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 [9] Blog [10] Posts
 
 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
 
 [49] Twitter [50] Mastodon [51] Github [52] LinkedIn [53] Website
 
 Related posts:
 
 [54] Policy update: We’re still remote
 
 Don’t waste time commuting to an office. Test Double will continue to be a 100%
 remote software development company that values autonomy and flexibility.
 
 An icon of a clock Publish Date
     July 7, 2021
 An icon of a human figure Authors
     [55]Todd Kaufman
 An icon of a paper organzier Categories
     [56]Our Company
 
 [57] Celebrating Agent 00100 milestone
 
 Test Double celebrates hiring Agent 00100—a big milestone and a reminder about
 why we do this: to fix what's broken in software.
 
 An icon of a clock Publish Date
     June 1, 2021
 An icon of a human figure Authors
     [58]Todd Kaufman
 An icon of a paper organzier Categories
     [59]Our Company
 
 [60] 5 for 5000: Find your leading indicators
 
 It's easy to tune out talk of metrics and spreadsheets, but one of the best
 ways to ensure long-term success is to uncover the numbers that signal future
 events while there's time to act on them
 
 An icon of a clock Publish Date
     October 22, 2020
 An icon of a human figure Authors
     [61]Justin Searls
 An icon of a paper organzier Categories
     [62]Our Company
 
 Looking for developers? Work with people who care about what you care about.
 
 We level up teams striving to ship great code.
 
 [63] Let's talk
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 [63] https://link.testdouble.com/blog-cta-sales
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