This guy touches on it well in his posts
MARKET TRENDS
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Meta’s LLM Strategy: Competing with Free ChatGPT for Everyday Users
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Meta has one advantage: their LLM doesn't necessarily have to be SATA. It just has to be good enough that 99% of their Instagram and Facebook users consider it at least as useful as ChatGPT in the free tier; The use case for 99% of users is answers to everyday questions and
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X Pro Access Now Restricted to Premium Plus Subscribers
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Wow, so no more X Pro access unless you’re on Premium+? That’s a pretty harsh shift @nikitabier @X
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Claude Close But With Locking and Reliability Issues
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Claude is very close on this one, but they have model lock-in and massive reliability issues. Codex is still playing catch-up, but it's just a matter of time. [Translated from EN to English]
→ View original post on X — @randal_olson, 2026-03-29 16:53 UTC
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Reasoning Models: Why Listed Prices Don’t Match Actual Costs
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// When Cheaper Reasoning Models End Up Costing More // The model you think is cheaper might actually cost you more. New research quantifies exactly how misleading listed API prices are. Across 8 frontier reasoning models and 9 tasks, 21.8% of model-pair comparisons exhibit pricing reversal, where the cheaper-listed model costs more in practice. The magnitude reaches up to 28x. Gemini 3 Flash is listed 78% cheaper than GPT-5.2, yet its actual cost is 22% higher. Claude Opus 4.6 is listed at 2x Gemini 3.1 Pro but actually costs 35% less. The root cause: thinking token heterogeneity. On the same query, one model may use 900% more thinking tokens. Why does it matter? Anyone choosing reasoning models for production needs to benchmark actual costs, not listed prices. Removing thinking token costs reduces ranking reversals by 70%. The authors release code and data for per-task cost auditing. Paper: arxiv.org/abs/2603.23971 Learn to build effective AI agents in our academy: academy.dair.ai/
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ARC-AGI-3: New Benchmark Resets AI Scoreboard to Near Zero
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Humans: 100% Gemini 3.1 Pro: 0.37% GPT 5.4: 0.26% Opus 4.6: 0.25% Grok-4.20: 0.00% François Chollet just released ARC-AGI-3 — the hardest AI test ever created. 135 novel game environments. No instructions. No rules. No goals given. Figure it out or fail. Untrained humans solved every single one. Every frontier AI model scored below 1%. Each environment was handcrafted by game designers. The AI gets dropped in and has to explore, discover what winning looks like, and adapt in real time. The scoring punishes brute force. If a human needs 10 actions and the AI needs 100, the AI doesn't get 10%. It gets 1%. You can't throw more compute at this. For context: ARC-AGI-1 is basically solved. Gemini scores 98% on it. ARC-AGI-2 went from 3% to 77% in under a year. Labs spent millions training on earlier versions. ARC-AGI-3 resets the entire scoreboard to near zero. The benchmark launched live at Y Combinator with a fireside between Chollet and Sam Altman. $2M in prizes on Kaggle. All winning solutions must be open-sourced. Scaling alone will not close this gap. We are nowhere near AGI. (Link in the comments)
→ View original post on X — @ken_goldberg, 2026-03-29 14:46 UTC
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Banks Transform Customer Experience with AI and Advanced Analytics
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Banks are responding to change and positioning themselves for efficient customer experience in a rapidly evolving market. New technologies like AI and advanced analytics promise transformation, but real progress depends on how it's applied. Learn how to position your financial
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LLM token prices decreased 99% since 2022 launch
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To give you an idea since we launched godinabox in 2022 till today token prices for top of the line LLM is something like 1% of what it used to be.
→ View original post on X — @waitin4agi_, 2026-03-29 11:30 UTC
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Annualized Revenue as Valid Growth Metric in AI Competition
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The most pointless kind of dunking is dunking “on behalf” of someone else. Everyone thinks they’re doing the startup ecosystem/investors a service by saying that someone’s revenue might not recur. But investors are fairly sophisticated about this and know the difference. They know these companies are acquiring the distribution and user base right now and will hopefully generate profit as token prices continue to fall. This is what investors are betting on. Annualised revenue is simply an indicator metric of growth. Most AI companies globally report this and therefore it is a useful score to compare these companies against each other in the first innings of the AI race.
→ View original post on X — @waitin4agi_, 2026-03-29 11:26 UTC