Generally speaking, I agree with you. AI is very good at making good looking unit tests that don't actually test the important parts. But as long as you understand this, it still helps you with the sunny-day-tests and obviously if you are specific, it can even sometimes write tests that cause the code to fail. I recommend letting AI to find a bug in the code first and then ask it to write a test that triggers that bug. When executing the test, you will instantly know if the bug was real or not and if the test triggers it, AI managed to find a bug and write a test for it.

Google

I disagree. It depends on how you use it. You can either ask it to write the app for you, or you can ask it questions like "How do I write if-else using this language?" or "How do I store data into database in this environment" or "What does this code do?" "Explain this part me". AI is like a personal teacher, if you use it that way.

For things that you are good at, you can use it so that you first write or think of the solution yourself, then you ask AI to write a solution and compare them. Did the AI do something differently? Why did it do so? Which is better way of doing things? This way you can grow outside your own bubble.

But I do admit, that it is highly unlikely that a human would do this, because our brains are designed to look for the easy way out.

Non programmer writing unit tests with AI is dangerous. AI has a habit of writing unit tests that match the code, so the new tests will pass even if there are bugs in the code. I have seen AI doing this so many times that I must assume that it is normal for the AI to do so. 100% coverage, 0% value.

Despite this, AI is not useless for writing unit tests. You can ask it to find an error from the code and then ask it to write unit test that triggers that error. This way you can usually verify that the finding is an actual bug and you also have a test which can be used to verify that the bug gets fixed.

I agree that Claude is good, but the question is "If I would pay Google 2 trillion, could they train their AI to be better within 2 years?". Could they perhaps do that even with 2 billion or 200 million? That is how I would evaluate them.

If you were in Hiroshima in 1945, you could have died walking down the street because someone solved a physics problem.

I think that SpaceX is going to build the satellites on the Moon using robots and launch them using rails to minimize launch cost. In theory is sounds like a good plan.

Personally, I think we will invent more efficient AI faster than the factory is up and running, but I have to admit that SpaceX makes the world more interesting so I have nothing against them.

I have seen Gemini improving even in my own private benchmarks.

"Gemini 4 Argon, which is rolling out to a set of trusted cyber defenders through our Fairwind Program"

Google said already in 2019 that they moved their focus from gaming to other areas.

Medical research is not a finite resource. The more money you have on the field, the more people will join the field. And pretty much any research you do, can benefit other research in unexpected ways. There are even some worm researchers and yogurt researchers who have made significant contributions to medical research.

Looking at the benchmarks it looks like it is better than the previous version, so that is probably why they released it. So I don't think that it is rushed.

"One employee built an app that turned a nearly four-hour manual process into a one-second automated check, saving tens of thousands of hours annually. "

This is the reason why AI will take jobs. World is full of tasks that are dead easy to automate, but no-one has just not done it yet. AI doesn't have to be smart, it doesn't even need to be good. If it can write a script that compiles, it can automate.

In 2025 Claude had pass rate of 2% in FrontierMath. In 2026 Claude had 88%. Perhaps we should just wait a few years and see if the pass rates stop increasing?

Source:
https://epoch.ai/frontiermath/...

Grades increase extrinsic motivation, but they often hurt intrinsic motivation. I've been thinking that we should replace grades with a single percentage number per subject. This number would tell that you have learned for example 12% of math. This percentage would be calculated automatically based on your answers on controlled environment (tests or class room work). Behind this number would be actual data that shows exactly the things you have not yet learned, so teaching could be targeted there. But as tests would sometimes test past knowledge also, the number might drop also.

The benefit of this single number would be that it would be very simple way to tell how much knowledge a person has and long term learning would provide better scores than studying just for the test.