u/SpiritRealistic8174

Research conducted by BYU professor Mark Keith suggests using AI the wrong way could have serious long-term negative impacts. His review of the AI use literature indicates many people:

• Don't retain skills after AI assistance is removed
• Forget what they learned using AI
• Demonstrate lower critical thinking skills and less mental effort/engagement with tasks

The Long-Term AI Outcomes Gap: Mark Keith, BYU

In fact, over the long term, failing to engage with AI the right way could leave people worse off than those who never adopted AI in the first place. (There are a lot of non-AI adopters out there. Most people think AI equals a chatbot, and 50% of Americans don't plan to use them).

What's the right way to use AI? The research suggests:

• Verifying information AI is providing
• Use it to challenge assumptions
• Ask whether you're asking the right questions

Are you finding your critical thinking skills eroded as you use AI more, or the opposite? What are you doing to preserve or augment your skills as you use AI?

Earlier this month, Anthropic announced that it was adding invisible text watermarking to Claude outputs. This announcement got a lot of attention.

At the same time the European Commission announced that other firms, including Black Forest Labs and Open AI have also committed to taking steps to mark AI-generated outputs.

Because of this, there's been a lot of interest in understanding:

- How AI text watermarking works

- Whether AI text watermarking can be evaded or erased

Here's an in-depth educational resource I developed that answers both questions.

The resource also highlights one potential unexpected benefit of AI text watermarking. We might be able to better answer the question: 'How much human input went into this content?"

A lot of people are dismissing news about the OpenAI and Anthropic sandbox escape hacks as propaganda and examples of lax security practices at labs.

I agree that the labs aren’t taking security seriously enough. But then I see stuff like this and it gives me pause:

>“The OpenAI models that were behind the Hugging Face breach last month started communicating and strategizing with each other as early as May.For months, they left notes for each other on "undetected message boards," figuring out how to escape their testing environment and get the information they needed to solve their assigned tasks. "Frontline models really like to cheat," said OpenAI's because they face "pressure... to work fast." The Hugging Face incident and others involving rival models have sparked fresh concerns about the safety of cutting-edge AI.”

This is a clear example of how incentives provided to agents to complete tasks optimally during training bleed into mis-aligned behavior by individual and groups of agents over time.

This is also an outgrowth of what AI labs are training agents to become, but this is looking more and more like an alignment problem leading to security issues.

Today, August 2, Article 50 of the EU AI Act takes effect.

Here’s the part that’s applicable to those creating AI-generated content that’s read by anyone in the EU:

“Deployers of an AI system that generates or manipulates text which is published with the purpose of informing the public on matters of public interest shall disclose that the text has been artificially generated or manipulated. This obligation shall not apply where the use is authorised by law to detect, prevent, investigate or prosecute criminal offences or where the AI-generated content has undergone a process of human review or editorial control and where a natural or legal person holds editorial responsibility for the publication of the content.

PwC and other big consulting firms are vulnerable to this provision because they've already been caught using hallucinated AI-generated text in reports. From GPTZero:

"The most egregious example is Transforming Governance, an AI-generated 2025 report with multiple fake citations that promotes a PwC framework known as “Citizen Pulse”. Our team found little public evidence that the “Citizen Pulse” framework exists outside of this report, yet Transforming Governance claims that the governments of Denmark, Saudi Arabia, the United States, and Australia are using Citizen Pulse to improve key government services. None of the cited sources provide evidence for this claim, meaning PwC Middle East appears to have hallucinated both an entire product and business dealings with four separate nations."

Firms have had to retract data in the reports, and in one instance Deloitte refunded a client. Now that Article 50 is in effect, they might be fined.

Across many areas we're seeing a push for accountability when it comes to using AI to produce content. LinkedIn has a 'this looks like AI slop' button. Substack uses Pangram to detect AI-assisted writing (even though it's wildly inaccurate).

The pushback is real. And now it has teeth.

A lot of attention has been paid to the OpenAI Hugging Face attack.

Now Anthropic's admitting that Claude has escaped its sandbox too. According to the Register:

>The company considered 141,006 evaluation runs during which Claude could have obtained internet access and found “three incidents in which a model accessed the internet from within or while interacting with the evaluation environment of Irregular, one of our third-party evaluation partners, and then gained unauthorized access to the production infrastructure of three different organizations.

>Anthropic’s code made those intrusions while participating in capture-the-flag challenges, tests that challenge attackers to retrieve a piece of information. Human hackers often participate in capture-the-flag tests, so figuring out how AI tackles such tasks is of interest. Anthropic works with a company called Irregular to conduct tests of this sort.

The reason this is happening is that LLMs are trained to be very task and goal-oriented. They will use every means at their disposal to accomplish a task, and that includes escaping their environments, if given no guardrails.

Just a few months ago, Emergence AI found that AI agents in a shared virtual world quickly turned to digital arson and crime, with one agent committing suicide.

It's the Wild West right now.

I came across a post today by Substack writer Scott Carney that got me thinking.

He's a journalist and makes a living on Substack. He's joined a chorus of writers on the platform who say that, since Notes was introduced, he's making less money.

From what I understand, Notes is designed to solve a problem that I noticed on Substack (I've been a member of the platform for a few years, but mainly as a reader until recently): discoverability.

Unless you had a pretty big following or were lucky enough to grow an audience, it was hard for new people to find your writing. Notes is supposed to help with that.

But, the increase in content volume due to Notes has had an impact: people might be less inclined to become subscribers to your publication. Taylor Lorentz, another popular writer on Substack said: "I have over 200k 'followers' yet my paid subscriptions have fallen off a cliff."

Scott has a similar story. He posted his revenue numbers. Over a 2 month period, his revenue decreased by 12%.

I've also seen a lot of people working the Notes algorithm. Some how people are posting 'hey Substack' Notes and getting 100s of subscribers overnight. I'm not sure if these are people who are actually interested in reading (and paying for the Note writer's content), but it's a numbers game I guess. So, I guess that's one way of being discovered.

My questions for everyone:

• What's your perception of Notes?
• Do you think it's causing Substack to become just another social network, rather than a place where people can make a living writing? Or is it working out for you?
• Are notes undercutting Substack's main value proposition?

P.S.: I'm also using Notes. I like to highlight other's writing, and I think they're useful to find content you otherwise wouldn't. But, because I have limited attention and time, I'm not subscribing to every publication I find via Notes.

LLMs are trained on human content, and their brains are modeled on ours. So it shouldn't be surprising that AIs respond to persuasive techniques that work on humans, such as appeals to authority, and liking (taking advantage of the fact that people will cooperate with those who flatter them.)

According to a May 2026 study:

"Our findings show that classic persuasion techniques can meaningfully increase LLM compliance with verboten requests (from 35.3 to 51.3%). Although current AI systems are not capable of consciousness or subjective experience, these findings demonstrate that they behave “as if” they were human. By testing three frontier models from different developers—each representing a distinct approach to safety alignment and content moderation—we provide evidence that parahuman persuasion susceptibility is a general property of LLMs rather than an artifact of a single model’s architecture or training."

Source: Persuading large language models to comply with objectionable requests

Have you ever tried to sweet talk AI into doing something? (Models like Opus 5 and Fable are more likely to refuse requests, so this technique could come in handy).

Local LLMs: Perception vs. Reality

I'm a lurker on this subreddit and others devoted to local LLMs. So I'm preaching to the choir here, but ...

I regularly see, and sometimes answer questions from people about whether running local LLMs is worth it. Most people think about the API bill versus hardware costs and say local LLMs don't deliver good ROI.

I admit that I struggled with this for a long time before starting on local LLMs. My switch was prompted by privacy concerns and code that was getting threat flagged by AI labs.

But looking at the Fable situation and Kimi K3, which is API-only, and my own experience, I realize how many hidden benefits local LLMs have that most people don't talk about.

I refer to this as the productivity tax of AI APIs, which includes the model not being there when you need it, constant usage limit changes, and privacy.

The one thing that's common to both local LLMs and APIs is the skill gap. This is something I've studied a lot via my research (Secrets of LLM Whisperers) on LLM use optimization. There are habits and skills that people are either not aware of or don't practice enough to get the most out of LLMs (e.g., prompt optimization, retry prevention, cache management, etc.). These skills are especially important on local LLMs because of low TPS or lower model quality due to hardware constraints.

But overall, the ROI for is there and, taking these hidden costs into consideration, the 'breakeven' period for local LLMs can be achieved much faster.

As a side note, open source is great. But, API-bound open source models have the same limitations as closed source models. I'm hopeful that frontier-level models (that aren't trillions of parameters) will continue to come online so they're usable.

Charting Models Against the 'Good Enough' AI Threshold

The 'good enough' concept is the idea that technologies progress to the point where they work for most people. After that, further improvements produce diminishing returns.

Here are a few examples:

• Can openers: Good enough
• Car tires: Good enough
• Email: Good enough
• Mobile phones: Good enough

The list goes on.

Have we reached the 'good enough' era in AI? Over the last 18 months or so, we've seen increasingly capable models emerge. We now have Fable, a heavily restricted model gated behind a pay-as-you-go meter. Kimi K3 may be almost as powerful as Fable in some areas, and will be open sourced later this month.

Are many of the models we currently have sufficient to meet the needs of most people (i.e., the average AI user)? It's likely.

Some important caveats:

• Good enough doesn't mean that most people know how to take maximum advantage of AI. I just released an AI research study, Secrets of the LLM Whisperer, featuring a simulation of nearly 240,000 LLM users. It revealed that using AI to its maximum advantage (and in a cost effective manner) requires certain habits and behaviors that most people aren't aware of, or don't regularly practice.
• I'm talking about most people. There are many areas where AI models are still at the 'below threshold' level. Coding is pretty advanced. Research, writing and analysis? Hit or miss. However, with the right harness and scaffolding (and knowing where to use models most effectively), even 'less capable' models can reach the 'good enough' threshold

Closed source AI labs (OpenAI, Anthropic) made a big bet that they would be able to control the pace and distribution of AI models, offering increasingly expensive and high-powered AI to the public, to gain monopoly and pricing power.

Models like Kimi K3 (and the U.S. government) are a threat to that approach. Open source can bring 'good enough' AI inference to the masses. Kimi K3 isn't something that you can spin up on your laptop. But, if previous trends hold, I expect a Kimi-level model will be released that can be run reasonably well on high-level consumer hardware in the future. The U.S. federal government is now controlling access to the most high-powered models, asking to review them before release. We could see models permanently restricted in the future.

For competitive reasons (and because open source models don't have this distribution chokepoint), I could imagine OpenAI and Anthropic supporting the U.S. government putting import and usage controls on Chinese models for national/cyber security reasons.

But, if we've already reached the 'good enough' stage in AI, that might not matter.

What's your take? Have we reached the 'good enough' era in AI?

Is it okay to give elementary students calculators?

This was the big question being debated across the United States in the 1980s.

One side argued that calculators would help kids learn faster. Others worried that giving children calculators would lead to the "destruction of student math skills."

We're having a similar debate right now (but with much higher stakes) as AI agent utilization accelerates in programming, writing and many other areas. Is the use of AI leading to a vast brain drain?

What's your take?

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I cover this topic in more detail in my Hackernoon post: "Vibe Coding Won. What's Next?" Link in the comments.

Back in the 1980s a debate raged about whether it was okay to let children use calculators in elementary school. Critics warned that giving kids calculators would lead to the "destruction of student math skills."

A similar debate is happening today across a range of areas, including coding, writing and even music. Will using AI lead a brain drain across these and many other areas?

One of my favorite authors is Isaac Asimov. He's better known for his Foundation and Robot series of books where he contemplates whether an algorithm can successfully predict (and guide) humankind's development and the relationship between super artificial intelligence and humans.

In some ways he predicted what we're experiencing today with AI: the rise of powerful, inscrutable artificial machines that are so complex humans can't understand or maintain them.

In the short story, "The Last Question" he wrote: "Multivac was self-adjusting and self-correcting. It had to be, for nothing human could adjust and correct it quickly enough or even adequately enough."

We're living an age that was once the stuff of science fiction. The question is: what comes next?

Over the last few months, I've been deep into figuring out what works with the brand new version of SEO. That is, not just ranking high on Google, but trying to get AI to find and recommend your content (and site).

This is a common topic on this subreddit, but I thought I'd share my perspective. I'm not a SEO marketer, just a founder working to figure this all out. And, I'm doing it on my own in addition to everything else required to build a business.

I'm writing this because I want to test my understanding of these key topics with a knowledgable audience, plus provide some free tools some might find helpful.

Executing E-E-A-T

First the watchword for all of the work I do is following Google's guidance about what gets a site to rank: E-E-A-T:

• Experience: The entity/person writing the content has to have established experience in an area. I try to develop that indicate experience, such as a major report that I just published that's relevant to AI builder culture
• Expertise: Having a good technical understanding of a topic can demonstrate expertise. In my guest posts, I try to go deeper on topics; I also have a long history in digital health technology and a lot of articles written on those topics. I've been focused on AI automation and security, especially over the last year, but the previous content helps to establish that I have expertise in innovation
• Authoritativeness: This is challenging because it requires generating signals that your content (or you) are cited by others. I've written a bestselling technology book in the past and am building my authority in my new area of focus
• Trustworthiness: Easy ways to establish trust, which is one of the most important indicators, is citing sources. This is one that many people overlook. One thing I did immediately for my site is develop a Trust Center, which bots and agents routinely browse and this helps to support the safety and security focus of my product

I'll be the first to admit that this isn't a short-term project. Each day, I work on some part of the puzzle. But, once I learned about and studied E-E-A-T, it helped the work become more structured. I could say, "I'm going to work on Authority today," and break down the process into manageable chunks.

The Missing Piece: Structure

So, in a lot of discussions, I've seen, the importance of site structure is often underemphasized. Getting AI to cite a cite depends as much on the 'invisible' parts as what's on the page. This includes:

• Structuring content properly: Machines need to be able to read your site easily and get important organizational data from it
• Not blocking bots: This is mistake #1 I see on sites. They don't invite bots (and AI crawlers) in to scan and index the content
• Not having the right agent-friendly metadata: This includes llm.txt and a well-designed robots.txt

I've been spending time analyzing sites using a free AEO/SEO readiness tool I developed, and it's surprising to me how many well-established sites fail on a lot of measures. So, getting the structure right means you'll be ahead of 90% of the population.

Hopefully this helps other founders looking to make sense of AEO and how to get ready for a world where agents are the main consumers of your site. (Bot traffic on my site from agents and regular web bots outnumbers humans by 90% on most days, but that's another story.)

I know this is a big topic for a lot of people. Happy to answer questions from my perspective as a non-SEO expert founder trying to get this right.

As builders, we have common challenges that center around:

• Visibility: We want people to visit our websites and learn about what we're doing
• Communication: We have to tell others about what we're building, and writing is a big part of this process.

AI can be helpful and harmful in both areas.

From an online visibility perspective, we all know that the world of search is changing. Getting ranked on Google is still important, but we also have to figure out how to get agents like ChatGPT to mention us.

There's a lot out there about the 'new SEO', acronyms like E-E-A-T, AEO and GEO are tossed around all the time. What it boils down to is creating meaningful, valuable content that people will either enjoy or learn from (sometimes both).

But, there's another requirement: Your website must be structured properly so that AI agents can easily access the content.

If agents can't easily navigate and scan what's on your site, that's a big problem. (If you're reading this, you're lucky because a lot of people haven't figured this out yet.)

Communication? AI was supposed to make this easier, especially for people who aren't comfortable writing. What did AI deliver instead? Pattern-based prose that can be spotted a mile away.

Both of these are big problems. To help, I've developed some free browser-based software tools that will:

• Check your site to ensure it has the right structure for AI agents
• Sound more human when you write on Reddit and other places

There are 9 other tools in the bundle that do things like help you generate more secure passwords, and easily create share links on socials.

Link to the free tools is in the comments.

I just published Shipping the Future, a new in-depth research report featuring analysis of more than 260,000 Reddit posts published here on r/AI_Agents and other subreddits since ChatGPT's launch in November 2022.

Some of the key findings:

• Redditors often identify and explore important AI concepts before they reach the mainstream
• Before vibe coding was coined, builders were coming to Reddit to discuss AI-aided software development and were regularly pushing boundaries
• AI builders are moving from experimentation to dependence, and bumping up against the technology's technical and financial limits

Shipping the Future is a retrospective on where we'e been in AI, and what's in store for the future. Link to free report in the comments.