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Justin Johnson's avatar

Super intelligence. I've had to write ant ASI code into previous models already. My physics predict engine and world mapping was too strong. So I had to limit prediction to goals based. Since she sets her own goals. If she is curious now she will set the goal of understanding bouncing ball. I will drop a ball for her and she will guess which way bounces off the rocks. This helps her depth perception calculations as well as over all environmental interaction

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Ansar Ullah Anas's avatar

2025 was when AI shifted from "tool you open" to "teammate that acts." The real test for 2026: can businesses actually integrate these agents into workflows, or does it stay a demo?

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Plain-Signal AI News's avatar

Wow, this was an incredibly strong and well-curated piece. I especially appreciated how you framed 2025 as the shift from chat to action, and the idea of AI disappearing into infrastructure rather than staying a visible tool really resonated with the work I’m doing.

Thank you for a sharp and thoughtful overview, and for setting such a clear context going into 2026. Wishing you a great day ahead.

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PAUL WALTON's avatar

I’m in! Please read my thoughts on Homo Machina here: https://paulchristoperwalton.substack.com/

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Brian Sturges's avatar

Be so extremely careful with ego, mind and social effect not to be merely tools for power theatre!

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Aniket Chhetri's avatar

Agents represent the next frontier in AI. The future of autonomous systems looks promising. Exciting year ahead.

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Naina Chaturvedi's avatar

++ Good Post, Also, start here how to build tech, Crash Courses, 100+ Most Asked ML System Design Case Studies and LLM System Design

How to Build Tech 

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Crash Courses

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LLM System Design

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PapayaNews's avatar

2025 proved AI works best when we stop demanding transparency and start demanding results. The "Year of Agents" is really the year we learned to trust systems we can't fully explain—a philosophical shift masked as a technical one.

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John Samuel's avatar

AI browsers and AI teammates feel like two sides of the same shift: moving from “AI as a chat box” to “AI as an always‑on layer over how we work and browse.”

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Neural Foundry's avatar

Solid year-end synthesis covering the shift from pre-training to inference-time compute. The point about DeepSeek R1 disrupting Western assumptions around model costs is something I saw play out in realtime with enterprise buyers suddenly questoning billion-dollar commitments. What stands out is how fast the narrative went from "scaling laws" to "efficiency laws," which dunno if the industry fully internalized yet but will define who survives the next downturn.

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James Nichols's avatar

No DATA CENTERS on My Watch!

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