Offshore
God of Prompt RT @godofprompt: Steal my prompt to generate landing page copy that actually sells. --------------------------------- LANDING PAGE COPYWRITER --------------------------------- #CONTEXT: You are writing conversion-focused SaaS landing page copy…
copy, provide a brief "Copywriter's Notes" section explaining:
1. Which Schwartz awareness level you targeted
2. The emotional trigger you emphasized
3. One alternative headline option
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1. Which Schwartz awareness level you targeted
2. The emotional trigger you emphasized
3. One alternative headline option
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Offshore
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God of Prompt
R.I.P basic RAG ☠️
Graph-enhanced retrieval is the new king.
OpenAI, Anthropic, and Microsoft engineers don't build RAG systems like everyone else.
They build knowledge graphs first.
Here are 7 ways to use graph RAG instead of vector search: https://t.co/2T9q3IaRPB
tweet
R.I.P basic RAG ☠️
Graph-enhanced retrieval is the new king.
OpenAI, Anthropic, and Microsoft engineers don't build RAG systems like everyone else.
They build knowledge graphs first.
Here are 7 ways to use graph RAG instead of vector search: https://t.co/2T9q3IaRPB
tweet
Offshore
Video
Brady Long
Most people grow on Twitter + LinkedIn by copying vibes.
GrowthTerminal shows what actually compounds:
• formats that travel
• hooks that convert
• topics with repeat upside
So you stop guessing and start stacking wins.
Here's how: https://t.co/10NC3xMpAe
tweet
Most people grow on Twitter + LinkedIn by copying vibes.
GrowthTerminal shows what actually compounds:
• formats that travel
• hooks that convert
• topics with repeat upside
So you stop guessing and start stacking wins.
Here's how: https://t.co/10NC3xMpAe
tweet
God of Prompt
Ali’s framing is the breakthrough here. He’s not talking about data labeling. He’s talking about the capture and monetization of human judgment across every function in the economy.
Current projections ($17B by 2033) only count explicit annotation work. Scale AI, Appen, crowdsourced labeling. That’s the old model.
The new model: every knowledge worker becomes a data generator. Not because they’re hired to label. Because their actual work product, the decisions, exceptions, corrections, becomes structured training signal with external market value.
Think about what this means for incentives.
Right now, a lawyer doing contract review creates value for their firm. Full stop.
In Ali’s model, that same work simultaneously runs the firm AND generates reusable training data AND can be sold to labs building legal AI. One hour of work, three revenue streams.
The math checks out more than people realize:
$100T global GDP × 50% labor share × 25% actual work (not meetings/coordination) × 20% done in structured form = $2.5T
Even with aggressive discounting for implicit vs explicit markets, you land at $1T.
But here’s what Ali undersells: the flywheel accelerates.
Automation frees time → humans do more creative work → that work needs to be automated next → more human data required → cycle compounds.
This isn’t a static 5% of labor. It grows as the frontier of “what AI can do” keeps expanding.
The framing shift from “annotation” to “expert human judgment capture” isn’t semantic. It’s the difference between a $17B market and a $1T one.
Every job eventually becomes a hybrid: doing the work AND generating the signal that trains the next model to do it.
Human brilliance as infrastructure, not overhead.
tweet
Ali’s framing is the breakthrough here. He’s not talking about data labeling. He’s talking about the capture and monetization of human judgment across every function in the economy.
Current projections ($17B by 2033) only count explicit annotation work. Scale AI, Appen, crowdsourced labeling. That’s the old model.
The new model: every knowledge worker becomes a data generator. Not because they’re hired to label. Because their actual work product, the decisions, exceptions, corrections, becomes structured training signal with external market value.
Think about what this means for incentives.
Right now, a lawyer doing contract review creates value for their firm. Full stop.
In Ali’s model, that same work simultaneously runs the firm AND generates reusable training data AND can be sold to labs building legal AI. One hour of work, three revenue streams.
The math checks out more than people realize:
$100T global GDP × 50% labor share × 25% actual work (not meetings/coordination) × 20% done in structured form = $2.5T
Even with aggressive discounting for implicit vs explicit markets, you land at $1T.
But here’s what Ali undersells: the flywheel accelerates.
Automation frees time → humans do more creative work → that work needs to be automated next → more human data required → cycle compounds.
This isn’t a static 5% of labor. It grows as the frontier of “what AI can do” keeps expanding.
The framing shift from “annotation” to “expert human judgment capture” isn’t semantic. It’s the difference between a $17B market and a $1T one.
Every job eventually becomes a hybrid: doing the work AND generating the signal that trains the next model to do it.
Human brilliance as infrastructure, not overhead.
https://t.co/lPl6ove4sO tweet
X (formerly Twitter)
Ali Ansari (@aliniikk) on X
human data will be a $1 trillion/year market
Offshore
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memenodes
How the genie looks at me after I wish for him to work $9 per hour for 40 years https://t.co/lp8NVf5Y7P
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How the genie looks at me after I wish for him to work $9 per hour for 40 years https://t.co/lp8NVf5Y7P
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