This is primarily a story of a failure to supervise the creation of the report, rather than anything related to AI.
The role of the outsourced consultancy in such a project is to make sure the findings withstand public scrutiny. They clearly failed on this. It's quite shocking that the only consequence is a partial refund rather than a review of any current and future engagements with the consultancy due to poor performance.
There shouldn't be a meaningful difference if the error in the report is minor or consequential for the finding, or if it is introduced by poorly used AI or a caffeinated up consultant in a late-night session.
To expand on the overlooked point: it gives you a DB and a programming environment (however challenged) that you can use without needing sign-off from IT. In any moderately sizeable organization, getting approval to use anything but standard software is slow and painful.
Nobody wants to explain to IT that they need to install Python on their machine, or drivers for sqlite, or - god forbid - get a proper database. Because that requires sign-off from several people, a proper justification, and so on.
Being close to the edge of AI usage, it's important to realize that most AI use cases are not "fully autonomous AI software engineer" or "deep research into a niche topic" but way more innocuous: Improve my blog post, what's the capital of France, what are some nice tourist sites to see around my next vacation destination.
For those non-edge use cases, costs are an issue, but so are inertia and switching costs. A big reason OpenAI and ChatGPT are so huge is that it's still their go-to model for all of these non-edge use cases as it's well known, well adopted, and quite frankly very efficiently priced.
Reading through the source [1] they basically get to that huuuuge number by including AI-enabled devices such as phones that have some AI functionality even if not core to their value proposition. That's basically reclassifying a big chunk of smartphones, TVs, and other consumer tech as GenAI spending.
Of the "real" categories, they expect:
Service 27bn (+162% y/y)
Software 37bn (+93% y/y)
Servers 180bn (+33% y/y)
for a total of $245bn (+58% y/y)
That's not shabby numbers, but way more reasonable. Hyperscaler total capex [2] is expected to be around $330bn in 2025 (up +32% y/y) so that'll most likely include a good chunk of the server spend.
I mostly agree on the first point. Even prior to the price race to the bottom, no AI Lab managed to make any money above marginal cost on inference, let alone recoup investment in infrastructure or model training. Clearly, investment in infrastructure and model training have been largely subsidized by VCs. It's a bit unclear how much of a subsidy inference costs had. The fact that AWS runs hosted inference at roughly similar cost than AI Labs suggests to me that there's at least not a massive subsidy going on at the moment.
I don't subscribe to the narrative that nation states (i.e. China) massively support DeepSeek. Thus, while their core business as a hedge fund is clearly profitable, they have considerably less deep pockets and willingness to front losses than the investors in VC supported AI Labs. Consequently, I expect their inference cost to at least cover their marginal costs (i.e. energy) and maybe some infrastructure investment.
All that suggests that they've managed to lower cost (and with that presumable resource and energy requirements) of inference considerable, which to me is a clear game changer.
Feels surprising that there isn't a modern best-in-class non-LLM alternative for this task. Even in the post, they described that they used a hodgepodge of headless Chrome, readability, lots of regex to create content-only HTML.
Best I can tell, everyone is doing something similar, only differing in the amount of custom situation regex being used.
How could it possibly be (a better solution) when there are X different ways to do any single thing in html(/css/js)? If you have a website that uses a canvas to showcase the content (think presentation or something like that), where would you even start? People are still discussing whether the semantic web is important; not every page is utf8 encoded, etc. IMHO small LLMS (trained specifically for this) combined with some other (more predictable) techniques are the best solution we are going to get.
Fully agree on the premise: there are X different ways to do anything on the web. But - prior to this - the solution seemed to be: everyone starts from scratch with some ad-hoc Regex, and plays a game of whackamole to cover the first n of the x different ways to do things.
Best of my knowledge there isn't anything more modern than Mozilla's readability and that's essentially a tool from the early 2010s.
Have you ever seen the research coming out of some of the outsourcing shops that the OP discusses in the post? They are hard not living up to that standard. It's important to realize that this is input for the analyst at a fund or investment bank to do some more digging on the companies and in the process potentially discover more. This isn't going straight to the CEO to form the basis of an investment decision.
https://github.com/AddictedCS/soundfingerprinting Is the library. It was the first thing I found and it seemed to do the job. I’m not a .NET guy so it’s implemented as a CLI tool that takes a wave file as input and spits out a list of timestamps.
Sharing the code would mean getting into the business of publishing an ad blocker which is not something I personally have the bandwidth for. It’s also far from my finest work, being something that I banged out in a weekend. Happy to answer other questions about it, though!
The role of the outsourced consultancy in such a project is to make sure the findings withstand public scrutiny. They clearly failed on this. It's quite shocking that the only consequence is a partial refund rather than a review of any current and future engagements with the consultancy due to poor performance.
There shouldn't be a meaningful difference if the error in the report is minor or consequential for the finding, or if it is introduced by poorly used AI or a caffeinated up consultant in a late-night session.