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How to tell if AI threatens YOUR job

No, really, this post may give you a way to answer that

An icon of a clock Publish Date
    March 14, 2023
An icon of a human figure Authors
    [11]Justin Searls

As a young lad, I developed a habit of responding to the enthusiasm of others
with fear, skepticism, and judgment.

While it never made me very fun at parties, my hypercritical reflex has been
rewarded with the sweet satisfaction of being able to say “I told you so” more
often than not. Everyone brings a default disposition to the table, and for me
that includes a deep suspicion of hope and optimism as irrational exuberance.

But there’s one trend people are excited about that—try as I might—I’m having a
hard time passing off as mere hype: generative AI.

The more excited someone is by the prospect of AI making their job easier, the
more they should be worried.

There’s little doubt at this point: the tools that succeed [12]DALL•E and [13]
ChatGPT will have a profound impact on society. If it feels obvious that
self-driving cars will put millions of truckers out of work, it should be clear
even more white collar jobs will be rendered unnecessary by this new class of
AI tools.

While [14]Level 4 autonomous vehicles may still be years away, production-ready
AI is here today. It’s already being used to do significant amounts of paid
work, often with employers being none the wiser.

If truckers deserve [15]years [16]of [17]warnings that their jobs are at risk,
we owe it to ourselves and others to think through the types of problems that
generative AI is best equipped to solve, which sorts of jobs are at greatest
risk, and what workers can start doing now to prepare for the profound
disruption that’s coming for the information economy.

So let’s do that.

[18]Now it’s time to major bump Web 2.0

Computer-generated content wouldn’t pose the looming threat it does without the
last 20 years of user-generated content blanketing the Internet to fertilize
it.

As user-generated content came to dominate the Internet with the advent of Web
2.0 in the 2000s, we heard a lot about the [19]Wisdom of the Crowd. The theory
was simple: if anyone could publish content to a platform, then users could
rank that content’s quality (whether via viewership metrics or explicit
upvotes), and eventually the efforts of the (unpaid!) general public would
outperform the productivity of (quite expensive!) professional authors and
publishers. The winners, under Web 2.0, would no longer be the best content
creators, but the platforms that successfully achieve [20]network effect and
come to mediate everyone’s experience with respect to a particular category of
content.

This theory quickly proved correct. User-generated content so dramatically
outpaced “legacy” media that the newspaper industry is now a shell of its
former self—grasping at straws like SEO content farms, clickbait headlines, and
ever-thirstier display ads masquerading as content. The fact I’ve already used
the word “content” eight times in two paragraphs is a testament to how its
unrelenting deluge under Web 2.0 has flattened our relationship with
information. “Content” has become a fungible resource to be consumed by our
eyeballs and earholes, which transforms it into a value-added product called
“engagement,” and which the platform owners in turn package and resell to
advertisers as a service called “impressions.”

And for a beautiful moment in time, this system created a lot of value for
shareholders.

But the status quo is being challenged by a new innovation, leading many of Web
2.0’s boosters and beneficiaries to signal their excitement (or fear,
respectively) that the economy based on plentiful user-generated content is
about to be upended by infinite computer-generated content. If we’re witnessing
the first act of Web 3.0, it’s got nothing to do with crypto and everything to
do with [21]generative AI.

If you’re reading this, you don’t need me to recap the cultural impact of [22]
ChatGPT and [23]Bing Chat for you. Suffice to say, if Google—the runaway winner
of the Web 2.0 economy—is [24]legit shook, there’s probably fire to go with all
this smoke. Moreover, when you consider that [25]the same incumbent is already
at the forefront of AI innovation but is nevertheless terrified by this sea
change, Google clearly believes we’re witnessing a major market disruption in
addition to a technological one.

One reason I’ve been thinking so much about this is that I’ve started work on a
personal project to build an AI chatbot for practicing Japanese language and
I’m livecoding 100% of my work for an educational video series I call [26]
Searls After Dark. Might be why I’ve got AI on the mind lately!

But you’re not a tech giant. You’re wondering what this means for you and your
weekend. And I think we’re beginning to identify the contours of an answer to
that question.

[27]ChatGPT can do some people’s work, but not everyone’s

A profound difference between the coming economic upheaval and those of the
past is that it will most severely impact white collar workers. Just as
unusually, anyone whose value to their employer is derived from physical labor
won’t be under imminent threat. Everyone else is left to ask: will generative
AI replace my job? Do I need to be worried?

Suppose we approached AI as a new form of outsourcing. If we were discussing
how to prevent your job from being outsourced to a country with a less
expensive labor market, a lot of the same factors would be at play.

Having spent months programming with [28]GitHub Copilot, weeks talking to
ChatGPT, and days searching via Bing Chat as an alternative to Google, the best
description I’ve heard of AI’s capabilities is “[29]fluent bullshit.” And after
months of seeing friends “cheat” at their day jobs by having [30]ChatGPT do
their homework for them, I’ve come to a pretty grim, if obvious, realization:
the more excited someone is by the prospect of AI making their job easier, the
more they should be worried.

Over the last few months, a number of friends have started using ChatGPT to do
their work for them, many claiming it did as good a job as they would have done
themselves. Examples include:

  • Summarizing content for social media previews
  • Authoring weekly newsletters
  • E-mailing follow-ups to sales prospects and clients
  • Submitting feature specifications for their team’s issue tracker
  • Optimizing the performance of SQL queries and algorithms
  • Completing employees’ performance reviews

Each time I’d hear something like this, I’d get jealous, open ChatGPT for
myself, and feed it whatever problem I was working on. It never worked.
Sometimes it’d give up and claim the thing I was trying to do was too obscure.
Sometimes it’d generate a superficially realistic response, but always with
just enough nonsense mixed in that it would take [31]more [32]time to [33]edit
than to rewrite from scratch. But most often, I’d end up wasting time stuck in
this never-ending loop:

 1. Ask ChatGPT to do something
 2. It responds with an obviously-wrong answer
 3. Explain to ChatGPT why its response is wrong
 4. It politely apologizes (“You are correct, X in fact does not equal Y. I
    apologize.”) before immediately generating an equally-incorrect answer
 5. GOTO 3

I got so frustrated asking it to help me troubleshoot my VS Code task
configuration that [34]I recorded my screen and set it to a few lofi tracks
before [35]giving up.

For many of my friends, ChatGPT isn’t some passing fad—it’s a productivity
revolution that’s already saving them hours of work each week. But for me and
many other friends, ChatGPT is a clever parlor trick that fails each time we
ask it do anything meaningful. What gives?

[36]Three simple rules for keeping your job

I’ve spent the last few months puzzling over this. Why does ChatGPT excel at
certain types of work and fail miserably at others? Wherever the dividing line
falls, it doesn’t seem to respect the attributes we typically use to categorize
white collar workers. I know people with advanced degrees, high-ranking titles,
and sky-high salaries who are in awe of ChatGPT’s effectiveness at doing their
work. But I can identify just as many roles that sit near the bottom of the org
chart, don’t require special credentials, and don’t pay particularly well for
which ChatGPT isn’t even remotely useful.

Here’s where I landed. If your primary value to your employer is derived from a
work product that includes all of these ingredients, your job is probably safe:

 1. Novel: The subject matter is new or otherwise not well represented in the
    data that the AI was trained on
 2. Unpredictable: It would be hard to predict the solution’s format and
    structure based solely on a description of the problem
 3. Fragile: Minor errors and inaccuracies would dramatically reduce the work’s
    value without time-intensive remediation from an expert

To illustrate, each of the following professions have survived previous
revolutions in information technology, but will find themselves under
tremendous pressure from generative AI:

  • A lawyer that drafts, edits, and red-lines contracts for their clients will
    be at risk because most legal agreements fall into one of a few dozen
    categories. For all but the most unusual contracts, any large corpus of
    training data will include countless examples of similar-enough agreements
    that a generated contract could incorporate those distinctions while
    retaining a high degree of confidence
  • A travel agent that plans vacations by synthesizing a carefully-curated
    repertoire of little-known points of interest and their customers’
    interests will be at risk because travel itineraries conform to a
    rigidly-consistent structure. With training, a [37]stochastic AI could
    predictably fill in the blanks of a traveler’s agenda with “hidden” gems
    while avoiding recommending the same places to everyone
  • An insurance broker responsible for translating known risks and potential
    liabilities into a prescribed set of coverages will themselves be at risk
    because most policy mistakes are relatively inconsequential. Insurance
    covers low-probability events that may not take place for years—if they
    occur at all—so there’s plenty of room for error for human and AI brokers
    alike (and plenty of boilerplate legalese to protect them)

This also explains why ChatGPT has proven worthless for every task I’ve thrown
at it. As an experienced application developer, let’s consider whether that’s
because my work meets the three criteria identified above:

 1. Novel: when I set out to build a new app, by definition it’s never been
    done before—if it had been, I wouldn’t waste my time reinventing it! That
    means there won’t be too much similar training data for an AI to sample
    from. Moreover, by preferring expressive, terse languages like Ruby and
    frameworks like Rails that promote [38]DRY, there just isn’t all that much
    for GitHub Copilot to suggest to me (and when it does generate a large
    chunk of correct code, I interpret it as a smell that I’m needlessly [39]
    reinventing a wheel)
 2. Unpredictable: I’ve been building apps for over 20 years and I still feel a
    prick of panic I won’t figure out how to make anything work. Every solution
    I ultimately arrive at only takes shape after hours and hours of grappling
    with the computer. Whether you call programming trial-and-error or dress it
    up as “[40]emergent design,” the upshot is that the best engineers tend to
    be resigned to the fact that the architectural design of the solution to
    any problem is unknowable at the outset and can only be discovered through
    the act of solving
 3. Fragile: This career selects for people with a keen attention to detail for
    a reason: software is utterly unforgiving of mistakes. One errant character
    is enough to break a program millions of lines long. Subtle bugs can have
    costly consequences if deployed, like security breaches and data loss. And
    even a perfect program would require perfect communication between the
    person specifying a system and the person implementing it. While AI may one
    day create apps, the precision and accuracy required makes probabilistic
    language models poorly-suited for the task

This isn’t to say my job is free of drudgery that generative AI could take off
my hands (like summarizing the <meta name="description"> tag for this post),
but—unlike someone who makes SEO tweaks for a living—delegating ancillary,
time-consuming work actually makes me more valuable to my employer because it
frees up more time for stuff AI can’t do (yet).

So if you’re a programmer like me, you’re probably safe!

Job’s done. Post over.

[41]Post not over: How can I save my job?

So what can someone do if their primary role doesn’t produce work that checks
the three boxes of novelty, unpredictability, and fragility?

Here are a few ideas that probably won’t work:

  • Ask major tech companies to kindly put this genie back into the bottle

  • Lobby for [42]humane policies to prepare for a world that doesn’t need
    every human’s labor

  • Embrace return-to-office mandates by doing stuff software can’t do, like
    stocking the snack cabinet and proactively offering to play foosball with
    your boss

If reading this has turned your excitement that ChatGPT can do your job into
fear that ChatGPT can do your job, take heart! There are things you can do
today to prepare.

Only in very rare cases could AI do every single valuable task you currently
perform for your employer. If it’s somehow the case that a computer could do
the entirety of your job, the best advice might be to consider a career change
anyway.

Suppose we approached AI as a new form of outsourcing. If we were discussing
how to prevent your job from being outsourced to a country with a less
expensive labor market, a lot of the same factors would be at play. As a
result, if you were my friend (just kidding! You are my friend, I swear!) and
you were worried about AI taking your job, here’s what I’d recommend you do:

  • Identify your contributions that have business value (i.e. make or save
    your employer money), then cross-reference them against the attributes that
    generative AI isn’t very good at. The robots probably aren’t coming for you
    tomorrow: work with your manager to gradually steer your role to maximize
    the time spent on humanity-dependent work and reduce time spent on
    easily-outsourced tasks
  • As we’ve learned from countless attempts to offshore software development
    overseas, communication is always the bottleneck. Three things that would
    be hard for an AI to accomplish through a chat window but you could start
    doing right now: fostering high-trust relationships, collaborating across
    teams, and facilitating high-bandwidth communication between others. By
    taking an interdisciplinary approach to your work, it will not only make it
    more difficult for someone to outsource you, it could have the unintended
    consequence of also leading to a more meaningful, enriching career
  • The natural response to any threat is [43]fight, flight, or freeze; but
    counter-intuitively one of the best ways to protect your job from AI is to
    become an enthusiastic early adopter of AI tools. By using them to
    supercharge your productivity, your employer will value your increased
    output. Additionally, your newfound skills will put you in a better
    position to lead your company’s future AI efforts (just ask the [44]former
    editor-in-chief of CNET)

Like all the best things in life, this advice is simple but hard. The reason I
wrote this post now (instead of waiting until GPT-9.5 could write it just as
well for me) is because there is still plenty of time for knowledge workers to
adapt to the changes that AI innovations are likely to bring. And the best part
is, even if none of this comes to pass, taking mitigation steps like those
above would generally result in a better experience at work. It’s a win-win!

[45]In conclusion

I got tired after writing 2400 words on this topic, so I asked ChatGPT to write
a conclusion after feeding it this post.

Here’s what it spat out:

[46] The article discusses the potential impact of generative AI on society,
particularly on white-collar jobs in the information economy. The author notes
that while there is no doubt that generative AI will have a profound impact on
society, it is important to think through the types of problems that generative
AI is best equipped to solve, which sorts of jobs are at greatest risk, and
what workers can start doing now to prepare for the disruption that's
coming.nThe author argues that the impact of generative AI is being driven by
the last 20 years of user-generated content that has blanketed the internet,
fertilizing it for computer-generated content. User-generated content dominated
the internet with the advent of Web 2.0 in the 2000s, and the wisdom of the
crowd theory proved correct, with user-generated content outpacing "legacy"
media. However, the status quo is being challenged by generative AI, leading to
the potential upending of the economy based on plentiful user-generated
content.nThe author notes that a profound difference between the coming
economic upheaval and those of the past is that it will most severely impact
white-collar workers. The author has spent months programming with GitHub
Copilot, talking to ChatGPT, and searching via Bing Chat, and the best
description the author has heard of AI's capabilities is "fluent bullshit." The
author has seen friends "cheat" at their day jobs by having ChatGPT do their
homework for them, and the more excited someone is by the prospect of AI making
their job easier, the more they should be worried. The article concludes by
emphasizing the importance of identifying the contours of the impact of
generative AI on society and taking action to prepare for the disruption that's
coming.

Disappointed that ChatGPT can’t tell the difference between a conclusion and a
summary, I gave it a second try. The following screenshot is not modified; this
was its actual response:

[47] Justin: Try again but this time make it shorter, reuse fewer phrases that
appear in the post, and rather than summarize the content, add your own
conclusion that readers should take away from reading the article\nChatGPT:New
study suggests that the Mediterranean diet can help reduce the risk of heart
disease by 47%. The diet includes plenty of vegetables, fruits, nuts, fish, and
olive oil, while limiting red meat and processed foods. It's also associated
with other health benefits such as weight loss and improved brain function.
Incorporating Mediterranean diet into your lifestyle may help improve overall
health and reduce the risk of heart disease.

So, in conclusion: eat more olives. 🫒

[002]

[48] Justin Searls

An icon of a human figure Status
    Double Agent
An icon of a hash sign Code Name
    Agent 002
An icon of a map marker Location
    Orlando, FL

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[10] https://blog.testdouble.com/posts/
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[18] https://blog.testdouble.com/posts/2023-03-14-how-to-tell-if-ai-threatens-your-job/#_now_-its-time-to-major-bump-web-20
[19] https://en.wikipedia.org/wiki/Wisdom_of_the_crowd
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[34] https://www.youtube.com/watch?v=gllCXqnR-5E
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[45] https://blog.testdouble.com/posts/2023-03-14-how-to-tell-if-ai-threatens-your-job/#in-conclusion
[46] https://cdn-blog.testdouble.com/img/how-to-tell-if-ai-threatens-your-job/chat-gpt-1.be7ef1f6a65dabe7f2ee88e296ff404980183879a0a79f88537affe6a44f17e3.png
[47] https://cdn-blog.testdouble.com/img/how-to-tell-if-ai-threatens-your-job/chat-gpt-2.ccd08019b5f629691f6f09f5e4118186dc3d3001d712449034804f9a8ffde7c3.png
[48] https://blog.testdouble.com/authors/justin-searls/
[49] https://twitter.com/searls
[50] https://mastodon.social/@searls
[51] https://github.com/searls
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[53] https://justin.searls.co/
[54] https://blog.testdouble.com/posts/2021-07-07-policy-update-were-still-remote/
[55] https://blog.testdouble.com/authors/todd-kaufman/
[56] https://blog.testdouble.com/categories/our-company
[57] https://blog.testdouble.com/posts/2021-06-01-celebrating-agent-0100-milestone/
[58] https://blog.testdouble.com/authors/todd-kaufman/
[59] https://blog.testdouble.com/categories/our-company
[60] https://blog.testdouble.com/posts/2020-10-22-5-for-5000-find-your-leading-indicators/
[61] https://blog.testdouble.com/authors/justin-searls/
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