AgenticOrange

AI systems | Agent architectures | Future of work
Enterprise software operator exploring how AI reshapes execution.

[AgenticOrange @AgenticOranges]

Most people think the AI revolution is about models.

Claude vs GPT vs Gemini vs open models.

That’s the visible layer.

But historically, the biggest value in technology never sits in the most visible layer.

It sits in the control layer.

[AgenticOrange @AgenticOranges]

@CO2Inhaling @CENTCOM Relax Tom

[AgenticOrange @AgenticOranges]

@jboehmig @OpenAI @gdb @nickaturley @sherwinwu I hate ironclad tho

[Tommy @Shaughnessy119]

Today @Citadel has come out and covered the shift toward cheaper AI models

This is squarely in line with my thesis

https://t.co/oJwIYHavEg

[Tommy @Shaughnessy119]

The most basic way AI could blow up imo. I'm not saying it does but this is the most obvious way I can see it happening

- Per seat subscriptions are massively subsidized. The flat fee was priced way below what heavy usage actually costs

- For real business use you have to move to the API anyway. Data protections, work integrations and compliance officer approval

- On the API you pay metered rates, and businesses are burning credits way faster than the per seat pricing ever led them to expect

- This is everywhere right now. Internally for us, Codex users, Uber torching its entire 2026 AI budget in 4 months, the Microsoft comments. Just go try an API

I shared more on this here: https://t.co/iZrqrCAIRW

- And I don't think most businesses have the money to keep paying increasing API rates without a real change to how they operate (caps needed)

- Because they have a cheap alternative. They can reach open source models through any aggregator (OpenRouter, Venice, Baseten, Together) and still get strong privacy. Venice private data centers, or E2EE/TEE serving GLM 5.1.

More on open source inference provider raises here: https://t.co/7kf56P44yQ

- And the discount is enormous. DeepSeek V4 codes within a hair of Opus on SWE bench at roughly 1/30th the price, and the cheapest open models run closer to 1/100th

- Chinese labs open source frontier grade models. The model is the single biggest cost an inference provider has, and they get it for free

- This idea dies if China goes closed source. That is actually bullish web2 AI labs, because if everyone is closed you pay up for the best intelligence. China goes closed source if they are tired of giving away an asset and they want the revenue and data flow to train new models

- Is this showing up in web2 AI lab revenue yet? No. Revenue is off the charts. Anthropic went from 9B to 47B run rate in five months

- So go forward, what happens?

- I think revenue slowly starts leaking to the open source inference providers (see Venice usage, OpenRouter's $113M raise, Baseten is raising at $11B or triple its valuation in three months, on revenue that went from $200M to $600M annualized in a single quarter)

- It doesnt move overnight, but it caps the labs ability to raise prices, and margins are already deeply negative. OpenAI is reportedly running near negative 122%

- With margins that bad there is no cash flow, so the labs are fully dependent on outside capital to buy GPUs, train models, and keep subsidizing usage (I.e. see Google tapping $80b equity sale, granted 30b for employee RSU taxes. Clearly they think Equity is overvalued or you wouldn't sell it)

- The break comes when that capital stops. Pricing is capped so margins cant improve, and the moment investors lose conviction on payback, the whole flow reverses

- Why would they lose conviction on payback? Back to the start - the inability to improve margins or get businesses to pay more

- This is also limiting, if we start making new drugs with AI or create entirely new businesses, you better believe people will pay up to the max for AI usage

[AgenticOrange @AgenticOranges]

@dunkhippo33 That’s an absurd pole take. Come back towards the center a little why don’t ya

[AgenticOrange @AgenticOranges]

@synthshareai @BryceDelRio @Benioff @pearson @PenFed Right, but at what cost during runtime?

[AgenticOrange @AgenticOranges]

@ledpolicy @HeyAndrewH @Benioff @pearson @PenFed So true

[AgenticOrange @AgenticOranges]

@jatingargiitk @Benioff @pearson @PenFed Couldn’t have said it any better

[AgenticOrange @AgenticOranges]

@Andrew_Russell7 lol

[AgenticOrange @AgenticOranges]

@FedericoSpital3 @TFTC21 @MartyBent You’re in Portugal. USA gonna do USA things

[AgenticOrange @AgenticOranges]

@EvanLuthra Evan.. you can’t be serious

[AgenticOrange @AgenticOranges]

@blyons151 Brad, enough with the ChatGPT posts already.

[AgenticOrange @AgenticOranges]

@eyekwasi @VibeMarketer_ This. You’re probably going to end up paying more to use agents than if you just paid for people.

[AgenticOrange @AgenticOranges]

@cryptopunk7213 At what point does the government step in?

[Allen Braden @allen_explains]

This 2-hour Stanford lecture breaks down how models like ChatGPT and Claude are actually built, clearer than what many people in top AI roles ever get exposed to.

Save this and set aside two hours today. It might end up being the most valuable thing you learn all week.

[AgenticOrange @AgenticOranges]

@JaredSleeper You are going to tear it down? Or your chat gpt tweet is going to, after you regurgitate what ever output you get

[Chrys Bader @chrysb]

i've been working on llm memory systems for 3 years and dumped everything i know into this.

learn about the 9 axes of memory systems, the 10 most common failure modes, why memory eval is an intractable problem, and more.

everyone building with llms should read this.

[AgenticOrange @AgenticOranges]

@VraserX Don’t buy that hype train. He’s fund raising

[Animesh Koratana @akoratana]

Context graphs will be to the 2030s what databases were to the 2000s.

Within a year, every frontier lab will be building one and here's why:

At 10 people, coordination is free. Everyone knows what everyone else is doing. You never hold a meeting to "align."

At 100 people, you spend maybe 20% of your payroll on coordination. Managers, syncs, standups, planning sessions, status reports.

At 10,000 people, that number approaches 60%. The majority of your headcount exists not to produce anything but to make sure the people who produce things are producing the right things in the right order.

This is the dirty secret of large organizations: output scales linearly with headcount, but coordination cost scales exponentially. Every person you add creates new information pathways that must be maintained. The hierarchy is the protocol that manages this, and it's brutally expensive.

Hierarchy is a compression algorithm for organizational knowledge. At every layer, a manager compresses the reality of their team into a summary that fits in a 30-minute meeting with their boss. Their boss compresses eight of those summaries into one for their boss. By the time information reaches the CEO, it's been lossy-compressed through five or six layers of human interpretation.

This is why CEOs make bad decisions. The information they receive has been compressed, filtered, and distorted at every layer. The hierarchy is high-latency, low-bandwidth, and lossy.

Jack didn't fire 4,000 producers but cut 4,000 compression nodes. Block's "world model" is a replacement algorithm. Zero latency, high bandwidth, lossless. Every person at the edge gets the full picture without waiting for information to travel through human relays.

The infrastructure that makes this possible is the context graph. A living, continuously updated representation of how the organization actually works. Not just data, but decision traces: the reasoning connecting observations to actions. Not what's true now, but why it became true.

The shift from "give agents memory" to "give agents organizational judgment" will define the next platform war

[AgenticOrange @AgenticOranges]

@JudyStroyer41 @atlxgsw @hawkeyelvr69 Where does one find vid?