If you come to my event I guarantee I'll save you enough on claude tokens to pay for your beer.


LinkedIn Content Strategy & Writing Style
AI Tech Consulting at Every
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Mike Taylor positions himself as a high-leverage AI practitioner who bridges the gap between technical execution and strategic resource management. His content strategy centers on the "quantization" of work, treating token budgets and AI agents with the same rigor he once applied to large-scale digital ad spend. He is notable for his radical transparency and experimental rigor, often stress-testing new models through personal benchmarks like cloning interviewees or automating entire book drafts to find the "juice" in the context. By blending AI tech consulting with a pragmatic operator's mindset, Mike offers a unique value proposition: he doesn't just report on AI trends, he builds the workflows that prove their economic and creative utility.
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If you come to my event I guarantee I'll save you enough on claude tokens to pay for your beer.

This is why I'm always so busy – AI increases the opportunity cost of not working and so no matter how much you automate, you end up busier than ever!
We gave this a boring name on purpose so we only get people genuine interested in agents in production.

I’m happy to share that I’m starting a new position as Head of Technology Consulting at Every Inc.!
This is a thoughtful essay on how AI impacts jobs. The key point is that models make older expert work easy and lead to a flood of sameness. To differentiate, we'd have to rely on human expertise fo…
1.5 posts/week
Posts / Week
18
Total Posts Analyzed
LOW
Posting Frequency
460.7%
Avg Engagement Rate
STABLE
Performance Trend
280
Avg Length (Words)
HIGH
Depth Level
ADVANCED
Expertise Level
0.85/10
Uniqueness Score
NO
Question Usage
0.15%
Response Rate
Writing style breakdown
<start of post>
I’ve spent the last 48 hours trying to break the new Claude model, and I think I finally found its ceiling.
Most people are using it for basic coding tasks, but I wanted to see if it could handle high-stakes strategic planning. I fed it three years of raw financial data and asked it to find the "hidden leaks" in our customer acquisition cost.
It didn't just find the leaks; it built a custom attribution model that accounted for seasonal decay. I actually enjoy reading the logic it uses to justify its decisions. It feels less like a calculator and more like a senior analyst who’s had too much coffee.
I also tested it on my "impossible" benchmark: rewriting a 2,000-word technical essay into the style of a 1920s noir novelist without losing the technical accuracy. It nailed it on the first prompt.
The hard part now isn't the tool—it's the imagination of the user. If you're just asking it to summarize emails, you're leaving 90% of the value on the table.
I put together a full breakdown of the prompts I used and the raw outputs over on the Every site. Give these a try on your most ambitious project this week:
<end of post>
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