Programmers are modern-day computers

&gt; <i>Tomorrow&#x27;s job [...] will look a lot more like:<p>[...]<p>3. evaluating whether the end result works as intended.</i><p>I have some news for the author about what 80% of a programmer&#x27;s job consists of already today.<p>There is also the issue #4, that &quot;idea guy&quot; types frequently gloss over: If things do <i>not</i> work as intended, find out <i>why</i> they don&#x27;t do so and work out a way to fix the root cause. It&#x27;s not impossible that an AI can get good at that, but I haven&#x27;t seen it so far and it definitely doesn&#x27;t fit into the standard &quot;write down what I want to have, press button, get results&quot; AI workflows.<p>Generally, I feel this ignores the inherent value that there is in having an actual understanding of a system.

I strongly disagree with this. “Computers” would have not been replaced with the machines that replaced them if those machines routinely produced incorrect results.<p>One could argue that for applications where correctness is not critical my position does not apply, however this is not the analogy that the article is making.

What a superficial and short-sighted take.<p>&gt; Even he, subconsciously, knows it doesn&#x27;t pay off to waste cognitive energy on what a machine could do instead.<p>&gt; It&#x27;s not laziness, but efficiency.<p>It&#x27;s only efficient in the short-term, not long-term. Now the programmer never understood the problem, and that is a problem in the long-run. As any experienced engineer knows - understanding problems is how anyone gets better in the engineering field.

For this I&#x27;ve always called myself &quot;software plumber&quot;.<p>I quite liked that analogy about excavator because I think along similar lines. Mechanising drudgery work in construction industry enabled us to build superstructures that are hundreds of stories tall. I wonder if LLMs will enable us to do something similar when it comes to software.

Okay, but what will you actually do when your LLM writes code which doesn&#x27;t actually error but produces incorrect behaviour, and no matter how long you spend refining your prompt or trying different models it can&#x27;t fix it for you?<p>Obviously you&#x27;ll have to debug the code yourself, for which you&#x27;ll need those programming skills that you claimed weren&#x27;t relevant any more.<p>Eventually you&#x27;ll ask a software engineer, who will probably be paid more than you because &quot;knowing what to build&quot; and &quot;evaluating the end result&quot; are skills more closely related to product management - a difficult and valuable job that just doesn&#x27;t require the same level of specialisation.<p>Lots of us have been the engineer here, confused and asking why you took approach X to solve this problem and sheepishly being told &quot;Oh I actually didn&#x27;t write this code, I don&#x27;t know how it works&quot;.<p>You are confidently asserting that people can safely skip learning a whole <i>way of thinking</i>, not just some syntax and API specs. Some programmers can be replaced by an LLM, but not most of them.

Oh boy, I sure love to see some guy with a blog waxing poetic about the future of AI. The insistence that &quot;most people haven&#x27;t realized come to terms with it yet&quot;, and then proceeding to make a total fucking guess out of his ass.<p>Reminds me of that one asshole who couldn&#x27;t find a pen, and wrote a whole NYT article about &quot;the demise of the pen&quot;.

indeed. I&#x27;ve changed my title to &quot;Staff Software Conductor&quot; since I just wave my hands in the air with little sticks called cline and aider.

Premises seems completely flawed to me. It’s like if we would say because we have calculators, knowing basic arithmetic mentally will no longer be a necessary step to go through. All the more when the these calculators are actually not giving you exacts results, but excel at producing a lot of plausible lookalike approximations based on large corpus of computation samples.<p>So it seems not only very wrong on what is the hard part in programming, but also misguided about where (current) LLM use can shine, including in programming context.

Those three steps at the end do a lot more heavy lifting than the author appears to suggest.

&#x27;Did the computer create a generation of &quot;illiterate mathematicians&quot;?<p>No, it only freed us to solve higher-level problems.&#x27;<p>I&#x27;m not so sure LLM-based code-assistants&#x2F;writers are directly comparable to the almost perfectly deterministic logic gate chips and monitors&#x2F;printers and so on that we call &quot;the computer&quot;.<p>Wordpress is likely a better comparison, which allowed more web sites to come into existence with little effort, but as far as I can tell didn&#x27;t free anyone to &quot;solve higher-level problems&quot;. Deterministic code generation might have to some extent, but it&#x27;s mainly used in enterprise software where the problem domains are quite old, quite stable, like accounting, and the ontologies haven&#x27;t changed since before &quot;the computer&quot; and the established knowledge professionals (accountants, &#x27;legal&#x27;) have a strong dominance over discourse, software rules and how compliance is achieved.

While I have been amazed by the progress of LLMs and believe that this ultimately leads to AGI, I still think that LLM aided development is limited to things that are often done and are well documented in the training corpus. Writing code with new libraries or less documented lower level apis still requires you to actually do it. LLMs slowly separate you from your understanding of what your code is doing, and if they don’t meet a minimum threshold of proficiency on a topic, they will slowly and insidiously degrade your codebase until you realize Claude (or whoever else) has built a house of cards.

Except that going from human computers to mechanical computers made us more precise and more accurate at doing calculations, not less.<p>Modern AI assistants have yet to demonstrate greater level of competence than even an inexperienced human programmer. Anyone telling you otherwise has fallen victim to extrapolation.

Just ask yourself if this AI generated software is what you want managing your bank account or the monitor next to your bed in the hospital. The lack of determinism makes LLMs unsuited for many tasks where precise outcomes are necessary.

This guy is wrong and this comment gives as much reason to believe me as he&#x27;s given to believe him.

&gt; Anyone who tells you otherwise is deluding themselves.<p>Always good when a blog author ends with an ad hominem not supported by their claims.<p>It would have been more useful if they had explained how Rice&#x27;s theorm was falsified, and that HALT&#x2F;FRAME have been invalidated.<p>In my experience, for any problem that is non-trivial, LLMs require more knowledge and tacit experience.<p>Programming has always been more about domain understanding etc...<p>Things will change, but this is new tooling which requires new knowledge. I have had to start explaining Diaconescu&#x27;s Theorem to a lot more people as an example.

This article read like one of those annoying LinkedIn “hot takes” that keeps showing up on my feed. I’d suggest sharing this type of content there rather than on HN, please.

&quot;Those are the very skills that are going to become a lot less relevant, for the precise reason that, now, the machine can do those things.&quot;<p>A photocopier could steal books, a VHS recorder could steal movies, a computer could steal digital works.<p>LLMs now steal pseudo-legally. Any person without morals could have done the exact same thing before LLM laundromats by just using Google search and GitHub.<p>&quot;Anyone who tells you otherwise is deluding themselves.&quot;<p>No, you are deluding yourself.

While I don’t disagree with the author, I think he misses the larger point that there are two distinct groups of programming code writers, that I’ll call “eager” and “reluctant” until I can think of better terms.<p>“Eager” programmers find great joy in producing reams of computer code. They prefer to have iron-clad requirements handed to them and then work uninterrupted turning those requirements into a program.<p>“Reluctant” programmers have problems to solve, and have found that the quickest way to do that is to write code. If solving the problem is faster by not writing code they will do that.<p>“Computers”, that is, the job formerly performed by humans, favored the “eager” type of person. The people giving work to Computers were “reluctant”, and moved that work to faster tools when they became available.<p>The issue is that we have armies of “eager” programmers. Can they adapt to become “reluctant” or does the thought of writing less code cause them to resist change?

I sure wish I had access to the crystal ball people like the author and linked-in top-voices have that enable them to utter with unawavering conviction those decrees, while warning that everyone that doesn&#x27;t agree with them are &quot;deluding themselves.&quot;