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Research and briefings on AI business, agents, operating models, intelligence economics, creator monetisation and AI opportunities hiding in plain sight.
Global Adoption
Joined February 2019
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Para lo que quieras montar, es importante pensar como estarán las cosas en 5 años.
Yo creo que en 5 años la IA podrá hacer todo el trabajo operativo que hace un humano en un ordenador. No habrá gente haciendo clicks.
En este sentido creo que los SaaS no se verán como hoy, serán interfaces para revisar lo que hacen los agentes. Pero lo agentes no necesitarán botones.
Entonces, cuando pienso en que construir en un entorno donde la IA podrá hacer todo ese trabajo operativo para mi hay 3 opciones:
1. Comprar un negocio físico, automatizar con IA y crecer.
2. Ser muy bueno implementando IA en negocios existentes
3. Montar un SaaS vertical para montar la versión nativa que necesitan estos negocios
Opción 1 es la más directa y de menos riesgo (siempre que compres bien). Pero lo difícil es comprar bien. Aquí @AdriaMrqs nos puede contar más. También el que tiene el trabajo más tedioso y a largo plazo. Te toca aceptar estar en una fábrica/almacén/etc y lidiar con problemas humanos en esta transición.
Opción 2 es la que tiene más sentido para perfiles de negocio que han estado expuestos a tecnología los últimos 5-10 años. Lo difícil aquí es gestionar el ego y aceptar no montar un producto "chulo" para ser una máquina de ejecutar y mostrar impacto a empresas que necesitan esta transformación.
Opción 3 para mi es el resultado de hacer la opción 2. Si tu haces 20 implementaciones en una vertical, encuentras pains comunes. Si eres capaz de productizar, implementar en los próximos 50 clientes será mucho más fácil i escalable.
Es muy fácil querer saltarse opción 2 y montar 3 directamente, pero creo que es clave hacer la 2 bien hecha.
El error es querer hacer cosas que los frontier labs acabarán haciendo. El acierto es estar pendiente de lo que pueden hacer y saber apalancarte en ello mejor que nadie.
I built a 400K+ audience here, but I'd do it differently if I had to start again.
Today, you can build an audience on any of the usual suspects: Instagram, X, YouTube, LinkedIn, and TikTok.
But these platforms are quite saturated now. Everyone is there!
Fortunately, there's a whole new creator economy surging around writing stories.
These guys built an audio fiction platform where 300K+ people published their first story in the past year.
They say that at least 5 creators have earned $1M+, and the top 1% earn around $58,000 per year.
This is definitely becoming another good option.
Introducing Sherpa: the most advanced fiction writing AI
We accelerated from $250M in ARR to $500M because Sherpa helped increase content production by 1200% in 1 year
Sherpa was trained on 5.5B hours of playtime with minute by minute dynamic retention data.
550K+ creators have produced 2.6M hours of content annualised using it
Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios.
10% of eligible writers on Pocket FM make >$200K
One blockbuster produced >$100M in revenue
3 writers have become millionaires in <2 yrs
We built Sherpa to enable anyone to make >$1M by writing world-class fiction stories:
1. The Idea: Drop a 1-2 sentence concept. Sherpa interrogates it like a veteran editor on tension, stakes, and psychology
2. World & Characters: It builds out the complete lore, tone, and character psychologies
3. Sub-Plot planning: Breaks the premise into arcs, arcs into episodes, and episodes into scenes
4. Scene-by-Scene Generation: Outlines and drafts entire episodes, with you able to steer, rewrite, or override anytime
5. Editorial Review: Stress-tests every draft for pacing, engagement drop-offs, prose, and coherence before it locks
6. One-Tap Production: Pick a voice, convert to audio drama, and publish directly to Pocket FM’s millions of listeners
7. Global Scale & Monetization: Revenue-share on performance, with automatic localization so you earn across international markets
Test Sherpa for free here: pocketfm.com/sherpa
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Generic LLMs fail at serialized fiction because they lack a long-horizon narrative reward function.
Sherpa solves this through three core technical leaps:
1. Narrative World Model (State Tracking & Retrieval): Context windows degrade over long runs. Sherpa constructs an evolving semantic knowledge graph tracking character states, secrets, and plot dependencies. High-speed retrieval surfaces exact context on demand, maintaining zero continuity decay across hundreds of episodes
2. Hierarchical Story Planner: When writing a 500-episode story like Naruto, you need to plan 100s of sub plots. Rather than generating linearly, Sherpa decomposes narrative across discrete levels: season -> arc -> sequence -> episode -> scene.
Rather than generating everything upfront, like a generic LLM, Sherpa uses progressive planning and dynamic replanning. As the story evolves, it identifies what changed, traces the downstream impact, and replans only the affected parts.
3. Prose Engine (Trained on series' retention data): LLMs write robotically, but serial fiction needs emotion, tension, pacing, and dialogue that sounds like real people.
Sherpa's Prose Engine was designed specifically for storytelling. It was built on 1B+ tokens of Pocket's own stories, trained by learning from what listeners engage with, where they drop off, and what keeps them hooked.
Feedback is taken from specialized evaluator models that measure every scene against a 40-item checklist. (Evaluator models were benchmarked against human reviewers and matched them 80–90% of the time.)
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Owning distribution and creation puts us in a very unique spot.
More shows -> More data -> Sherpa becomes better -> more creator success -> more creators -> more shows
Pocket FM has already seen one $100M IP. I believe Sherpa will soon lead to dozens of single-person studios creating billion-dollar shows.
Most people are scared of AI but I think it'll unlock more human creativity, help creators earn more, and bring the next great IPs to life. This will create millions of jobs and new income streams.
AIadaptive retweeted
Today we’re releasing Helix 2.5
We rented 30 homes in the Bay Area. The robots arrived with no additional training and started doing useful work
AIadaptive retweeted
As per usual, be careful not to be pulled in by the self-anthropomorphism, which was implied by the prompt.
This fit into my Claude usage limit, but the entire analysis of the manuscript (including many agents) & movie would have cost about $85 in tokens on Fable 5.1 otherwise
AIadaptive retweeted
absolutely incredible read
everyone should be a knowledge engineer
your shared context as a company is your biggest moat
AIadaptive retweeted
what is Jev and how to use it, a deep dive ⤵︎
flaviocopes.com/jev/
AIadaptive retweeted
AI video creation is getting more powerful, but only a few have figured it out.
Thats why I built Videoclaw.
I want it to be so easy, you don't need to figure it out. Just prompt, and watch it edit, generate and create video :)
Today we launch.
The video was made entirely with Videoclaw, blending human and generated footage. To show how truly easy it is, prompts and proofs for each project are in the thread.
Try it for free.
Oh, and there’s one more thing 👇
AIadaptive retweeted
I crunched 16,600 X posts and 514 podcasts to write The State of AI Report.
It covers the 10 trends shaping AI adoption going into 2027:
01 Horizontal Agents - Grok Bot and the merging of the model, harness, and application layers
02 Knowledge Work Factories - building loops so your agents can achieve your goals while you sleep
03 The Verification Gap - turning taste into a test
04 Open Models - cost, specialization, and privacy fuel demand
05 The Context Layer - your company's playbooks and expertise become infrastructure
06 Generative Media - advancements in voice, video, and image realism
07 AI-Native Services - sell the outcome not the output, build agents to scale
08 Vibes Shift - from Dario fueled replacement fear to building the factory
09 Bubble Talk - what funding and capex mean for vendor selection
10 Deployment - the rise of forward deployed engineering
AI didn't take summer vaca All layers of the stack improved. And so did best practices for AI adoption inside companies.
Read this practical report that separates AI signal from noise, shows where AI creates value today, and helps prioritize investments for 2027:
atherial.ai/state-of-ai
AIadaptive retweeted
we almost never test new foundation models but we've been testing this for ~a week @every and it's pretty wild.
the kind of things that will be obviously indispensible in 6-12 months
it doesn't produce words as output, it produces probabilities. so it can efficiently act as a judge in cases where you'd need a Fable-level model—but in our testing was 25x faster and 600x lower priced
excellent vibe check by @hammer_mt on @every:
every.to/also-true-for-human…
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
AIadaptive retweeted
GPT-6 Astra is by far the best inside Codex.
& this is THE Complete Beginners Guide to Codex.
The 28 things you need to know about Codex and GPT-6 to start using it for knowledge work and coding work.
My goal with this video: give you one guide you can send to anyone on your team so they can get comfortable with Codex, understand what’s possible, and start finding ways to use AI in your business.
00:00 Intro
01:15 The Codex Platform
02:02 Downloading Codex (ChatGPT Desktop)
03:25 1. Codex Is the App, GPT-6 Is the Model
06:10 2. Build Any App
10:32 3. Host Sites with GPT Sites
18:12 4A. Building Desktop App
21:33 4B. Building an iOS app
24:09 5. Built-In Web Browser
32:27 6. Create Any Document
35:00 7. Annotation Mode
38:41 8. Production-Ready Apps
44:29 9. Chats & Projects
48:54 10. Multitasking
50:09 10B Side Chat
51:13 11. Sections
54:27 12. Connect Every App (Plugins)
01:03:17 13. Ask Codex What to Work On
01:05:39 14. Blender & 3D Assets
01:08:25 15. Video Games
01:09:24 16. Interactive Learning Websites
01:13:12 17. Scheduled Tasks
01:16:19 18. Email & iMessage
01:18:56 19. Skills
01:22:54 20. Record and Replay
01:26:45 21. External APIs in Skills
01:30:00 22. Video Understanding Skill
01:35:51 23. Inline Charts
01:38:03 24. Control Codex from Your Phone
01:42:49 25. Generate & Edit Images
01:45:17 26. Sub-Agents
01:48:09 27. Computer Use
01:49:35 28. Whatever You Want
AIadaptive retweeted
Talked to a CMO of a $70M company about AI adoption.
They have two great things going for them:
• Vision for a truly AI-native product marketing function
• Buncha great Skill files that the team is using
But here are their problems:
• Manually babysitting every step of their AI workflows
• No bandwidth for high ROI work like sales enablement that AI can handle
• No in-house AI engineering team to get AI handling entire work streams reliably
This is a common problem state now. Everyone is deploying AI. Few are taking full advantage of what AI is capable of today.
Trillions of dollars are being spent on models and harnesses. They can do more than enrich leads and write emails.
As a result of the adoption gap, AI investments fail to show measurable returns. They get some time savings and quality improvements, but not much impact on revenue growth or margin expansion.
Companies try to bridge the gap by building in-house. Either by context switching an engineer from product to internal tooling, or asking a non-technical person to figure out engineering.
I'm all for vibe coding, but you need more than that to get AI handling larger and more complex work streams, reliably and at scale.
You have to remove humans as the bottleneck for AI knowing what to do next. You have to close the context loop so AI can cook for you before anyone opens their laptop in the morning.
AIadaptive retweeted
OPENAI 🔥: As tradition dictates, OpenAI plans to announce a new solution for its agent ecosystem at the upcoming DevDay 2026.
This time, we will get Managed Agents 🤖
> Users will be able to create Agents, Environments, and Agent Sessions directly via the OpenAI Platform.
> The Codex Plugin will help users build, configure, and deploy agents, lowering the barrier to entry.
> OpenAI's Managed Agents could be hosted on the OpenAI platform or self-hosted.
Loads of details inside 👀
AIadaptive retweeted
You can now share your ChatGPT Site without sharing it with the world.
Invite specific people to view your site while keeping it private from everyone else. Business and Enterprise teams can also invite guests from outside their workspace, making it easy to share dashboards, project hubs, and anything else they build with clients and partners.
Available on Plus, Pro, Business, and Enterprise.
AIadaptive retweeted
Claude is highly capable at writing code, but until now it had nowhere to put it.
You get a script, not a product.
Zite just solved the deployment gap.
They released an integration for Claude, ChatGPT, and Cursor that turns generated code directly into usable business apps.
What Zite handles everything automatically:
→ live databases
→ logins and roles
→ user notifications
→ hosting
Best part?
Their pricing model!
It uses zero Zite credits.
You just run it on the LLM subscription you already pay for 👀
🤝 Paid partnership