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[1]Test Double The Test Double logo
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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 clock Publish Date
March 14, 2023
An icon of a human figure Authors
[11]Justin Searls
An icon of a human figure Authors
[10]Justin Searls
As a young lad, I developed a habit of responding to the enthusiasm of others
with fear, skepticism, and judgment.
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.
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.
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.
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.
There’s little doubt at this point: the tools that succeed [11]DALL•E
and [12]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.
While [13]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.
If truckers deserve [14]years [15]of [16]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.
So let’s do that.
[18]Now it’s time to major bump Web 2.0
[17]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.
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.
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 [18]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 [19]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.”
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.
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.
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
[20]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.
If you’re reading this, you don’t need me to recap the cultural impact
of [21]ChatGPT and [22]Bing Chat for you. Suffice to say, if Google—the
runaway winner of the Web 2.0 economy—is [23]legit shook, there’s
probably fire to go with all this smoke. Moreover, when you consider
that [24]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!
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 [25]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.
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
[26]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?
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.
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.
Having spent months programming with [27]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
“[28]fluent bullshit.” And after months of seeing friends “cheat” at
their day jobs by having [29]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:
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
• 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 [30]more [31]time to [32]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
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:
I got so frustrated asking it to help me troubleshoot my VS Code task
configuration that [33]I recorded my screen and set it to a few lofi
tracks before [34]giving up.
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
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?
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.
[35]Three simple rules for keeping your job
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?
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.
[36]Three simple rules for keeping your job
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
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.
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 [36]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)
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:
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
[37]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 [38]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 “[39]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
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
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).
To illustrate, each of the following professions have survived previous
revolutions in information technology, but will find themselves under
tremendous pressure from generative AI:
So if you’re a programmer like me, you’re probably safe!
• 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)
Job’s done. Post over.
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:
[40]Post not over: How can I save my job?
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
So what can someone do if their primary role doesn’t produce work that
checks the three boxes of novelty, unpredictability, and fragility?
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).
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 [41]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
So if you’re a programmer like me, you’re probably safe!
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.
Job’s done. Post over.
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.
[41]Post not over: How can I save my job?
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 [42]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 [43]former editor-in-chief of CNET)
So what can someone do if their primary role doesn’t produce work that checks
the three boxes of novelty, unpredictability, and fragility?
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!
Here are a few ideas that probably won’t work:
[44]In conclusion
• Ask major tech companies to kindly put this genie back into the bottle
I got tired after writing 2400 words on this topic, so I asked ChatGPT
to write a conclusion after feeding it this post.
• Lobby for [42]humane policies to prepare for a world that doesn’t need
every human’s labor
Here’s what it spat out:
[45]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.
• 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
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:
[46]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.
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.
So, in conclusion: eat more olives. 🫒
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.
[47]Justin Searls
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:
An icon of a human figure Status
Double Agent
• 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)
An icon of a hash sign Code Name
Agent 002
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!
An icon of a map marker Location
Orlando, FL
[45]In conclusion
[48]Twitter [49]Mastodon [50]Github [51]LinkedIn [52]Website
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:
[53]Policy update: We’re still remote
[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.
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 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
An icon of a human figure Authors
[54]Todd Kaufman
[57] Celebrating Agent 00100 milestone
An icon of a paper organzier Categories
[55]Our Company
Test Double celebrates hiring Agent 00100—a big milestone and a reminder about
why we do this: to fix what's broken in software.
[56]Celebrating Agent 00100 milestone
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
Test Double celebrates hiring Agent 00100—a big milestone and a
reminder about why we do this: to fix what's broken in software.
[60] 5 for 5000: Find your leading indicators
An icon of a clock Publish Date
June 1, 2021
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 human figure Authors
[57]Todd Kaufman
An icon of a paper organzier Categories
[58]Our Company
[59]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
[60]Justin Searls
An icon of a paper organzier Categories
[61]Our Company
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.
[62]Let's talk
[63] Let's talk
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References:
[76]Test Double
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[1] https://testdouble.com/
[3] https://testdouble.com/
[4] https://testdouble.com/agency
[5] https://testdouble.com/services
[6] https://testdouble.com/careers
[7] https://blog.testdouble.com/
[8] https://testdouble.com/contact
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[10] https://blog.testdouble.com/posts/
[11] https://blog.testdouble.com/authors/justin-searls/
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[16] https://www.nytimes.com/2017/08/11/business/dealbook/teamsters-union-tries-to-slow-self-driving-truck-push.html
[17] https://www.nbcnews.com/business/autos/millions-professional-drivers-will-be-replaced-self-driving-vehicles-n817356
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[26] https://www.youtube.com/playlist?list=PLIuJbrOVyGjkRj7UM_whr-CPoqcXTOsZa
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[40] https://en.wikipedia.org/wiki/Emergent_Design
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[67] https://testdouble.com/careers
[68] https://blog.testdouble.com/
[69] https://testdouble.com/contact
[70] https://mastodon.social/@testdouble
[71] https://github.com/testdouble
[72] https://www.linkedin.com/company/testdouble
[73] https://twitter.com/testdouble
[74] tel:+16143494279
[75] mailto:hello@testdouble.com
[76] https://testdouble.com/privacy-policy
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