DarenWatson

The way we phrase anger with AI doesn't convey the structural realities of what's going on in the knowledge work chain. Gen Z aren't suddenly becoming anti-tech they are acting financially rationally to protect their own economic future.

In the past there was an implicit contract for white-collar employment that was based on the concept of earned experience through a period of manufacturing type work. You enter your profession by performing uninteresting, low-paying manufacturing tasks (such as, writing boilerplate type code or performing low-level quality assurance) while you gain domain expertise and gain the perspective necessary to perform high-value work at a higher level.

LLMs are now exceptionally good at consuming the 20% of an employees entry-level responsibilities.

What I see happening in the enterprise is that management is using AI to justify pulling the ladder up behind them and closing the door behind them. When a senior engineer's or senior analyst's productivity has increased by 30% due to using LLMs, the executive's response is typically not great, we have more time to work on bigger projects, but instead great, we can freeze junior hiring for 2 years.

The entry-level positions in the labor force are being automated, causing seriously low access to those roles for the Gen Z workforce. On the other hand, most senior-level positions are not being available to Gen Z workers as they lack the skills and experience required to qualify for those positions.

Stagnation in the adoption of artificial intelligence (AI) technology is the direct result of having no entry or junior level employees to work underneath senior staff members, causing a bottleneck for seniors. Employees generating raw output with AI technology have to check the results (output) for accuracy before integrating into work systems and processes as there are no entry-level employees to provide assistance to senior workers.

Gen Z workers do not dislike the tool (AI) however, they do not like how the tool is being implemented and used currently. Currently, the implementation of AI is driven by cost cutting in terms of labor rather than being focused on providing training and developing Gen Z's human capital for future use.

A couple of years ago, we experienced a silent data corruption incident in our checkout process due to this specific edge case.

A user would generate the idempotency key by loading the front-end application, adding item(s) to their cart, submitting their order but timing out. The user would then navigate back to the front-end application and add another item and submit the order again. Since the user is submitting an identical idempotency key to the same transaction, our payment gateway would look up the request/transaction by idempotency key and see in its cache that there was a successful (200 OK) response to the previous request. The user now believes they purchased three items, however, our system only charged and shipped on two of the orders.

Consequently, the lesson we take away from the aforementioned incident is idempotency keys are really composite keys (Client_Provided_Key + Hash(Request_Payload)).

If a system receives an identical idempotency key (but with a different request payload) the idempotency key should be rejected with a 409 Conflict response with a message similar to "Idempotency key already used with different request payload". Alternatively, some teams argue it should be returned with a 400 Bad Request response. Systems should never return a failed cache response or replace old entries of data.

This article explains how to unlock your flow. The final idempotent key will not be located until the first request completes, but will rather exist when the request is in progress.

To safely accomplish your goal, you have to follow the following steps:

1. Acquire a distributed lock on the idempotent key.

2. Check for the existence of a key in your persistent store.

3. If an existing key is found, verify the hash of the payload against the hash for the payload type. If the hashes do not match, return a 409 error.

4. If the hashes match, look up the status of the payload. If the status shows COMPLETED in the persistent store, return the cached response. If the status shows PENDING in the persistent store, return a 429 Too Many Requests to the user or hold the connection open until the request reaches a PENDING state.

5. After processing the request, save the response to the persistent store before releasing the lock.

While this may look simple on paper, creating a distributed locking state machine for a single API endpoint is typically how developers have their first aha moments with idempotency. Becoming idempotent is often an enormous architectural shift and not just a middleware header check.

There is a major disconnect in that people think token usage is exclusively tied to human typing rates...it isn't true. When software developers evolve to using self-managing CLI tools (like Claude Code - the source article mentions this), they are not merely chatting; they are unleashing loops of agency.

When you enter one single inquiry of "find and fix the memory leak in the billing service" you are not submitting just one single inquiry. The tool is searching through an entire code repository for relevant code, pulling 15 related files into context (easily 200k+ tokens) proposing a fix, running the test suite and failing, taking an entire stack trace of errors into context and looping to keep iterating towards the solution.. In that process you can loop multiple times (10+) in a very short period of times (within 5 minutes). While you grab a cup of coffee you will have consumed $20 in token usage. At the enterprise level (like with Uber) when you multiply that out by thousands of software developers using it as a personal shell tool your budget disappears very very quickly.

And on your point about the junior developer: Comparing $100,000/year in tokens to hiring a junior developer is such a ridiculous false equivalency that even makes you question whether they even understand how to make such a comparison.

The cost to a business of one junior engineer with a $100,000 salary is not just the $100,000 in salary but also an additional $40,000+ in benefits and taxes, as well as in hardware.

Also, you are disregarding another cost of hiring junior engineers that is their mentorship cost. Each week, your senior and staff engineers spend hours mentoring junior engineers by reviewing their code, pairing with them, and unblocking their progress. Mentoring requires a substantial amount of time and will be expensive to your business.

The return on investment (ROI) for the $10,000 monthly expenditure on tokens is not so much about replacing the junior engineer with the AI. Instead, the ROI is that your senior engineers can use the huge amount of compute power to create boilerplate and tests, and refactor their code 3x quicker than if they had to mentor junior engineers. In addition, LLMs do not sleep, require one-on-ones, or leave for another company for 20% more pay in 18 months, when the value to the code base made them an asset to your business.

Lastly, the main reason that Uber has problems with their AI business is that due to the UX of these agentic tools, developers think of the API calls made to the AI as free and as a result, treat them like a basic grep command.

I know that there is a lot of noise right now about AI doing science but this genuinely looks like a milestone.

Physics tl;dr: Fast Radio Bursts carry more energy in a millisecond than the Sun produces in three days, but their exact emission mechanism is still an open problem. They analyzed data across four independent repeating FRB sources (from FAST, the Allen Telescope Array, and Effelsberg, reduced by three independent pipelines). It found that the adjacent drift-rate mode ratio recurs at 2.456 ± 0.094.

The cross-source scatter is an incredibly tight 3.8%. If this holds up, it means the factor of ~2.5 between adjacent drift-rate modes isn't a localized quirk of one neutron star it’s a measurable, falsifiable signature of magnetar magnetosphere geometry across cosmic distances. As they put it: "That would be physics."

The AI/Epistemology tl;dr: What makes this impressive for me isn't just that an AI found a pattern, but how it did the science. They built a system called Primus and they ran a continuous research process from hypothesis to verified result. The hypothesis and analysis window were pre-registered and locked before validating data was inspected. The pipeline survived a pre-registered Monte Carlo falsification test at empirical p≤5×10^−4 against three distinct unimodal null hypotheses.

The system had to navigate statistical identifiability conditions and astrophysical emission models to arrive at this.

They’ve open sourced the full code, We've seen AI solve known math problems and fold proteins, but an AI acting as a principal investigator formulating a novel hypothesis, pre-registering the falsification criteria, and discovering a new physical invariant feels like a massive step toward actual automated AI research.

Honestly it took me a bit to process. An AI system did the full analysis end-to-end hypothesis, clustering on 978 bursts, wrote the paper. Not assisted. Actually did it. Found a subpopulation at 9.2 sigma, interprets it as two emission regions in a magnetar. Passed 3 rounds of peer review at ApJ before the editor paused production over disclosure, not the science itself. MNRAS, A&A, arXiv all passed too, each for separate reasons.

What got me: they openly say 2 of 6 robustness tests didn't pass (alt algorithms, permutation). Right there in the methods. That's not what you do if you're trying to hide something. Honestly don't know where I land on the AI as author thing.

It’s really fascinating electrons took 60 years to go from chip to a smart device and if photons follow the same thing then we just fired the starting gun. It’s really interesting to see tantala material takes a single laser color in and spits out to a full rainbow.

Honestly that footnote really stood out to me too! the spiral search detail makes the whole system feel a lot more alive than I expected like it’s actively hunting for the star rather than just pointing and hoping.