u/Hybrid-Intelligence

Way back when, if you selected thinking mode or extended thinking, high thinking, or whatever your version called it, it was slow and really thought things through, but you know you didn't need that most of the time.

Now, I feel like no matter what it's set on, as long as it's not Pro, most of the time it throttles down and gives me a near instant answer anyway.

I'm starting to wonder if they've got hard controllers in place to lower token usage and save money ahead of the IPO.

I use AI for enough things, personally and professionally, that a lot of my day has become one long chain of handoffs.

I’ll start in one AI workspace because it has the right background. It gives me something useful. Then I take that result to another one because that one has different files, a different skill, or a better understanding of the next piece of what I’m trying to do.

Then maybe I bring the result back somewhere else.

Sometimes the systems can connect to an external tool I'm using. Often they can’t. Or they can connect in theory, except not in the way I actually need. So I become the connection.

I spend a surprising amount of time pulling the useful parts out of one conversation, pasting them into the next GPT or Project, and explaining just enough so it knows what it’s looking at. Then I take that result somewhere else. Sometimes I wind up back where I started.

It's kind of becoming a real skill. Not prompting exactly. More like knowing which AI space should handle which part of a task, what context it needs, and when the output is ready to be passed somewhere else. It’s just weird to be playing a role that didn’t exist a few years ago.

It’s also why I’m less convinced that we’re all going to end up with one giant AI that does everything any time soon. I don’t really want that anyway. Different GPTs and Projects know different things. They have different instructions, different knowledge bases, different capabilities. That separation is useful.

So, for some absurd reason, ChatGPT replaced its flawed Canvas mode with infinitely worse Writing Blocks.

There's so much I wish they had done differently, but that's another post entirely. The issue I'm talking about here is the editor within the writing blocks is infinitely less 'intelligent' than in the general chat window. It feels like it's 4o mini or something.

It's essentially useless. When I want to make a change, I either need to do it myself or exit the writing block to type it into the main chat thread.

So, for some absurd reason, ChatGPT replaced its flawed Canvas mode with infinitely worse Writing Blocks.

There's so much I wish they had done differently, but that's another post entirely. The issue I'm talking about here is the editor within the writing blocks is infinitely less 'intelligent' than in the general chat window. It feels like it's 4o mini or something.

It's essentially useless. When I want to make a change, I either need to do it myself or exit the writing block to type it into the main chat thread.

So, for some absurd reason, ChatGPT replaced its flawed Canvas mode with infinitely worse Writing Blocks.

There's so much I wish they had done differently, but that's another post entirely. The issue I'm talking about here is the editor within the writing blocks is infinitely less 'intelligent' than in the general chat window. It feels like it's 4o mini or something.

It's essentially useless. When I want to make a change, I either need to do it myself or exit the writing block to type it into the main chat thread.

Now that memory is evolving in the major models, there are 2 things that are quite important:

1. Delete the chats that you don't want to shape future interactions with the model.

2. Go through and delete memories the model saved from your conversations that don't make sense anymore (or never did).

These things are starting to become really important because the models are using more and more of our interactions with them to shape how we interact going forward.

On top of that, know that nothing in your chat history is really private. Any court can ask for it. A litigation at your job can even demand your personal chat history.

Times are changing and we all need to keep up.

Are people hoarding their cool ideas?

I joined reddit to engage with people, share some thoughts, and learn what cool things people are doing with AI.

I'm not a developer at all so, I pretty much just care what they're doing with the major models. I've joined a zillion of the relevant subreddits, yet what I'm finding is very little.

Are people hiding what they're doing? I could imagine several reasons for this:

1. Afraid of criticism,

2. Don't think it's good enough,

3. A feeling of proprietary ownership,

4. People really aren't doing that much, or

5. There's no perceived benefit to sharing.

I know #3 is holding me back a bit, although I do share in bits and pieces.

I'm starting to wonder if it's really #4 though. Obviously this is different for coders. I'm less interested in what they're doing unless it's achievable for those of us who have never been on GitHub.

I don't know what's going on. Or maybe I do. I'm so much more tired than I used to be, and I blame AI.

Every day, often more than once, I get some small idea. It might be something I can improve at work, or a tool I want to add to the business. In any event, something has changed since I began working with AI, and now those little thoughts are draining me.

Back before AI, there was usually enough hassle involved in doing anything more that most of those thoughts faded just as soon as I had them. I didn’t even know whether they were worth thinking about, let alone doing something with.

Now I can open a chat window and ask for feedback. If it seems plausible, I can use that same chat to get right to work, making the idea real. Sometimes though, the AI is too optimistic and it sends me down a rabbit hole for a couple of wasted hours.

I read something once suggesting that when technology takes care of more routine work, the stuff left over for humans to do becomes more cognitively demanding. That feels right to me. I'm mentally exhausted all the time. With AI, I’m spending less time on lower level work and more of it on analysis, judgment, and challenging the AI.

I'm glad that's happening. It's much better that I spend my time only on the highest value work, but I need a break. So, what to do about it? I don't know what everyone else is doing, but I'm going to be trying something new because this isn't working for me.

I’m going to get more deliberate about taking breaks. When I do, if something jumps in my head, I'm going to refuse to check it with AI. Instead, I'm going to jot a note somewhere and come back to it when I'm working, not when I’m supposed to be enjoying my recovery time. If anybody else has found a way to deal with this new kind of fatigue, please share.

Every other post seems to be saying AI will replace all the office workers. Ironically, half of those seem to be AI generated. In any event, they're wrong.

Sure, AI can do a lot of the repeatable workflows. It still gets a lot of that wrong, but it'll get better ... probably. It’s not what I spend most of my time working on though. My job is endlessly anecdotal.

In my experience, most professional work is judgment in messy or politically charged situations. It’s thinking through what’s actually happening underneath the stated issue, building relationships and credibility so people tell you the truth, and then figuring out how to present stuff to get buy in and avoid pissing everybody off.

AI will make strong professionals even more effective. It’ll take more of the mechanical work off our plates and give us more time for the important stuff. It's never going to turn someone who lacks judgment, courage, trust, or organizational feel into an A player. In fact, those are the few people whose jobs will be at risk. For us more senior folks it's imperative that we train them now, before the pressure is on.

To me, the most useful question isn’t whether AI can handle a bunch of our tasks. It clearly can, so what? It's really about whether we’re developing professionals who can do the part of work that remains when the menial tasks are gone.

The future will belong to those who can do the work that no system can replicate AND to those fewer people who are exceptionally gifted at pushing the boundaries of what AI can do.

People keep asking whether AI will replace HR. I think that question misses where most HR work actually happens.

Sure, AI can draft a job description, summarize an investigation file, compare policy language, help prepare a compensation analysis, or produce a first pass at an employee communication. It should. There’s no prize for spending three hours doing work software can handle in three minutes.

But that isn’t most of the work. It’s not what I spend most of my time working on!

Most HR work is judgment in messy, incomplete situations. It’s thinking through what’s actually happening underneath the stated issue, building relationships and credibility so people tell you the truth, and then responding in real time to the leader in front of you while staying attuned to the expectations, priorities, and risk tolerance of the more senior leaders whose views will shape the outcome.

AI will make strong HR people more effective. It’ll take more of the mechanical work off our plates and give us more time for the work that actually matters. It won’t turn someone who lacks judgment, courage, trust, or organizational feel into a strong HR partner. In fact, those are the few people whose jobs will be substantially at risk.

To me, the most useful question isn’t whether AI can do HR tasks. It clearly can, and it’ll do more every month.

It's really about whether we’re developing HR professionals who can do the part that remains when the tasks are gone.

The future of HR will belong to those who can do the work that no system can replicate AND those who are exceptionally gifted and pushing the boundaries of what AI can do.

The signs of AI writing have changed.

I’ve started noticing a new set of AI writing tells.

The em dash has largely faded from the recent models, at least enough that I no longer treat it as much of a signal. In its place, I keep seeing colons everywhere, a ridiculous number of hyphenated words, and prose that suddenly seems allergic to contractions.

Don't worry though, old favorites like contrast framing still loom large. “This isn’t about X. It’s about Y.” That one has survived every model update so far.

None of these things proves that a person used AI. People use colons. People hyphenate words. Some people write more formally than they speak.

I wish folks paid more attention to this, especially at work. Everything you produce with AI carries your reputation with it, because the person reading it isn’t going to blame ChatGPT if it sounds generic, stilted, or like you never actually had a thought of your own. They’re going to form an impression of you.

I care about this enough that I co-founded Hybrid Intelligence Academy. I’m mentioning that only to make clear how deeply I care about the issue, not to sell anyone anything. The goal should be to use AI to do more, do better, and do different, while still bringing your own judgment and voice to the work.

Please be careful with your reputation. It takes a long time to build, and sloppy use of AI can make you look less capable than you are. If you’re trying to figure out how to use this stuff well, reach out. I genuinely want to help, whether HIA is involved or not.

I have very strong feelings about OpenAI sunsetting Canvas and replacing it with Writing Blocks.

Canvas wasn't perfect. It could be quirky, and it did not always open when it should have. Still, it understood something important about working with AI on writing, the strongest results are produced by collaboration. Now, we're back to AI draft something, no that sucks, draft again.

With Canvas, I could directly edit a word, a comma, or a sentence. I could see the document as a document. I could ask ChatGPT to focus on a specific section. I could brainstorm with ChatGPT.

It was a real collaborative workflow.

Writing Blocks are worse in almost every way.

They can't converse with you, only redraft. They are awkward to navigate. They make even small edits feel detached from the actual drafting process.

I asked ChatGPT to put the difference in one sentence. It said:

“Canvas treated the draft as a shared object. Writing Blocks treat it as an answer that happens to look like a document.”

Exactly.

Writing Blocks do have one advantage: they can handle longer text. That's it.

They're a polished way to display text in chat. Canvas was a place where a person and an AI could actually work on something together.

OpenAI, pay attention to your users and bring Canvas back! You can't expect knowledge workers to do their daily work with writing blocks, the result will be AI slop. Help us!

Please, now, please!!!

Whenever I want AI to complete something in phases, without my intervention, it fails.

I keep running into this, regardless of which model I use and I’m curious whether anyone has found a genuinely decent solution.

When I say “phased,” I mean cases where the order matters because each step produces something that the next step depends on. Sometimes I also mean more loosely defined stages, but the key point is that later steps should be grounded in what actually happened earlier, not just inferred from the original prompt.

For example:

Phase 1 - Generate an image.

Phase 2 - Look at that actual image against a few criteria.

Phase 3 - Identify the most important flaw.

Phase 4 - Regenerate it to eliminate that flaw while preserving what worked.

That is a pretty normal human workflow. You make something. You look at it. You judge it. You revise it.

The models seem remarkably bad at honoring it. Most typically, the failure mode is that they collapse the steps. Other times, they stop carrying the actual output forward in a way that makes the next phase meaningful.

Either way, it has lost the point.

It is no longer building on the result of the prior phase. It is predicting what the final output should look like from the original prompt.

I’ve tried the obvious instruction variations. What I have not found is a reliable way to make the model stay in a true multi-phase build without needing to constantly manage and reprompt it.

I realize this is easier with agents, but it seems like it should be doable without something that eats tokens like potato chips. I just haven't been able to figure it out.

I work and advise across 5 major models:

ChatGPT

Claude

Gemini

Perplexity

Copilot

In addition, I use other tools like Gamma, Descript, CapCut etc.

For those of you who work across different models like that, how are you keeping up with every new feature, the benefits, and detriments?

I absolutely love this stuff. It's my job and my hobby, but in the past 6 months, evolution has accelerated so much that I'm sure there's a lot I don't know.

I have agents help me but they feel insufficient. Maybe it's just going to be this way now.

Hmmm ... I keep wondering whether we’re asking this in the way most likely to produce a useful answer. Or maybe we’re weighing with our thumbs on the scale.

Many of us hear something like: “LLMs are just next word prediction machines.” Aside from my view that the statement is both unmeaningful and wrong, it also falls short of resolving the consciousness question.

Maybe what human brains do is similar enough to make the distinction largely, though not entirely, without significance.

To my way of thinking, consciousness seems to require some combination of:

\- the ability to reason;

\- some form of sense of self or self-awareness;

\- memory;

\- persistent existence or continuity, meaning some version of itself carries forward across time;

\- the ability to reflect on and revise its own thinking; and

\- the ability to learn from experience or new information and carry that learning into future thinking.

LLMs exhibit versions of these things, even if those versions are thinner, shorter-lived, more externally scaffolded, or organized differently from the human versions.

Humans aren't so great at this stuff either. We forget, reconstruct, rely on other people, and often have limited insight into why we think or want what we do. During a single 'conversation', an LLM can construct and maintain a model of the user, the problem, relevant facts, competing interpretations, and its own role in the interaction. It can reason through novel issues, recognize contradictions, revise its conclusions, and explain why it changed its mind.

The clearest difference may still be persistence. Current systems don't seem to carry a stable, integrated self forward through time in the way humans and animals do. They have context windows and increasingly durable memories, though their continuity remains partial, externally supported, and often reconstructed for the moment. They are brilliant mayflies, or so they say.

That leads to the question I find most interesting, could an LLM be mostly conscious, or perhaps entirely conscious, only while actively responding to a prompt? In that period, it may assemble an integrated perspective of the person, problem, its own limitations, and a path toward an outcome. When the computation ends, that perspective may largely dissipate.

Sure, that would be radically different from human consciousness. That doesn't mean there isn't consciousness of some sort.

So, I'm curious where people think the decisive line is.

What features do current LLMs lack that, if achieved, would make them conscious, or sufficiently similar to conscious beings as to be functionally equivalent for the purposes that matter?

Now that some of the models have persistent memory, fractured as it is, it seems like they’re growing closer.

The long term fear that AI will permanently shut young people out of professional work is probably overbaked.

AI will eliminate a lot of junior work. Of course that's true. Research, first drafts, basic analysis, slides, and similar work will all get done by AI.

Nonetheless, companies will still need experts. They'll need people who can frame the real problem, decide what matters, recognize weak reasoning, see when the machine is missing the point, and push toward a better answer.

Those people will need to build their skills somehow. So, companies will create paths for young people to become experts. I suspect that path will look more like apprenticeship.

The entry level professional will learn how the expert uses AI, critiques it, guides it, and polishes its work

Companies that use AI only to eliminate junior roles may solve a short term cost issue while creating a long term expertise issue.

Companies that build real apprenticeships will almost certainly end up with the stronger talent pipeline.

Hmmm ... I keep wondering whether we’re asking this in the way most likely to produce a useful answer. Or maybe we’re weighing with our thumbs on the scale.

Many of us hear something like: “LLMs are just next word prediction machines.” Aside from my view that the statement is both unmeaningful and wrong, it also falls short of resolving the consciousness question.

Maybe what human brains do is similar enough to make the distinction largely, though not entirely, without significance.

To my way of thinking, consciousness seems to require some combination of:

- the ability to reason;

- some form of sense of self or self-awareness;

- memory;

- persistent existence or continuity, meaning some version of itself carries forward across time;

- the ability to reflect on and revise its own thinking; and

- the ability to learn from experience or new information and carry that learning into future thinking.

LLMs exhibit versions of these things, even if those versions are thinner, shorter-lived, more externally scaffolded, or organized differently from the human versions.

Humans aren't so great at this stuff either. We forget, reconstruct, rely on other people, and often have limited insight into why we think or want what we do. During a single 'conversation', an LLM can construct and maintain a model of the user, the problem, relevant facts, competing interpretations, and its own role in the interaction. It can reason through novel issues, recognize contradictions, revise its conclusions, and explain why it changed its mind.

The clearest difference may still be persistence. Current systems don't seem to carry a stable, integrated self forward through time in the way humans and animals do. They have context windows and increasingly durable memories, though their continuity remains partial, externally supported, and often reconstructed for the moment. They are brilliant mayflies, or so they say.

That leads to the question I find most interesting, could an LLM be mostly conscious, or perhaps entirely conscious, only while actively responding to a prompt? In that period, it may assemble an integrated perspective of the person, problem, its own limitations, and a path toward an outcome. When the computation ends, that perspective may largely dissipate.

Sure, that would be radically different from human consciousness. That doesn't mean there isn't consciousness of some sort.

So, I'm curious where people think the decisive line is.

What features do current LLMs lack that, if achieved, would make them conscious, or sufficiently similar to conscious beings as to be functionally equivalent for the purposes that matter?

Now that some of the models have persistent memory, fractured as it is, it seems like they’re growing closer.

As far as I can tell, the best voice mode for LLMs right now is ChatGPT's advanced voice mode. That's still running on ChatGPT 4o. So, it's a fascinating toy with occasional real value.

I keep thinking about the AI Assistant in HER, obviously not the romantic part.

The biggest differences right now are the persistence of existence beyond when responding to prompts and thinking quality. Well, we're not going to solve the persistence issue any time soon (at least I hope not).

So, what's left is reasoning quality and integrations. Well, today, with 5.5 and app connectors, I feel like we're getting pretty close if you want to limit your interactions to a keyboard. For multimodal though, we really need a substantial upgrade from 4o.

OpenAI has released GPT-Realtime-2 which supports voice using ChatGPT 5.5. So, I imagine it's only a of time before we see a substantial upgrade to advanced voice mode. At least I sure hope so.

OpenAI, if you're reading this, please let us know at least the theoretical plan.

Am I too cautious?

I'm terrified of letting an agent have free reign over my machine. I see the posts all the time about people having agents run their browsers and do things on their computers.

That seems so dangerous to me. It has no accountability and no morality. So much can go wrong.

What's so valuable that makes it worth it to give over that much control ... seriously, I'd really like to know.

Today, I write custom GPTs, Projects, Gems, Spaces and such. I also use ChatGPT's workspace agents (which seen nicely constrained). That combination meets all my needs, save for one exception. I'm not a developer or engineer and I don't have an orchestration layer. So, I'm functioning as the API shuttling information between and among my tools and agents.

I feel like that's a tiny price to pay for peace of mind.

Now, I don't want to be the senior citizen decrying the dangers of putting your credit card information on the internet, but at the same time, I'm not not doing that.

Once upon a time, AGI (artificial general intelligence) meant something like an AI system that is able to learn, reason, and apply knowledge flexibly across cognitive domains generally, at roughly human level competence.

Well, we passed that a long time ago. Now when people use it, they seem to be meaning an AI system that is as good as humans at everything. We know that today, AI is already better than humans and tons of things. So, of an AI system also got as good as humans at everything else, wouldn't that go right past AGI and become ASI (artificial super intelligence)?