Yes, to a gross approximation, scientists cannot and do not advance their career by giving a negative result. That is just how it is, anyone who’s tried being a scientist for a day realizes this.
In ML for example, if you try some weird idea, and it does not beat baseline methods on any specific benchmark, then your paper 9.99/10 will not get accepted. In fact I don’t think I’ve ever seen a negative result paper ever get accepted into a good ML conference. At the very least, the authors make up their own benchmark and claim their method is best in their own benchmark, and the reviewers then quibble about whether such a benchmark is relevant, and then after back and forth, they come to a decision and decide to accept the paper or not.
In ML for example, if you try some weird idea, and it does not beat baseline methods on any specific benchmark, then your paper 9.99/10 will not get accepted. In fact I don’t think I’ve ever seen a negative result paper ever get accepted into a good ML conference. At the very least, the authors make up their own benchmark and claim their method is best in their own benchmark, and the reviewers then quibble about whether such a benchmark is relevant, and then after back and forth, they come to a decision and decide to accept the paper or not.