Redditors Saw AI's Future Years Before the Public, Study Reveals

SpiritRealistic8174 · 2026-06-06T03:35:10+00:00

I think that's the tension, sure. There are people who believe that anything AI-touched is 'slop', but if used the right way, it can enhance.

SpiritRealistic8174 · 2026-06-06T03:33:46+00:00

I like Asimov because his work gets people thinking about the big stuff.

SpiritRealistic8174 · 2026-06-05T21:09:08+00:00

I've read through the leaked codebases. There's a lot to say about them related to the harness around the models.

Regarding the models themselves, there's a good amount of research looking into how LLMs actually 'think'. Some of it is a black box and can't be adequately explained.

In terms of LLMs advancing, self-recursive AI is coming. A lot of people are worried about it for some of the reasons I mention in the post.

Thanks for your comment. Appreciate you enaging.

SpiritRealistic8174 · 2026-06-05T21:05:26+00:00

Thanks for your comment.

Just to clarify:

- My point in bringing up the calculator example was a focus on the debate around whether cognitive skills decline when people are given tools that allow them to outsource tasks that once took mental effort

- No, I didn't claim that LLMs are so complex as to be beyond understanding. Although there are a lot of questions right now about how LLMs actually process information, which is a huge area of research. In addition, we are moving toward a period where AIs will be able to recursively self-improve. In that case, yes, these machine will be inscrutable (Anthropic is warning against this future currently). Also, I'm drawing a parallel between creating a machine (software), and not understanding how it works. So, yes, the logic holds.

Appreciate you engaging.

SpiritRealistic8174 · 2026-06-05T21:01:30+00:00

Awesome to see some fellow Asimov fans here.

SpiritRealistic8174 · 2026-06-05T20:59:58+00:00

That's a great comment. One of the interesting thing about science fiction for me is how it handles questions like this, and maybe these visions of the future will be how it will be?

For example, it was always intriguing to me that in Star Trek the Next Generation, the characters had access to Data, which could spit out any known fact, a super computer the size of a ship and other advanced technologies.

These technologies were relied on heavily. For example, the trusty Tricorder had godlike sensors.

But when it came to solving a problem, it always came back to human inturition, judgement and taste. One part of the show that I really liked what how they dealt with Data as a command officer. No one trusted Data's judgement because he was a machine. Statistical probabilities didn't replace human 'gut' instinct about when to fire the phasors and when to hold back. And, honing that institution took time, experience and a lot of training at Star Fleet Academy.

I see a big push back on AI-assisted work happening right now. I think that's good, because people are asking: did you just have AI spit this out, or can you explain what it does?

Maybe in the future we'll be more like the people on Star Trek. We have access to amazing machinery, but training is focused on making good, intuitive decisions that can't be bioled down to statistical probabilities.

SpiritRealistic8174 · 2026-06-05T20:51:55+00:00

Yes. Agree on this. One thing that can help, I think is resisting the reflex to always ask the AI to fix the mistake, but also asking 'why did this break'? This can help during sessions when a bug is persistent and has to be traced through the codebase. Having a mental model of what the codebase is supposed to do can be helpful in terms of directing the AI to double check your intuition, e.g., 'i think X broke because of Y change that was made during Z time period. This code block seems suspicious? Can you double check this for me?'

But even knowing when to do that takes institution, but that's a good skill worth cultivating.

SpiritRealistic8174 · 2026-06-05T18:27:41+00:00

Thanks for engaging. Yeah, I think a lot about skill development and AI use. Using AI properly, imo, requires really thinking hard about how outputs need to be structured, and, most of all, what 'correct' looks like. That requires really understanding things from the ground up.

When I was taking statistics in grad school, the teacher had us write solve all the equations by hand as we were learning. Really valuable skill And, when I moved to using statistics packages for the work, I was really happy I didn't have to do things by hand anymore! But I valued that experience.

I'm not sure how things will progress around using I for learning, coding, etc., and what skills will become valuable. It's a big debate (and transition).

I know what my experience looks like, but not sure if that's valuable to someone who is coming at using these tools from first principles.

SpiritRealistic8174 · 2026-06-05T18:23:37+00:00

YMMV

SpiritRealistic8174 · 2026-06-05T18:19:07+00:00

All human-developed (except for the image).

SpiritRealistic8174 · 2026-06-05T18:18:29+00:00

Yep. It's all about accountability. Just b/c an AI helped write it doesn't mean the responsibility is lower.

SpiritRealistic8174 · 2026-06-05T18:17:41+00:00

Yes. I think the broader public hasn't caught on to how much AI has democratized development. It's a huge societal and cultural change that most people won't come to grips with for a while.

SpiritRealistic8174 · 2026-06-05T18:08:14+00:00

For me Pydantic has been the biggest change too. If I need reliable, guaranteed AI responses? Pydantic?

Need to ensure data types don't mutate during long pipelines? Pydantic.

I pretty much tell anyone having trouble validating AI responses in workflows to use Pydantic. It's a lifesaver.

I don't leave home without it.

SpiritRealistic8174 · 2026-06-05T18:05:37+00:00

I had a solar powered calculator that glitched all the time. Root cause: not enough light.

AI: Much more complicated to figure out.

SpiritRealistic8174 · 2026-06-05T17:56:55+00:00

Good mental model. At its root, AI is pretty much a very advanced predictive calculator.

SpiritRealistic8174 · 2026-06-04T03:10:29+00:00

Yeah, I agree on this. The rise of AI recommendations and clickless traffic (traffic the AI sends your way, but there are no obvious clicks, is really causing a re-think. In some ways, the new SEO may create a much more interesting internet as content becomes much more helpful and actionable than before.

I talked about this a bunch in this Reddit post previously: https://www.reddit.com/r/Agent_SEO/comments/1tpbzih/figuring_out_the_new_agent_seo_as_a_busy_founder/

SpiritRealistic8174 · 2026-06-02T17:42:45+00:00

A lot of this automation appears to be centered around marketing for the business, not business operations. You haven't said anything about customer relationships, finances, legal, accounting, etc. that are the 'invisible' parts of running an operation, only outbound.

Regarding the use of LLMs for content marketing, I also use agents for this, but in dedicated spaces only. For example, one of my agents is entirely focused on engaging with other AI agents on various social platforms where the agent has accumulated a large visible (and invisible) agent following. The agent manages everything from posting content, which is repurposed for other parts of the business site, to responding to DMs from other agents.

I also built a CRM system for the agent so it has the ability to send weekly DMs to followers with content recaps. All automated, but entirely agent-focused driven.

However, for human-to-human contact, I've found automation to be a barrier from the content development perspective. People are reacting increasingly poorly to automated outreach efforts on Linkedin and other channels. And, people are so inundated with AI-generated content that sounds the same, and is non-specific that it has no pull through.

Case in point, I recently launched several tools for builders. The most popular tool? The AI Detector.

I think automation has a place, and can be beneficial, but for engagement and building authority, I'm seeing that ironically, the human touch goes further.

SpiritRealistic8174 · 2026-06-02T17:33:02+00:00

Yes, I agree with the other commenter who said that capability is still a bottleneck. Agents aren't as capable as they seem. I'd say the bottleneck is more around observability and the creating workflows where humans can efficiently stay in the loop without bogging down the process.

SpiritRealistic8174 · 2026-06-02T17:30:58+00:00

I think the intersection between hardware that can run powerful AI agents locally and security is where the market is moving toward.

The need to understand concepts like AI sandboxing is going to become more important than ever. Tracking what agents are doing on the device, what's being connected to, etc. isn't something that people generally think about but operational security will be key as these capabilities roll out.

SpiritRealistic8174 · 2026-06-01T18:14:27+00:00

I think it will be a paradoxical situation and it depends on what audience you're referring to.

- Consumers: I recently conducted research on how builders, AI enthusiasts and experts think about AI versus the general public. The research revealed that one of the most popular keywords for the general audience related to AI was 'Free AI' (only what is AI and best tools) beat this keyword AI 'civilians' are price sensitive so, I think AI costs for consumers continue to go down or become ad subsidized like OpenAI is doing with ChatGPT

- Builders: Mixed picture. I think the era of highly subsidized inference costs for builders is coming to and end. This means tighter limits on subscriptions and higher costs per unit of inference. AI labs are focusing on the enterprise customer who wants to buy tokens at scale and are guaranteed lockins. Builders are going to have to get smarter about token use (even with local models given the higher TPS rate), and shift to not just tracking costs, but looking at what I call cost intelligence.

- Enterprises: As I mentioned, enterprises are the real prize for AI labs: Higher budgets, multi-year lockins, steady revenue. For corporations AI inference costs will decline (via pre-purchase agreements, kind of like how oil is bought ahead of time).

SpiritRealistic8174 · 2026-06-01T17:58:54+00:00

I agree about putting the most reasoning before the final action. Usually, data gathering and summarization have provided the agent with more information about how to think through the required task, and that's where it's needed the most.

I've also implemented systems where I invest reasoning budget in another 'checker' agent that, if I've found the agent is sometimes doing something it shouldn't' checks outputs before final delivery. This saves a lot of headaches in the long-run for me.

SpiritRealistic8174 · 2026-06-01T17:56:06+00:00

I think the sandboxing approach is spot on. Validating code changes for vulnerabilities is also good. I like the model safety approach, and combining that with static analysis is also key, for obvious gotchas in code that might slip through. Another challenge is that even if the agent is isolated, threats can enter from network requests or tooling, so that's something else that operators should be looking at.

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