I imagine I'll be editing this a bit, so I apologize if there are obvious typos left from any changes I make while I'm thinking. Sorry for the mini-essay. :)

Also, these points are not to be taken separately. They're part of a broader argument and should be treated as a unit.

1. Programming competitions are deliberately scoped down. Actual day-to-day work consists of meeting with stakeholders, conducting research, synthesizing that research with prior knowledge to form a plan, then executing. This work skips to the plan synthesis, relying on pattern-matching for the research component.

2. This current work, even if refined, would be insufficient to conduct daily programming work. This is just an extension of point 1; I acknowledge that you're talking about the future and a hypothetical better system.

3. The components required for your hypothetical programming bot are the components not covered by this work.

4. Context-aware/deep search tools are still very incomplete. There are some hints that better user-intent models are around the corner (i.e. companies like TikTok have built models that can adroitly assess users' intents/interests). I've seen no work on bringing those models to bear on something more nebulous like interpreting business needs. (But I also haven't been actively searching for them) Also, Google, who dumps a large amount of money into search every year, is among the best we have and it's definitely far from what we'd need for business-aware programming bots.

5. Conducting the research step in the programming process automatically will require better tools.

6. Conversational AI is still very incomplete. See Tay bot from Microsoft for examples of what goes wrong at scale. People, in general, are also not very aware of themselves during discussions and even very intelligent people get locked in a particular mindset that precludes further conversation. If a user tries fighting the bot by insisting that what they said should be sufficient (as they definitely do to other humans) that could pollute the bot's data and result in worse behavior.

7. Meeting with stakeholders part of the programming process automatically will also require better tools.

8. By points 5 & 7, critical domains still require more research. There is ongoing research in fields like Q&A, even some commercial attempts, but they're focused on mostly low-level problems ("construct an answer given this question and some small input")[0].

9. Advanced logical reasoning is advanced pattern matching + the ability to generate new reasoning objects on the fly.

10. Current systems are limited in the number of symbols they can manage effectively, or otherwise use lossy continuous approximations of meaning to side-step the symbol issue (it's a rough approximation of the truth, I think). See [1] for an up-to-date summary on this problem. Key phrase: binding problem neural networks

11. Current "reasoning" systems do not actually perform higher level reasoning. By points 9+10.

12. Given the rich history and high investment over time these fields (points 4, 6, and 11), it is unlikely that there will be a sufficiently advanced solution within the next 15-40 years. These fields have been actively worked for decades; the current influx of cash has accelerated only certain types of work: work that generates profit. Work on core problems has kept going at largely the same pace as usual because the core problems are hard-- extra large models can only take you so far, and they're not very useful without obnoxious amounts of compute that aren't easily replicated.

13. Given the long horizon in point 12, programmers will likely be required to continue to massage business inputs into a machine-usable format.

The horizon estimate in point 11 was a gut estimate and assumes that we continue working in parallel on all of the required subproblems, which is not guaranteed. The market is fickle and might lay off researchers in industry labs if they can't produce novel work quickly enough. With the erosion of tenure-track positions taking place in higher education (at least in the US) it's possible that progress might regress to below what it was before this recent AI boom period.

[0]: https://research.facebook.com/downloads/babi/
[1]: https://arxiv.org/pdf/2012.05208.pdf