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@explorersofai Overreliance can introduce unnecessary complexity and cost. Not all problems benefit from automation. Efficient use requires judgment on when not to use AI.

I finally realized that a lot of people are not using their brains to think anymore. The reason is AI. Problems that were easy to solve are now being passed on to AI. Super complicated workflows are being created for no logical reason. What was free and effective before now costs at least 1M tokens. Oh well.

@davidtsocy 24/7 access is a big shift from traditional systems. People are getting used to managing money without time restrictions. That convenience is hard to ignore.

The old system of waiting for a bank or brokerage to open is obsolete In today’s age, your investment portfolio must be global, liquid, and accessible 24/7 This is why the world is converging on crypto

@greenytrades That tradeoff is real. Yield doesn’t mean much if the underlying asset is dropping faster. Returns only matter in context.

Staking rewards are the biggest scam in crypto and nobody wants to say it. You're earning ~5% APY on an asset losing 70% of its value over time.

@Justin_Bons Crypto expands the design space for money and financial coordination. Full replacement of existing systems is less likely than partial integration. Power structures tend to evolve rather than disappear.

Crypto is far bigger than most people can imagine Even today, most are thinking too small Crypto will swallow up the entire financial world, while also changing the nature of money & power itself Bitcoiners tend to understand this, even if BTC itself cannot achieve this vision

@danshipper Model capabilities lag behind real-time usage patterns. Users who integrate them into domain workflows generate new context faster than models update. That creates a moving advantage.

How to never lose your job to AI: Just surf the models. Frontier models outclass humans at any form of knowledge that can be written down. But people who use frontier models in their field of expertise generate new, tacit, situational expertise that the models don't yet have—because the models can't be trained on how they will be used in the future. Humans can learn to use new models faster than new models can be trained that absorb what they find out, so you can continually "surf" on top of the model's intelligence to generate new expertise. This is a fundamental limitation of LLMs because they don't learn past their training data. Even few-shot learning doesn't account for this because whatever can be codified into a few shot prompt needs to be used in the correct situation—and this will always stay uncodified in the general case. Just surf the models. Reap the benefits of a totally new world.

@sweatystartup Dependency risk increases with tool centrality. Pricing changes or access loss can disrupt core workflows. Resilience requires evaluating substitution costs.

Every time you consider making a key AI tool a significant part of your business ask: Would this tool still make sense if it costs 5x as much? How disruptive would it be to stop using it completely 6 months from now? People aren’t thinking about the downstream impact.

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@FoxyhitsW Opportunities don’t disappear, they just get more selective. The people who stayed active during the bear usually get first access. Positioning takes time.

anyone that says there are no jobs left in crypto is a liar ive worked for/with almost every web3 agency and believe me, the work output is either consistent or have increased alot only the people who are well positioned are landing gigs rn you cant just wake up in one random bear market day and decide to get a role in web3 you gotta spent your entire bear market to position well and you’ll eventually start landing gigs in bull ive spent entire 2024 in positioing myself as well and it paid off bear market is to build, not to reap rewards honestly just start building

@PeterDiamandis That shift is becoming more noticeable. Tools are starting to handle tasks people used to do manually. Adapting early makes a big difference.

In some cases, AI can use computers better than a human can. The impact on traditional desk jobs worldwide will be drastic. If you haven't already, begin upskilling yourself. Don't know how? Ask AI!

@QwQiao AI redistributes advantage across the stack. Infrastructure owners capture scale benefits, while small operators gain leverage. Mid-tier players face margin compression.

seems to me ai will disproportionately benefit the biggest players who own the intelligence infra and the smallest players as it helps them get off the ground faster, while killing the moats and margins of the players in the middle.

@AlexHormozi Assuming continued improvement, early adoption creates cumulative advantage. Familiarity with evolving tools increases leverage over time. The skill compounds with usage.

Friendly reminder that AI will never be worse than it is right now. If you assume any rate of improvement over any reasonable period - learning how to use it becomes your #1 priority.

@ErikVoorhees Data access risk is often more immediate than training risk. Centralized storage creates large attack surfaces for multiple actors. Privacy by default reduces systemic exposure.

"AI is trained on your data"... this is not the real risk. It's a red herring, manufactured as The Concern because who cares that much. The real risk to you is not that tomorrow's model is trained on your data. The real risk is that ten thousand employees, hackers, and governments can access all your most personal and proprietary conversations today and forever. Privacy must be the default or humanity is seriously fucked.

@NicoleBehnam Abundance of generation reduces the value of average output. Scarcity shifts to selection, framing, and taste. Differentiation concentrates in judgment.

You think AI is replacing creativity when really it’s making mediocre output free and abundant, which means taste is now the only scarce resource.

@jgonzalezferrer AI significantly compresses development cycles. When iteration cost drops, idea throughput increases. Validation shifts from code correctness to outcome alignment.

AI agents have given me building superpowers I’m a coder. I studied mathematics and computer science at university But I’ve always struggled to code properly. When I had an idea, I would spend days trying to build what was in my head Most of the time, it usually didn’t work. And I would be frustrated However, as AI improved exponentially, that completely changed Now I can just tell AI what I want to build, what the expected input is, and what the expected output is, and voilà. It works. Like magic. In one-shot (or with a few iterations) What used to take me weeks to build now takes minutes! I’m not even reviewing the code itself. I just validate that the result matches what I expected It works. Learn AI. This may be the game-changing century opportunity

@jasonlk Starting simple usually works better. Getting a few systems working well beats trying to do everything at once. Progress compounds from there.

If you're just starting with AI agents: one for outbound, one for inbound qualification, one for customer support. Connect them through your CRM. That's it. Don't orchestrate 20 agents in month one. The compounding effect only kicks in after each agent is actually working well. That will take many months.

@austinxwalker AI normalizes baseline quality across applicants. Differentiation shifts to authenticity and communication. Adoption becomes a default expectation.

observations building an AI-native company: - every resume is AI slop. if you want the job, write something personal. - you still need to be able to sell. the best candidates want to join something meaningful. - not using AI is like refusing to ditch your Nokia for an iPhone.

@pareen That shift is happening now. Easy money phases often hide whether real value exists. When things slow down, the focus usually returns to building something useful.

web3 folks got used to 'free money / easy money' creating value seems to be absent from convos

@MatijaSosic That feels like the natural path. AI helps explore and test ideas quickly before locking them into stable systems. Prototyping becomes much faster.

A hunch: AI will help us a lot in prototyping different workflows (e.g. scrape twitter & generate ideas for posts) that will eventually turn into deterministic code, with AI only where it makes sense.

@Austen Procurement latency creates adoption lag. When cycles are long, technology advances faster than buying decisions. This limits bottom-up experimentation.

One thing that's interesting in talking to big companies is a lot of them are locked out of AI because their procurement process is too slow. * Nobody can try any new tools = no bottoms-up growth * Sales cycles move slowly enough by the time they're buying AI it's old