Andrew Stiefel | technology

AI Isn't Taking Your Job – The Economy Is

2025-05-16T00:00:00-07:00

Have you noticed the latest trend sweeping executive suites? This year, layoff announcements are getting blamed on AI. Or maybe it’s employee performance. No — it’s definitely AI.

But if you look closer, you’ll find a more familiar culprit: the economy.

Companies don’t want to admit revenue is slowing or that fundamentals are crumbling. It’s easier to say they’re “embracing AI” than to admit they’re under pressure to cut costs and protect margins.

Yes, AI is changing how we work — especially for junior roles in software development, sales, content creation, and support. But the wave of layoffs sweeping tech, healthcare, and biotech isn’t being driven by machines replacing humans. It’s being driven by elevated interest rates, post-Covid revenue declines, and investor pressure to do more with less.

AI just provides a convenient cover narrative.

A Correction Disguised as Innovation

Several major tech firms — including cloud providers, social platforms, and enterprise software giants — have collectively laid off more than 600,000 workers since 2022 according to Layoffs.fyi.

These cuts impacted recruiting, HR, operations, marketing, and in some cases engineering. Companies that ramped up quickly during the pandemic suddenly found they were overextended in a post-zero interest rate policy (ZIRP) world. Capital became more expensive. Growth expectations took a cut. And investors needed to see profitability instead.

This is a Covid-era bubble correcting itself. Most of these companies are still larger than they were in 2019. In fact, as research by the Pragmatic Engineer has shown, software engineering in particular has experienced a boom and bust.

The change in the number of listings in 2025, compared to 2020, for each of these areas:

All jobs: +10%

Banking and finance: -7%

Sales: -8%

Marketing: -19%

Software development: -34%

Overall hiring has mostly returned to 2019 levels. There is a hesitation to hire more, but that’s partially due to uncertainty about the level to hire for with AI and ongoing uncertainty about macroeconomic conditions.

From Tech to Biotech: The Narrative Spreads

Like any good pathogen, narratives like to spread. And this one is jumping from Silicon Valley to biotech and healthcare – the other industries most impacted by Covid-19.

A large mRNA vaccine manufacturer recently announced that it was merging the CTO and Head of People into a new Chief People and Digital Technology Officer role. The stated reason? To identify which roles are best suited for AI vs people.

But they are following a now-familiar playbook:

The company reported a GAAP net loss of $1.1 billion loss in Q1 2025
Revenue from Covid-era mRNA vaccines continues to fall
They have reduced operating costs by 19% to compensate

Behind the scenes, it’s facing post-pandemic revenue decline and investor pressure to reduce costs. So they’re turning to layoffs — with AI as the smokescreen.

So what is AI doing to jobs?

There is no question AI is changing work. I use AI in my job nearly every day. And it’s definitely impacting the labor market. A recent study found that:

Junior developers experienced 27-39% productivity gains using AI coding tools.
But senior developers saw smaller productivity gains, typically 8-13%.

AI enhances speed, but doesn’t replace deep expertise yet. In fact, the more you know about a subject or field the less magical it is.

Unsurprisingly, entry-level roles are the first to feel the effects: SEO writers, junior engineers, SDRs, and support teams. But the scale is smaller than the headlines suggest.

Even firms that proudly touted major AI-based role reductions have quietly reversed course — reopening hiring in those same departments just months later.

The Bottom Line

Companies aren’t laying people off because AI is replacing them. At least, not yet. If you’re familiar with AI technology, you know both how far and fast it has come — and all the countless ways it hallucinates or falls flat. It makes a great assistant, but a lousy replacement.

So don’t mistake narrative spin for a technological inevitability.

AI isn’t coming for your job. Because the CFO already did.

Disclaimer: The views expressed in this post are my own and do not reflect the opinions or positions of my employer. Company examples have been anonymized to focus on trends rather than individual firms. All references are drawn from publicly reported data.

Markdown Files, Not Apps

2025-01-17T00:00:00-08:00

Files, not apps. I’m convinced this is the best way to work in the future.

I write down almost everything important in my life: lists, ideas, plans, code, and articles. They form my extended memory. They are a record of what I’ve done, and who I’ve been. That’s why I only use markdown files.

Traditional document and note-taking apps like Word, Evernote, and Notion create data silos and vendor lock-in, limiting your ability to freely move and process information.

I discovered this the hard way when I tried to move between different ecosystems like Evernote and Apple Notes. Sure, there was a way to export my data. But it was a mess. Things broke, attachments went missing, and I spent hours converting, reformatting, and importing.

Markdown is simple

Markdown files solve these challenges through their fundamental simplicity. Any text editor can open and edit them, eliminating dependency on specific applications or platforms. They have all the basic formatting you need for headlines, bullets, lists, and even basic tables.

Markdown is portable

If you use Evernote or Notion, for example, you are helpless without them. If they go out of business you are trapped and have to move your files to another format. You will outlive those companies. I’ve already outlived a few note-taking companies.

I can work with markdown files on any device, forever. I can access them with any text editor I want. This makes them remarkably long-lived. Proprietary formats come and go, but markdown’s open, plain text nature ensures it will likely outlast me. This makes it the ideal format for building a personal knowledge base.

Markdown works offline

There are times when I want to be offline and unreachable, but would still like to jot ideas down in a format that is easily searchable. Working offline is an amazing productivity boost. Why give that up for an online database?

Markdown is ready for AI

This is a use case I wouldn’t have imagined a few years. But it’s the main reason I’m convinced markdown files are the future. Since markdown is just text, AI systems can easily process and analyze your notes.

For example, I can easily grab my “Ideal Customer Profile.md” file and throw it into ChatGPT, Claude, Gemini, or whatever I’m using at the moment to work with it. I can add my messaging framework, and immediately start brainstorming marketing copy.

I can run a local LLM, point it at a folder of my markdown files, and then interact with my notes by asking questions. It’s like a personal assistant built on everything I’ve learned and experienced.

Conclusion

Everything I write is human readable, and yet actionable with apps and tools. I always have my files available locally, and backups are just making a copy of the folder and putting it somewhere safe.

Markdown is simple, portable, independent, and free — exactly how I want to live.

Principles of Platform Independence

2025-01-10T00:00:00-08:00

The internet is filled with stories of people breaking free from Google’s ecosystem (here are a few examples I’ve seen recently). Yet in their rush to escape one tech giant, many users inadvertently chain themselves to another.

I’ve noticed a paradox on social media: people who champion digital independence while relying heavily on platform-specific tools like Apple Notes or Passwords. They overlook how Apple, like Google, builds its business model around service lock-in – from iCloud storage to annual hardware upgrades.

Personally, I prefer to maintain platform independence as much as possible. Over the years I’ve developed a set of criteria — let’s call them principles of platform independence — for choosing what apps and software I use. This way I retain control over my digital life regardless of which company’s software or hardware I use.

1) Cross-Platform Compatibility

Use applications that work across different operating systems (MacOS, Windows, Linux, etc.) to avoid being locked into one platform’s ecosystem. This ensures your workflow remains consistent regardless of the device you’re using.

2) Data Ownership

Choose applications that allow you to easily export your data in formats that can be read by different applications. You should have full control of your data, and it should be accessible in a readable format outside of the application.

3) Data Portability

Beyond just being able to export data, it should be in widely-supported open formats (like CalDAV for calendars, IMAP for email) rather than proprietary ones. This enables interoperability and switching between different clients.

4) Logic Separation

Your data shouldn’t be tightly coupled to specific application features. For example, your notes should be stored in a format that preserves basic formatting even if specific application features aren’t available elsewhere.

5) Local Control

Whenever possible, you should have direct access to your data in a readable format on your local system, not just through a cloud interface. This includes the ability to make local backups. I like to keep a version in the cloud, a version on my local system, a local backup, and a remote back up.

Dead Internet is No Longer a Theory

2025-01-03T00:00:00-08:00

Dead internet theory suggests most of the internet’s content and traffic is artificially generated by bots rather than humans. The conspiracy theory emerged amidst the fallout of the 2016 election, when news broke about websites creating fake news to gain clicks on Facebook and Google and leveraging bot accounts to amplify it.

Ironically, Meta decided to turn the conspiracy theory into reality.

Meta looks into the black mirror

Quoting from 404 Media:

Earlier this week, Meta executive Connor Hayes told the Financial Times that the company is going to roll out AI character profiles on Instagram and Facebook that “exist on our platforms, kind of in the same way that accounts do … they’ll have bios and profile pictures and be able to generate and share content powered by AI on the platform.

It didn’t take long for users on Blue Sky and Reddit to find some of these fake AI profiles on Instagram. The profiles were often offensive caricatures of what a gigantic corporation like Meta might imagine a “proud Black queer momma of 2” might post about. The profiles tended to post AI generated content similar to what other spammer’s on Meta’s Facebook and Instagram platforms have been creating.

Here’s the example I referenced above:

Most of these profiles were already a few years old, and Meta has since announced that they will be taking them down.

Dead internet theory gained traction online because it speaks to genuine concerns people have about authenticity and the increasing use of bots and AI online. I have no idea how anyone at Meta could think launching this exact thing would be a good idea.

I also suspect these profiles will still get rolled out in the future. But next time they won’t have the “AI managed by Meta” labels attached to them.

Navigating the dark forest

So where to from here? We’re entering a new dark forest where it’s increasingly difficult to find the humans among the trees. Maggie Appleton tackled these challenges in one of her blog post’s entitled “The Dark Forest and Generative AI.”

Humans who want to engage in informal, unoptimized, personal interactions have to hide in closed spaces like invite-only Slack channels, Discord groups, email newsletters, small-scale blogs, and digital gardens . Or make themselves illegible and algorithmically incoherent in public venues.

The new challenge isn’t generating content — it’s proving that you’re human.

Fortunately, we humans evolved a whole bag of tricks to tell if something is a predator or not in the forest. Now we have to apply them online. Appleton suggests a few techniques in her post:

Triangulate – verify information from multiple sources (e.g. the SIFT strategy)
Be original – introduce new ideas (AI can only share what’s come before)
Be creative – use language quirks that AI wouldn’t use

In the future I expect we’ll see more institutional verification, similar to what LinkedIn has been rolling out for profiles. In LinkedIn’s case they are using a mix of ID verification (drivers license, passport, etc) to verify people and email verification to confirm companies.

Back to IRL as the norm?

If there’s something to be optimistic about, I suspect it will be the re-emergence of in-person value. Before the pandemic we were already seeing an uptick in “digital essentialism” as people got tired. We were then thrown even more into the online realm with the pandemic, and now we’re seeing more and more misinformation, astroturfing, and other deceptive practices online. I suspect more and more of us will be looking for connection outside the old electronic waterholes of Facebook, Instagram, etc.

I, personally, look forward to a slower, less online world if that’s indeed the future.

Weighing in on AI Hype and Risk

2024-12-10T00:00:00-08:00

Casey Newton wrote a piece last Thursday titled “The phony comforts of AI skepticism” — and wow did people have some thoughts about it:

Gary Marcus responded against the mischaracterization of his position
Edward Ongweso had quite a few more things to say
Casey Newton responded while basically doubling down on his argument
Dave Karpf waded in with his perspective and a recap of the debate

I’m sure there’s more out there but you get the idea. I almost didn’t write something myself, but one part of this conversation still feels like it’s missing to me. So let’s take a brief look at Newton’s argument before diving in.

Newton’s argument in a nutshell

To briefly summarize, the core thrust of Newton’s piece is that the entire range of AI discussion boils down to two groups: critics external to firms and organizations directly working on or studying AI who believe “AI is fake and sucks” and internal critics building AI who understand “AI is real and dangerous” given current and future capabilities. Newton aligns himself with the latter and suggests that the former camp are not only incapable of recognizing genuine innovations made in this field, but risk leaving us blind to threats enabled by those advances.

It’s a widely reductive argument and Marcus and Ongweso do a great job of breaking down the issues with it, and in particular the straw man Newton builds for himself to attack. I’m not going to wade in there, but I recommend reading their posts for context.

I want to talk about how smart journalists like Newton can accidentally (or intentionally) fall for the hype, and in the process fail to correctly frame the conversation for their readers. And I want to be clear up front: I think this was a huge miss.

A false dichotomy

My particular issue with Newton’s article is about the placement of what I’ll call skepticism (“AI is fake and sucks”) and hype (“AI is real and dangerous”) as polar opposites on a spectrum. It’s so wildly off I’m surprised Newton chose to defend it. As many people noted in comments on Mastodon and Bluesky, the skeptics Newton is attacking are more likely to fall into the “AI is real and dangerous and is also fake and sucks” category.

Understanding how current generative AI models, and large language models (LLMs) in particular, fail to deliver on their promise usually means you also understand how dangerous these models are now, and will be in the future. Those shortcomings are especially dangerous when they are released to a public that doesn’t understand what those shortcomings are, and into a media environment that is still grappling with the ramifications of social media.

Sorting out risk from hype

My bigger issue is that Newton seems to fall for the hype from the people building and most likely to benefit from advances in AI. And make no mistake, making unsubstantiated claims about the future dangers of AI is a way of creating hype around the technology. It’s probably the oldest form of hype in computer science – we’ve been talking about imagined AI risks since at least the mid-19th century.

The problem here is that hypothetical risks are treated the same as actual risks. We know that the current GenAI models are accelerants to the issues of propaganda and misinformation that were already widely present on social media and other communication channels before ChatGPT was released. GenAI models are also wrong quite often, despite attempts to improve them, leading to a new form of misinformation when users aren’t appropriately skeptical of the responses they receive. And that doesn’t get into whether LLMs are even a viable path to true general intelligence or not.

We should be talking about emerging risks, but we need a lot more grounding in the conversation from journalists like Newton. And we should differentiate these as hypothetical risks, and they should be treated with more skepticism, especially when they come from the people most likely to benefit from them.

Final thoughts

As Marcus outlines at the start his essay, Newton does get a lot right, including that we should be preparing now for AI to get more dangerous. And we should be documenting the different attitudes that are emerging around AI. I agree with all this.

But we need the media to do a better job of framing the emerging discussion and attitudes towards AI without serving as hype for the individuals and firms building and benefitting from these models.