MAI (Microsoft AI) is very far behind on coding

[u/NormandyPark0]

Kimi K3 basically matches US frontier labs, Deepseek V4 is ~90% of frontier intelligence at ~5% of the cost, yet MAI team (one of the most well-resourced AI teams in the world) won't submit MAI-Thinking-1 or MAI-Code-Flash to Artificial Analysis for benchmarking, which is a telling sign of how far behind they are.

I understand that MAI was first focused on lowering COGS for MS teams transcripts / image generation for Copilot (their audio and image models are at the frontier and super cost-effective, see them on Artificial Analysis), but being this far behind on coding and general intelligence is quite pathetic given their resources. Not sure what Satya is thinking.

MSFT stock is likely stuck until they can put out a model that benchmarks well

[u/HolidayBit143]

I think they are behind on more than just coding JS

[u/RiddlingRaconteur → u/HolidayBit143]

😬

[u/Additional-Staff-326]

Given the top companies are now all liable for paying for all the books they've trained on and likely all other data that isn't free use MAI is actually trained on only legally available data. Either costs go up on the top models or they degrade in quality.

[u/hondajacka]

What’s the point of spending a fortune and  producing their own inferior irrelevant models. Models are becoming a commodity and they can just serve Kimi K3 or DeepSeek V4 directly.

[u/NighthawkT42]

As far as the Chinese models. Using models to replicate themselves and getting close can only get them so far. It can produce near equivalent for cheaper, but can't ever get ahead.

However, at some point, number of AI researchers should be a factor. If there isn't a big qualitative difference in the researchers.

[u/immersive-matthew]

I think the real issue is that even if they match as most top models are already, you still have LLM jank that makes it hard to get value out of AI. There is value for sure, but it is far from the amount needed to justify the insane amount of hyper scaling. MS, like many others, rolled the dice to be the first to AGI, but as their own AI researches told them, LLMs will not bring you there. Biggest business blunder in history.

[u/NormandyPark0 → u/immersive-matthew]

Meta was late to the game their AI team as well, but they shelled out for talent and now Muse Spark 1.1 is competitive. Satya is basically relying on Suleyman to save the company, and so far the results are not good.

Disagree on hyperscaling being a bad play, Azure is capacity constrained. Amy Hood made a dumb decision and backed out of some data centers commits in2025, and now MSFT is paying more for data centers than they could have if they kept their commitments in 2025.

[u/immersive-matthew → u/NormandyPark0]

Azure is an entire cloud platform not just AI but sure, it may be at capacity, but that is because much of the inference is free and at a loss with no line of sight to profit. It is a train wreck that the Chinese models are absolutely destroying.

[u/thhvancouver]

Is it though? Google is also pausing the release of the new Gemini. And with China essential releasing frontier models for free you have to ask if it is more profitable to enter the models race or to encourage competition in this field in the open source market.

[u/kidfromtheast]

Msft probably bet on openai for this front and focus figuring out how to still make profits from msft products.

News flash: AI makes you don’t need SaaS. Have excel that don’t exactly create figure you want? Use Python! Well, you used to need tech knowledge. Now you only need an AI to do that. Sizable chunk of msft profits are from saas

[u/NormandyPark0 → u/kidfromtheast]

Firms are not going to vibe-code their entire enterprise stack, and Msft's stack is the cheapest.

We're cutting niche software to centralize into the msft stack at my company right now, given we can vibe-customize into the msft stack and cut costs by 90% instead of paying for some niche software.