ever maintained a social media account for a business has been in the situation where you had reach and interactions with your audience at first, for free, and then over time it got harder and harder to reach that audience without paying until even today. Not only can you not reach your audience without paying, even when you do pay, you don’t necessarily reach your audience.

Google used to be just a collection of 10 blue links; back in the golden era of search, you could reliably count on Google to deliver you substantial amounts of business, in exchange for your data. Google has steadily taken over more and more of the real estate in search results, and now pretty much everything in a Google search result is intermediated in some way, from the one box to widgets to summaries and web guide and now AI overviews and AI mode.

Rand Fishkin at SparkToro, formerly at Moz, showed over time that Google has sent less and less traffic, fewer and fewer clicks, every single year. With things like AI overviews, that gets even worse; Search Engine Land reports that 2/3 of clicks never generate traffic now. The bargain has been rotting away for some time.

It isn’t just search, it’s also social media. Look at a system like Twitter, or their company formerly known as Twitter, I suppose. This was a company that for 15 years was the open standard of data sharing.

People could use its network graph to identify who was influential. It was one of the reasons why Twitter was so valuable and why so many influencer marketing systems were benchmarked against it, because you could browse the data without having to pay for it. And this didn’t stop people from advertising on the system, quite the opposite. People advertised there, even though it did not have the reach or draw of Meta and its properties.

When Elon Musk bought the company in 2022 and locked down the system in 2023, that came to a stop, and only the wealthiest, largest partner companies could access the data. That has been the case across all major networks now. Only the biggest, most well-heeled partner companies can access data. This puts smaller businesses at a complete disadvantage. They can no longer access any of their own data, and they are reliant on bigger tech partners - for considerable sums of money - to access data that used to be readily available.

As quickly as they could, the big tech companies tilted the tables in their favor - more for them, less for everyone else.

This is the part that pisses me off. The fortunes they have built and the money and power they have accumulated came from us, from our data. At the end of the day they built their empires on our data. Without us, they would literally have no product.

One of the definitions of social networks relies on the network effect, first described by Bob Metcalfe during the founding of Ethernet. A network effect is any effect where adding nodes to the network builds the network’s overall value. A network of fax machines has value, and every new fax machine sold increases the value of existing ones.

Social networks without users have no value. There’s no intrinsic value to them. If you were to put up a video site and put up a bunch of videos on it, it would have value to users without them contributing. That’s essentially what our blogs are or were, our newsletters, things like that.

We put up the content, and you don’t need to be an active participant to get value from it. A social network does not have value until users put content in it. So the social network relies on user contributions. Our data is what makes it valuable. Search engines also would not have any value if we were not actively contributing content to them through the publishing of it on the internet.

We have lost sight, as consumers, of who has the actual power, in the sense that if we stop publishing, if we stop contributing, the value of the network declines (or is taken over by marketing slop, which is effectively the same thing). This also means that when we’re using these tools, we have a right to our data. Everything that we do, everything we click on, everything we scroll by or swipe by, that is our data. We are contributing it to these companies, and they are giving us nothing in return. There is no value exchange.

It’s gotten so bad that in certain parts of the world, like Europe, regulations like GDPR had to force companies to allow us to download some of our own data, and even then, not all of it. If you’ve used tools like the data privacy and data download tools in any social network, you get a subset of your data, you do not get all your data.

They may say, “Oh, well, we keep you connected to your friends.” Yeah, I can email them. I don’t need your stupid social network to do that. I can use this old-fashioned thing called a telephone if I really want to talk to them. But we have been conditioned - duped - into believing that we need these systems, and that we should be grateful for whatever crumbs of data they’re willing to let us have.

Or even more hilariously, we should be grateful for the privilege of paying to access our own data. Which brings us to the topic of today’s newsletter. Given the monumentally unfair by design system in place, how could we start tilting the tables a little bit in our favor? How could we start making use of our own data?

Part 2: What’s Fair Game

Here’s how we will do this, in ways that do not violate laws, and do not violate even terms of service. While I wholeheartedly agree with Rand’s sentiment that we don’t owe these tech companies anything and we have no obligation to behave ethically towards them, I do still represent a company that does still have values, so the scope of today’s newsletter will be on things that are ethically defensible.

We could go all black hat and open up the big bag of evil tricks, but that would be irresponsible, and on Monday morning, Katie would probably slap me. For legal reasons, let’s play fair. We will save discussion of other tactics for when you and I are having coffee or the beverage of your choice at an event, and there’s nothing being recorded.

Here’s what I consider fair game: our data. We are going to take ownership of our data. By our data, I specifically mean data that is generated on our devices. When we interact with a search engine or a social network or anything, data flows to and from that third party to our devices. In the same way that I would consider an employee of a company visiting my house to be on my property, I consider a company’s data on my devices to be on my property, and that data is fair game.

Likewise, it would be inappropriate for me to break into someone else’s data center, hook up a bunch of hard drives, and try to take all their data. If I did that in the real world, I would rightfully be arrested for trespassing and breaking and entering.

So that’s our ethical boundary: my machine, my data.

If your company sends data to my machine, I have the right to use it while it is on my machine.

How do we get this data? In every web browser, but especially those built by Google, the Chrome interface, and every browser based on the Chromium platform, which includes things like Brave, there is a concealed (but not secret) data collection mechanism.

This data collection mechanism is called HAR files, which is short for HTTP Archive. When you go into the browser and turn on the developer tools, you can see it recording everything that you do: every click, every scroll, every piece of data that flows onto your screen. One of the things that I’ve said for a long time now, in one of the basic use cases of generative AI, is that if you can see it, AI can access it.

While you can take screen recordings and things, which is my preferred non-technical way of getting data out of systems that don’t want us exporting data, Google has given us the ability to use the same data collection mechanisms it uses. We’re going to use that today by showing you how to watch Chrome collect data, how to access that data, and then how to process that data with generative AI, with Claude Code, so that we can work with the same data that we are generating on behalf of a big tech company.

They should not be the only ones who benefit from our data. We’re going to use these tools as regular people, we’re not going to automate them fully. We’re not going to do massive, scaled attacks. We are going to collect our own data from our devices that is well within the boundaries of laws, of ethics, and terms of service.

Part 3: Thinking Through the Problem

The fundamental imbalance in the now broken bargain is that we generate data and big companies profit from it. We don’t have the opportunity to profit from the same data. One of the simplest (but not easiest) problems that every marketer wants to solve is: “how do we determine who’s influential about a particular topic or in a particular place?”

This is the heart and soul of influencer marketing. The first step in solving this problem is influencer identification. Who is influential?

To answer that question we need data, and we could go out to data brokers and vendors and big tech companies and spend thousands of dollars to subscribe to services. Or buy ads. But we have access to the data, we just don’t know it. Our first step is to use any Chrome-based browser and get familiar with its built-in data collection. Chrome is a data vacuum, it vacuums up massive amounts of data on Google’s behalf. Those same mechanisms are available to us.

In your Google Chrome, go to the View menu, select Developer, and then select Developer Tools.

You should see a panel appear on the side of your browser. At the top is a series of menus. You want to find the Network menu.

Open up any page in the browser while the network menu is selected and you will see this window fill up with stuff. What is this stuff? This is every bit and byte being sent and received by the website you’re on and what you do on it. If you can see it in the browser window, it is in this network pane.

Take special note of the download button, a tiny, tiny little icon.

This is the treasure chest right here. Everything that is being transferred to and from your computer in this window can be downloaded as an HTTP archive, or HAR file.

When you download the file and open it up, you will see that the entire file is in the JSON markup language. Here’s a tiny sample:

            {
              "name": ":authority",
              "value": "www.linkedin.com"
            },
            {
              "name": ":method",
              "value": "GET"
            },
            {
              "name": ":path",
              "value": "/voyager/api/graphql?includeWebMetadata=true&variables=(memberIdentity:ACoAAAAh1hYBC5uWZCSAvgqeeD6EwqwOV-4taAo)&queryId=voyagerIdentityDashProfiles.b5c27c04968c409fc0ed3546575b9b7a"
            },
            {
              "name": ":scheme",
              "value": "https"
            },
            {
              "name": "accept",
              "value": "application/vnd.linkedin.normalized+json+2.1"
            },

You don’t need to understand what any of this means. It’s not for you, it’s for the machines. What’s important is that we have the data now. We have our data that we generated by browsing around LinkedIn and interacting with people normally, as we do everyday.

What’s inside this massive archive, which can easily top one gigabyte of plain text? Every button click, every scroll, every tap, every like, comment, and share. Every profile we see go by. Every post, all the text, anything that’s on screen is in this file.

That gold mine is what we need to hand as raw materials to our AI agent of choice to turn it into useful insights. There’s a lot of stuff in here that doesn’t matter, things like navigation and menus, item information, timings, stuff that isn’t helpful. But by giving great directions to our AI system, we can weed all that out and focus only on the things that we want to know about.

Part 4: Processing the Data

The good news is that you don’t need to process this data by hand. You don’t even need to know necessarily what all data is. You only need to know what data is valuable that you want to work with.

There’s a field of study called network graphing, where you digest down connections and interactions that you would observe in a place like a social network and turn that into math. Every entity be it a person or a company or a post is a node, and every interaction like a like, comment, or share is an edge.

Think of nodes as nouns and edges as verbs.

My friend Mitch Joel has an expression, “It’s not who you know, it’s who knows you”. Influence is all about you being top of mind when someone else is thinking about you. Inside our social networking files that we generate are all the posts and comments of people talking about topics and other people. For any given topic, who is talked about the most?

If we were to digest down that massive file into those nodes and edges of who is most talked about, we could start to understand who is influential in any given topic. It’s outside the scope of this issue of the newsletter, but there are over a dozen different methods of measuring influence. In network graphing jargon, this is called measures of centrality. How important is any one node on the network as determined by the edges coming in and out of that node?

For anyone who has worked in SEO, you have almost certainly heard the term page rank. PageRank is a form of centrality measure. In the very early days of Google, a page’s importance was judged by how many other pages linked to it. The more inbound links you had, the more important your page was. That is still somewhat the case in modern SEO, but there are hundreds of other ranking signals besides page rank. However, that measure of centrality is something almost everyone in marketing has been exposed to at one point or another in the last 25 years.

What we do next is take that HAR file that we generated from our casual use of a social network, and we give it to an agent coding tool like Claude Code, along with detailed requirements about what we want to do, building a data processing system that can take our data from our device and turn it into useful insights.

When I did this for myself earlier, it ended up being about a 15 minute voice recording that then turned into over 75 pages of design documents. As I write this, Claude Code is implementing all those designs to make a functioning system that processes my data.

This is an example of the kind of thinking we should be adopting to our use of big tech systems. Any data that’s on our device within our four walls should be fair game. If big companies can profit off of what we generate for them, so should we.

Part 5: Wrapping Up

The key takeaways from this issue of the newsletter are that we should benefit from our data just as much as large corporations do. We have a right to our own data, and we have a right to use our data to benefit ourselves. In this context that means data that is on our devices.

The tools exist today to enable us, to give us access to our data and to give us a way to unlock its value. Think about all the places you go online and all the things that you do and see and hear. Think about all the data you generate for other corporations who will never send a penny your way. How could you use that data to benefit yourself or your company?

Take the examples I’ve shown in today’s newsletter, and think of the problems you would love to solve if you had access to the data. Based on all this information, how would you direct AI to make use of that data that your machine is already collecting, that big tech is already collecting on your behalf and turn it into something that benefits you.

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ICYMI: In Case You Missed It

Here’s content from the last week in case things fell through the cracks:

• LinkedIn’s AI Overhaul: Why the New Algorithm Changes Everything (And What It Doesn’t)

• Unlocking AI-Powered Productivity: How to Glue CLI Tools Together and Build Your Own Automated Workflows

• The Secret Weapon That Separates AI Beginners From Experts: Building Your Vocabulary First

• Could AI Have Recommended Bombing an Iranian School? A Disturbing Experiment with 4 Major AI Models

• AI Tools Are Lazy — And That’s Exactly Why You Need to Stop Accepting “Good Enough

• Almost Timely News: 🗞️ A Sober Conversation about AI and Employment (2026-04-26)

• In-Ear Insights: Generative AI for SEO and PPC

• So What? Measuring Competitive Relevance in GEO

On The Tubes

Here’s what debuted on my YouTube channel this week:

• So What? Measuring Competitive Relevance in GEO

• You Ask, I Answer: Building Repeatable Workflows To Stop AI Mistakes?

• You Ask, I Answer: Using AI For Customer Insights?

• You Ask, I Answer: How To Avoid The Yes Man Problem With AI?

• You Ask, I Answer: Handling The 20 Year Objection In AI Sales?

• You Ask, I Answer: How To Use AI For Buyer Evaluation?

• Almost Timely News: 🗞️ A Sober Conversation about AI and Employment (2026-04-26)

• You Ask, I Answer: Professional Headshots Using AI Tools

My Merch Shop

I’ve been adding so much stuff that I’ve decided to bundle it all in what I call a Merch Shop, because otherwise there’s literally too much to keep track of and I run out of space in my own newsletter.

So welcome to the Merch Shop!

Skills for Claude and Agentic AI:

• Job to AI Skill and Plugin - unpack any job description into what AI can and can’t do

• Fact Check Skill (FREE!)

Books:

• Almost Timeless: 48 Foundation Principles of Generative AI

• Generative AI for SEO and PPC Marketers

• Generative AI for Destination Marketers

Courses:

• GEO 101 for Marketers

• The AI Ready Strategist

• Mastering Prompt Engineering for Marketing

• Generative AI Use Cases

Subscriptions:

• The Prompt Playbook - collections of prompts from my channel and newsletters, delivered weekly

Recent Talks

These are just a few of the classes I have available over at the Trust Insights website that you can take.

• 👉 New! From Text to Video in Seconds, a session on AI video generation!

• Never Think Alone: How AI Has Changed Marketing Forever (AMA 2025)

• Generative AI for Tourism and Destination Marketing

• Powering Up Your LinkedIn Profile (For Job Hunters) 2023 Edition

• Building the Data-Driven, AI-Powered Customer Journey for Retail and Ecommerce, 2024 Edition

• The Marketing Singularity: How Generative AI Means the End of Marketing As We Knew It

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• Your 90 day GEO action plan

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Get Back To Work!

Folks who post jobs in the free Analytics for Marketers Slack community may have those jobs shared here, too. If you’re looking for work, check out these recent open positions, and check out the Slack group for the comprehensive list.

• Chief Marketing Officer For Healthcare Venture Capital Fund at Healthcare Shares

• Director Of Growth Marketing at Himalaya Wellness USA

• Director Of Marketing, Growth & Product at Drug Hunter

• Director Of Product Marketing at Medallion

• Director Performance Marketing at Foundation Partners Group

• Head Of Digital at Nood

• Head Of Growth Marketing at Zicasso

• Research Director / Sr. Research Director at Breakthrough Research

• Sr. Director, Organic Growth at Vuja Dé Digital

• Strategy Director at 829 Studios

• Vp Of Marketing Operations at SOHORANI

• Vp, Client Services at EVERSANA INTOUCH

Disclosure: I source these links from LinkedIn every week on the following criteria: New in the past seven days, Easy Apply on, remote roles, USA geography.

How to Stay in Touch

Let’s make sure we’re connected in the places it suits you best. Here’s where you can find different content:

• My blog - daily videos, blog posts, and podcast episodes

• My YouTube channel - daily videos, conference talks, and all things video

• My company, Trust Insights - marketing analytics help

• My podcast, Marketing over Coffee - weekly episodes of what’s worth noting in marketing

• My second podcast, In-Ear Insights - the Trust Insights weekly podcast focused on data and analytics

• On Bluesky - random personal stuff and chaos

• On LinkedIn - daily videos and news

• On Instagram - personal photos and travels

• My free Slack discussion forum, Analytics for Marketers - open conversations about marketing and analytics

Listen to my theme song as a new single:

• Apple Music

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Social Good: Ukraine 🇺🇦 Humanitarian Fund

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👉 Donate today to the Ukraine Humanitarian Relief Fund »

Events I’ll Be At

Here are the public events where I’m speaking and attending. Say hi if you’re at an event also:

• SmarterX AI Writer’s Summit, May 2026

• The Trust Insights Generative AI Workshop, sometime this spring!

• SMPS AI Conference, Austin, November 2026

• MarketingProfs B2B Forum, Boston, November 2026

There are also private events that aren’t open to the public.

If you’re an event organizer, let me help your event shine. Visit my speaking page for more details.

Can’t be at an event? Stop by my private Slack group instead, Analytics for Marketers.

Required Disclosures

Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.

Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.

My company, Trust Insights, maintains business partnerships with companies including, but not limited to, Amazon, Talkwalker, MarketingProfs, Agorapulse, The Marketing AI Institute, Spin Sucks, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.

Thank You

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Christopher S. Penn