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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
          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 [10]DALL•E
   and [11]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 [12]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 [13]years [14]of [15]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.

[16]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 [17]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 [18]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
   [19]generative AI.

   If you’re reading this, you don’t need me to recap the cultural impact
   of [20]ChatGPT and [21]Bing Chat for you. Suffice to say, if Google—the
   runaway winner of the Web 2.0 economy—is [22]legit shook, there’s
   probably fire to go with all this smoke. Moreover, when you consider
   that [23]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 [24]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.

[25]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 [26]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
   “[27]fluent bullshit.” And after months of seeing friends “cheat” at
   their day jobs by having [28]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 [29]more [30]time to [31]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 [32]I recorded my screen and set it to a few lofi
   tracks before [33]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?

[34]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 [35]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
       [36]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 [37]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 “[38]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.

[39]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 [40]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 [41]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 [42]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!

[43]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:
   [44]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:
   [45]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. 🫒

[46]Justin Searls

   An icon of a hash sign Code Name
          Agent 002

   An icon of a map marker Location
          Orlando, FL

   [47]Twitter [48]Mastodon [49]Github [50]LinkedIn [51]Website

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