d_burfoot

> I am not one of those AI people who believes that hundreds of millions of jobs will be wiped clean from existence in the next five years

"Wiped clean" is the wrong phrasing, and 5 years is a short timeframe given the ability of some sectors of the economy to resist change. But yes, hundreds of millions of white collar professionals will need to rethink their careers and their business models within the foreseeable future.

Just today there was an HN article about how shady the dental industry is. In the future, you will go to a special dental imaging facility where a modestly-compensated technician images your whole mouth and feeds the results into an AI system, and when you get home you can look at the assessment and chat with the AI about it. Then you can potentially go visit a dentist, oral surgeon, etc, if you actually need work. This system will produce far better dental health outcomes at far lower cost: the diagnoses will be more accurate, and there will be no bias towards additional, unnecessary procedures.

> they can't have Top Secret Meetings where a merger or acquisition is discussed

This actually seems completely doable for a properly configured LLM. There will probably be a platform in the future where companies can setup LLM to discuss business deals with other companies' LLMs and bring the resulting proposals to the human leadership. Every morning the CEO gets an email about the 3 most promising potential deals that were discussed, a summary of the big picture with TBD details highlighted, and a link to the transcript of the conversation. Plausibly the potential deal is with a company the CEO had never heard about or dealt with.

Partly this is because the big companies are diverting vast amounts of money towards building data centers, instead of hiring new grads. I expect young people who learn AI skills will be fine, once the data center spending binge is over.

Author's bio:

> Annie Sexton is a Developer Educator at ngrok with a passion for nerd-sniping developers.

Tip for smart science-y young people: think about a career in experimental physics. Experimental data is the complement of theoretical power. Since theory can be provided cheaply by LLMs, experimental ability is now the bottleneck for progress in physics.

I expect to see frontier labs or startups hiring experimentalists to provide data for LLMs to analyze, pushing towards breakthroughs in areas like room-temperature superconductors and fusion.

> the more you spent building A.I. infrastructure, the more you would earn, or so the logic went. Computer theorists called it the scaling hypothesis.

Good Gell-Mann Amnesia moment.

Every single knowledge worker is going to face this kind of crisis. It will be worse for people who cared deeply about their craft, but the crisis will have the same shape. The way through is to change the way you relate to your work, and to appreciate the fact that you can now produce far more output.

Mathematicians should realize: instead of spending decades on individual theorems, you can now create whole new subfields of mathematics. Of course, you won't be doing the creation directly, in the way the mathematicians of old did; that's just not the name of the game anymore. But you will still be directing the research and involved in the creation.

A good puzzle for political philosophers: how do you build a decent system of government in a world where people to not listen to reasoned arguments?

> What possible use could there be for doing this?

The point is to generate an enormous unlabeled dataset. Historically, ML for medical imaging depended on a small number of labeled images - small because you needed to have an expert study the image and label it as healthy/cancer/etc. But the "GPT breakthrough" was that it was better to use vast unlabeled datasets - in the case of LLMs, text - than small labeled ones.

There's a kind of Efficient Market Hypothesis of career advice that I wish PG took better notice of.

If a career path (e.g. startup founder) outperforms at time T1, then this fact will diffuse quickly throughout society, causing the path to become overcrowded, which pushes down the average performance. So at time T2 the path will no longer outperform. This is analogous to a stock becoming overpriced due to hype. I consider the founder path to be enormously overcrowded at this point.

The key to finding a good career is to play a kind of Money Ball - find paths that, for whatever reason, are mispriced and thus undercrowded.

39T is just the outstanding value of all the Treasury instruments (T-bills, etc). The entitlement programs (SSA and Medicare) have made commitments to certain levels of service and payments that are far in excess of the revenues they bring in, that's what is meant by "unfunded liabilities".