Its funny watching Open AI and Anthropic play the role of IP defenders while quietly sliding Deepseek's KV cache research into their minor releases to salvage their unit economics
Disliking clunky autocomplete is very different from going out of your way to replace Word with Libre Office. The average office worker will tolerate ten bad assistants before they voluntarily deal with ODF formatting quirks in a corporate environment
Glad to hear they are prioritizing evaluation right from the start. Usually people just throw together a rag pipeline on the knee and then judge the metrics by eye, skimming three responses in the terminal
Non-techies avoid the CLI like the plague and don't want to mess with .gitattributes for text files. But building a whole web service with a paid subscription just for that is total overkill when free desktop GUIs like Sublime Merge or VS Code exist
With that logic you could call SQL injections a natural feature of database management systems. If a general purpose system starts dropping tables or messing up numbers in a report just because that string was in the text it read, that system isnt worth a damn in the enterprise sector
Another important thing to keep in mind here is the software. Nvidia has been optimizing their software stack for decades to squeeze every clock cycle out of the hardware under any limits. TensorRT alone does straight up magic. Apple is just starting out with MLX, and their hardware is often idling not because it is worse per watt but because the compiler does not know how to optimally load the ALU blocks for specific graphs yet
AI makes it cheaper to create working fragments. It does not automatically make those fragments part of a maintainable system. In practice it may make the canonization step more important not less
Everything depends heavily on the segment. Open-weight models already look good enough for a ton of internal tasks right now but there is always gonna be that small percentage of workloads where the last few percent of quality are totally worth the money.
I feel like the market is just gonna become way more mixed. Not like "everyone is switching to open" or "everyone is staying on frontier" but a mix of multiple models for different scenarios
The weakest part of the article is that the forecasts up to 2029 assume the current market structure will barely change. In three years literally everything can change, like prices, models, hardware, and how we actually use LLMs
That is exactly why big clouds never put all their eggs in one basket, even if it is a super cheap and cold basket. The cost of protecting and backing up network lines for an isolated island quickly eats up any benefits from geothermal energy. Physical security for terabit lines is way more expensive than air conditioning these days
Iceland and Norway are part of the EEA so the AI Act and GDPR will reach them just like Germany or France. Running away there from regulators makes no sense. But running from bureaucracy to get land permits and substation connections - yeah maybe municipalities work faster there
I feel like this is way too binary. I don't have to write every line of code myself to understand the system. I don't write my own compiler HTTP stack or database either
It's more about the level of abstraction. If AI handles 80% of the grunt work and I spend my time on architecture and reviews that's still a win
I feel like the author is jumping way too fast from "OpenAI is losing money" to "the whole AI economy is broken." A company being in the red during aggressive scaling doesn't automatically mean the unit economics don't work.
That's why a hybrid approach is needed. The agent shouldn't be making up dimensions based on an image. It should use OCR to extract the size table from the datasheet, feed it into a parametric table, and only then map it onto the base enclosure template.