Casey Blue

guy with dog, AI realist, operator, building things

[Casey Blue @Caseyblue_]

@coreyganim if anyone has actually been on a client call trying to sell AI services they would know that this won't work

[Casey Blue @Caseyblue_]

@user41425 Depends what you like. Toronto is inferior in every sense besides food + social culture. So unless you want to be a fat extrovert Vancouver is better

[Bram Sugarman @bram]

This is everything I need to know about Canada’s national AI strategy.

[Casey Blue @Caseyblue_]

@jgreze @TownAI @arampell @a16z @kirstengreen @ForerunnerVC @firstround @altcap @conviction I'm sure it's useful as a consumer product and a nice user experience as a personal AI. But this is straightforward to build for companies and the trend is wanting this kind of thing tied to specific context for specific employees and to an overall context layer.

[Casey Blue @Caseyblue_]

@HaiderMakess give me 3 examples of people doing this

[Casey Blue @Caseyblue_]

@tokifyi Very obviously B. The most variety and comfort by far

[Casey Blue @Caseyblue_]

@realMikeChong @tokifyi ^ skill issue and market understanding issue. From someone who has sold and successfully completed multiple AI consulting projects

[Casey Blue @Caseyblue_]

@gregisenberg The big fallacy of the agentic era is that 10 people have the exact same workflow every single day.

[David @DavidSHolz]

someone should open up a late night bar in San Francisco for AI employees ironically named "Permanent Underclass" we can have drink names like "Hopium" and "Copium" and "Brandy is All You Need"

[Casey Blue @Caseyblue_]

@AlisonbobEth @tokifyi There are agents that do that kind of thing - Akai by Deel, another one I saw recently that was a YC company. But every business is different, there is sometimes low hanging fruit sure but nothing sticks unless the operator deeply understands the problem

[Casey Blue @Caseyblue_]

@AlisonbobEth @tokifyi Agreed, it turns into general ops consulting quickly. But willingness to pay is low, they’re the hardest to convince and tend to be the customers that keep squeezing for more time

[Casey Blue @Caseyblue_]

@gakonst @paradigm @tempo can this replace @viktor__com

[Casey Blue @Caseyblue_]

Are Ay (A) and Aye (I) the most annoying and distinct sounding letters in the english language? I feel like I can hear people saying those syllables in any noisy or open space from uncannily far

[Casey Blue @Caseyblue_]

@balajis I think out of 1000 people that watch a movie maybe 2 or 3 would care to do that. Not a good use case - for superfans maybe there is some reason but people want a story told to them. Why havent choose your own adventure interactive narratives worked?

[Casey Blue @Caseyblue_]

just impeccable writing and so on the nose

[Will Manidis @WillManidis]

https://t.co/aHOHtb1rcW

[Will Manidis @WillManidis]

https://t.co/aHOHtb1rcW

[Casey Blue @Caseyblue_]

@TrungTPhan The Anthropic/Wemby parallels are staggering

[Rohan Paul @rohanpaul_ai]

Harvard Business Review research reveals that excessive interaction with AI is causing a specific type of mental exhaustion ( or "AI brain fry"), which is particularly hitting high performers who use AI to push past their normal limits.

A survey of 1,500 workers reveals that AI is intensifying workloads rather than reducing them, leading to a new form of mental fog.

While AI is generally supposed to lighten the load, it often forces users into constant task-switching and intense oversight that actually clutters the mind.

This mental static happens because you aren't just doing your job anymore; you are managing multiple digital agents and double-checking their work, which creates a massive cognitive burden.

The study found that 14% of full-time workers already feel this fog, with the highest impact seen in technical fields like software development, IT, and finance.

High oversight is the biggest culprit, as supervising multiple AI outputs leads to a 12% increase in mental fatigue and a 33% jump in decision fatigue.

This isn't just a personal health issue; it directly impacts companies because exhausted employees are 10% more likely to quit.

For massive firms worth many B, this decision paralysis can lead to millions of dollars in lost value due to poor choices or total inaction.

Essentially, we are working harder to manage our tools than we are to solve the actual problems they were meant to fix.

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hbr .org/2026/03/when-using-ai-leads-to-brain-fry