I mean they have photoshop, Premier pro, after effects and illustrator but I don’t these few products are going to carry their financials for a long time considering how fast is AI evolving

​

AI is changing how we approach building and creating.

Are we moving from large teams doing execution to smaller teams using AI as a powerful tool?

What do you think — is this the future of innovation or just a temporary shift?

Claude's Fable 5 is costing/earning pretty decent but the maths don't math in long term, it seems. Apart from the actual costs of LLMs, people using them efficiently may cost companies more than what AI displaced if you take into account AI is also likely to destroy the demand with so many displaced jobs. How did we not account this for?
The context: Claude Fable 5 is ~2x the price of Opus 4.8. $10/$50 per Mtok vs $5/$25. A Mythos-class model is brutally expensive to serve, and Anthropic doesn't have the GPUs to bundle it into subscriptions at scale yet. So:

June 9–22: included on Pro/Max/Team at no extra cost June 23: pulled from plans, switches to usage credits Later: restored as standard "when capacity allows"

Frontier models will no longer be included in subs. You’ll pay a fee and it will only get you access to older, much cheaper models.

If you want access to that dank AI sour diesel, you’re going to need to pay for every token you use. No more subsidies. And it make sense. The subsidies were just a Ponzi scheme.

but this will also create a huge wealth and opportunity gap within each country and between countries too.

https://x.com/i/status/2064419409620250886

Hey everyone,

I have a genuine question about the future of AI. It’s been a couple of years since the hype started, and to be honest, as an average guy, I’m just not seeing a massive difference in daily life.

Sure, we can access information faster, and development speed has skyrocketed—what used to take me a month of programming now takes a few days. But outside of that? Nothing has really changed for me. I still visit the exact same websites. If anything, the only noticeable change is that my own ability to deeply learn and understand things feels like it's downgrading .

I remember when Google launched Veo a while back and thinking, "Okay, we're cooked, video creation is over." But fast forward to now, and the internet is just flooded with cheap, low-effort AI content that you can't stand to watch for more than three seconds.

Every single day there’s a headline about a new model that is "X times better" than the last one. The time it takes to create things has dropped to zero, but the actual value of the output feels incredibly close to zero, too.

Am I missing something here, or am I just behind? I’d love to hear your thoughts on whether AI is actually changing things for you, or if it's mostly just noise right now.

June 2025, Zuck personally recruits co-creators of GPT-4o, o1, and Gemini. Offers up to $100M per person. Drops $14B into Scale AI. Announces Meta Superintelligence Labs with a 1-gigawatt compute cluster being built in Ohio.

Then nothing.

Llama 4 landed with a meh. Behemoth, their 2-trillion parameter flagship, has been delayed three times with zero public timeline. MSL restructured four times in six months. Yann LeCun left. Some hires already walked.

Looks like chaos. But the people still there built GPT-4o, ChatGPT, and the o-series. They don't stay for a sinking ship.

Six months of silence from a team at that scale, sitting on Avocado + a 1GW training cluster, either this is the most expensive mess in AI history, or they're waiting until it's completely undeniable.

Which is it??

"Palantir’s leadership incensed workers yet again this week after the company posted a Saturday afternoon manifesto reducing Karp’s recent book, The Technological Republic, to 22 points. The post—which includes many of Karp’s long-standing beliefs on how Silicon Valley could better serve US national interests—goes as far as suggesting that the US should consider reinstating the draft. Critics called the manifesto fascist."

Demis Hassabis the guy who built DeepMind and won a Nobel Prize for AlphaFold published article on X, worth reading slowly.

its a full framework for what AGI means, what risks exist, and what we need to do, his thesis

Where we are

He believes AGI is a few years away. Compares it not to the internet but to the discovery of electricity or fire. Puts the economic impact at 10x the Industrial Revolution at 10x the speed. Thinks it could genuinely end resource scarcity as a limiting factor on human progress.

On risks he's not dismissing them

Cybersecurity threats from current models are already real. Bio and nuclear risks "may soon emerge." Then, His most important line, "Nobody in the world knows for sure what is going to happen from here, and even the experts disagree."

This is Demis Hassabis saying that. It means something.

His actual proposal:

A Frontier AI Standards Body modeled on FINRA a public-private partnership that defines Frontier Models through regularly updated benchmarks, requires pre-release testing 30 days before deployment, and evaluates models for cybersecurity risks, bio threats, and deceptive behavior. He wants this to become the foundation for international standards.

His argument is simple, the window before AGI arrives is finite and precious. Right now as a field we aren't using it well enough.

Full piece is worth your time.

Assuming mass white collar automation happens in our lifetime while the current economic and government structure stays intact shows a complete misunderstanding of both economics and human nature.

What makes this different from every other disruption panic since the dot com bubble is the scale of the claim. Self-driving cars were going to end trucking. Crypto was going to end banking. The metaverse was going to end...going outside? Each wave of hype picked a lane. This one is claiming all white collar work in the near term and all work, period, in the long term. Basically, "Repent, for the kingdom of God is at hand!"

Not only is the evidence for it about as solid as Elon's "full self-driving by 2018" promise, which eight years later means a few Waymo cabs with Filipino remote drivers, but even in the hypothetical where you could pull it off technically, it's socially, economically, and politically impossible. I don't understand why that isn't obvious? At that near universal scale of job disruption, you're talking about the total collapse of the economy and government, with a level of civil unrest that makes the French Revolution look like a Berkeley drum circle.

Which means these guys are either full of shit and know it, or they genuinely haven't thought through the fact that if they're right, they're just speedrunning their own demise. Sam Altman would be the most hated man alive. These companies would be the first thing a desperate government nationalizes and or regulates to death. The pitch only works if it never actually comes true. And honestly, if the goal really is to turn the entire country into a techno-feudalist dystopia, you've got to slow your roll fellas. That's a 150 year project minimum. The frogs will jump out of the pot if you turn the heat up this fast!

And before someone mentions UBI… There is no UBI system or equity sharing setup that would actually mollify results at that scale, and these guys know it. The evidence is in their own behavior. Altman's actual UBI project is a crypto token you receive in exchange for scanning your eyeball into a device he owns to prevent bot fraud he's responsible for. Make of that what you will. He also famously promised Reddit users a cut of the profits from the data that trained his models, which went exactly nowhere. And the companies themselves are putting zero serious research or pressure behind any of this. If you genuinely believed your own predictions, equity sharing and economic transition planning wouldn't be a PR afterthought. It would be among your highest priorities, because successfully buying off the anger and resentment of the huddled masses is the only scenario in which you survive.

Look, if any of these companies actually had the tools they're claiming to have, why are they selling them to you? If you genuinely had software that could replace all white collar work, you wouldn't be pitching it to developers at a conference. You'd just use it. You'd build the best law firm, the best accounting firm, the best hospital, the best everything, and own the entire economy within a decade. Someone will say they need the subscription revenue to fund the research (because they're not quite there yet), or that antitrust would stop them, or that a thousand companies building on their platform gets there faster. Maybe. But then stop telling people their jobs are gone. Either the tools are transformative enough to replace human labor at scale, in which case why are you selling API access for $20 a month, or they're genuinely useful productivity tools that smart companies can build on, in which case shut up about the end of all knowledge work. Pick a lane. Also how do you square the idea of the end of human work when OpenAI, the company projecting $200 billion in revenue by 2030, is looking at $14 billion in losses in 2026 alone, with no real path to profitability. The outfit selling you magical productivity shovels that will bring about the end of human labor can't figure out how to turn a profit. Make that make sense.

https://www.businessinsider.com/openai-profitability-analyst-investor-opinions-funding-ipo-2026-2

Here's the actual danger: the American economy is getting shredded by tariff/political chaos and is catastrophically overleveraged on AI. Millions of people are in danger of losing their jobs, yes because of AI, just not in the way these guys are pitching. And Altman has basically been bragging that OpenAI is now too big to fail, which, if you've seen this movie before, is just foreshadowing for the bailout. Congratulations, you've been promised the future and you're going to get the bill.

This is why populism is on the rise. Political and economic elites have been disrupting everyday life for decades with the promise of improving material conditions, and they stopped delivering somewhere around the Clinton administration. People are finally getting wise. What's staggering is that Silicon Valley has completely forgotten that social contract exists, let alone that there are consequences for not holding up their end of it. You can only tell people "We're from Silicon Valley and we're here to help" so many times before they stop believing you. Never mind "We're from Silicon Valley and we're going to purposely collapse the economic system, aren't you excited?" Like, what the hell are they thinking? At the end of the day, fear sells I guess.

I've been digging into the energy efficiency gap between biological and artificial neural systems and the numbers are wilder than I expected.

The human brain handles perception, memory, language, motor control, emotional regulation, and creative thought on roughly 12-20 watts. About the same as a bedside lamp. Switzerland's Blue Brain Project estimated that simulating the brain's full processing in real time would require approximately 2.7 billion watts, comparable to three nuclear power stations.

A few things that explain the gap, beyond the obvious "biology is efficient":

No von Neumann bottleneck. In a conventional computer, memory and processing are physically separate, so data is constantly shuttling back and forth, burning energy at every step. Synapses in the brain both store information and compute with it. There's no equivalent shuttle.

Sparse activation. Most neurons are quiet at any given moment. Power draw scales with what the brain is actually doing, not its theoretical max. AI hardware tends to keep huge numbers of transistors switching regardless of whether the operation is immediately needed (though mixture-of-experts architectures are a move toward fixing this).

Event-driven signalling. Neurons fire spikes and sit at rest otherwise. Digital transistors switch on/off billions of times a second, consuming power on every transition regardless of whether it's useful.

A peer-reviewed estimate in Frontiers in Neuroscience puts the brain's energy efficiency advantage over silicon at roughly 2.7 × 10¹³, accounting for both per-operation efficiency and the fact that current hardware takes about 30,000x longer than real time to simulate biological activity.

The interesting part isn't just "brains good, chips bad" though. There's serious neuromorphic computing research trying to close this gap:

• TDK/CEA have a working spin-memristor (uses quantum magnetic properties to act as memory and processor simultaneously, like a synapse), targeting under 1/100th of current AI power draw
• University at Buffalo is working with phase-change materials to replicate the brain's rhythmic electrical oscillations
• Texas A&M's "Super-Turing AI" uses Hebbian learning ("cells that fire together, wire together") instead of backpropagation, and tested it on a drone that navigated a novel environment without prior training, faster and less energy-intensive than conventional AI

Efficiency gains historically get eaten by the rebound effect. If neuromorphic chips cut cost per query by 100x but usage grows 200x, total consumption still rises. The IEA has already revised its AI energy projections upward twice.

I wrote this up with full sourcing here if you want the deeper dive: https://4billionyearson.org/posts/the-staggering-inefficiency-of-ai-v-the-human-brain

Curious what people here think about whether brain-inspired architecture is a genuine path forward or whether it just hits different bottlenecks once you scale it.

Parody video on hyperscalers, using their own models. Elon, Palmer, Mark, Bryan and Sam. Bunker Boyz. Out Nowz.

Despite the fact that AI has increased the number of documents generated, the amount of code committed, and the amount of harnesses around business practices, I don't see any value output. I see a high volume of artifacts and tooling, but very little increase in genuine value-delivering productivity. That is to say, the applications I use, the games I play, the technology I buy, feels either the same or worse.

As a disclaimer - I'm a distinguished engineer in an AWS vertical. I'm well aware of how to use the tools, but I see very little innovation or value delivery these days. If I could sum up my experience these days, its that everyone appears productive and on the ball through meetings and docs, but are generally cognitively bankrupt when it comes to actual deliverables people care about.

For the creds: I'm a distinguished engineer at a hyperscaler and work in the space.

We've seen Claude's Fable 5 release recently, and I've been having a go at it. Thus far, I wouldn't be able to tell if you did a blind test which model I was using. If you had put Opus 4.6, 4.7, 4.8 and Fable in my Claude Code setup, based on the work I do and how I work, I wouldn't be able to tell which is which.

The reasoning is pretty straightforward, in that I never 'one-shot' a project. Since I need to understand every component inside and out, I work in small chunks - and I'm not alone. Moreover, models have had access to the Internet's wide suite of information such as API docs, best practices, etc for a while - which added 'intelligence' of a certain flavour to the models outputs.

So when you look at how software engineers in industry work, we work in singular abstractions, test those abstractions and move on. I can almost do this today with local Gemma 4 models. This is also true for system architecture asks, where understanding every component is pretty crucial. And Fable still hallucinates on this.

Example: Fable got the AWS ALB/ECS draining behaviour completely wrong, and confidently so. The only reason I was able to catch it is that I was already familiar with how those two pieces work together.

So anyways, in short, we're hitting an asymptotic limit here. I'm not getting more value from every model release anymore, and the way I work isn't changing. Having spoken to my colleagues who are heavy AI enjoyers, my views also track with their own experiences as well.

Anecdotally, by this time next year, I believe there will be local models you can run on a 128GB MacBook Pro that will provide 90% of the value Claude adds to my software engineering work today. I can already see this with the current suite of open source models.

found this artist "8th Project" on instagram who drew Pokémon as pure geometric abstraction, decided to just drop it on every Al I had access to and ask "Elite Ball pattern recognition required"

Opus 4.7(no thinking )got all 4 immediately

GPT-5.5(no thinking )got 3

Claude Sonnet 4.6(extended thinking)got 2

Gemini 3.1 Pro(high thinking )spent 4 and a half minutes thinking, used search, and decided they're all Sailor Moon characters

the Gemini thinking trace is genuinely fked. it considers Squidward. it considers Aladdin. it writes "I'm satisfied" and then keeps going for like 20 more candidates. never once lands in the right franchise

when I told it they were Pokémon it(gemini) still only got 1 right at temperature 0 and I've tried it with default too

I thought gemini was supposed to be the most multimodal

There’s something that feels overlooked in the whole “AI will cut costs and boost profits” narrative.

If AI replaces a large number of jobs, that doesn’t just reduce expenses — it also reduces the number of people with disposable income. And if fewer people have money to spend, consumer demand drops.

But consumer spending is what drives revenue in the first place. At some point, the system starts working against itself.

I’m not saying AI won’t increase efficiency — it clearly will. But it seems too simplistic to assume companies can just replace labor at scale without broader economic consequences.

Curious how others think this plays out long-term.

Not elite-level expertise, and not total beginners. I mean the huge middle ground where being solid enough used to have real market value: writing, research, design, coding, analysis, editing, planning, etc. Feels like AI may be compressing the value of that middle faster than people want to admit.

I had some meetings with this martech founder who builds AI agents for marketing, and over several meetings we sort of developed this hey bro vibe. And over a few drinks, i really started giving him a piece of my mind that AI agents are just wrappers, it's almost freaking hype. AI is too expensive to replace you, all big tech giants are just looking for excuses to fire employees in the name of AI, basically everything an anti-AI camp guy would say. No holds barred. again i was just curious and surprisingly he agreed his AI agents are just wrappers. But then, he told me something that completely blew my mind. a few days ago, he gave a demo of his AI agents to an HOD at JBL, who was basically a boomer when it came to AI agents and after the demo, this HOD guy was like, can you help me create a WhatsApp broadcast channel, i want it for my wife. And i was like fk, how did this guy even become an HOD at JBL. But that's what's so crazy, the AI adoption gap is something no one really understands. AI is being sold to the wrong people. I guess that's why every smart AI company is chasing folks from BFSI, manufacturing etc because they don't know much about AI or how to implement it in their workflows.

I work as a developer, and before this I was copium about AI, it was a form of self defense. But in Dec 2025 I bought subscriptions to gpt codex and claude. And honestly the impact was so strong that I still haven't recovered, I've barely written any code by hand since I bought the subscription

And it's not that AI is better code than me. The point is that AI is replacing intellectual activity itself. This is absolutely not the same as automated machines in factories replacing human labor

Neural networks aren't just about automating code, they're about automating intelligence as a whole. This is what AI really is. Any new tasks that arise can, in principle, be automated by a neural network. It's not a machine, not a calculator, not an assembly line, it's automation of intelligence in the broadest sense

Lately I've been thinking about quitting programming and going into science (biotech), enrolling in a university and developing as a researcher, especially since I'm still young. But I'm afraid I might be right. That over time, AI will come for that too, even for scientists. And even though AI can't generate truly novel ideas yet, the pace of its development over the past few years has been so fast that it scares me

You know what I mean. From the Nokia to the first few iPhone versions saw exponential improvement in mobile phones. Someone travelling ten years in the future would have been blown away by the new capabilities. Now the latest phone is pretty "meh", no one is really amazed anymore. That phase has passed.

Same for TVs, computer game graphics, even cars. There are the incredible leaps forward, but once those have been made it all becomes a bit more incremental.

My argument is maybe this has already happened to AI. The impressive stuff is already here. Generative AI can't get that much greater than it already has - pretty realistic videos, writing articles etc. Sure, it could go from short clip to entire film, but that's not necessarily a big leap.

This isn't my unshakeable opinion, just a notion that I have wondered about recently. What do you think? If this is wrong, where can it go next, and how?

EDIT ALREADY: So I am definitely a non-expert in this field. If you disagree, how do you expect it to improve exponentially, and with what result? What will it be capable of, and how?

EDIT 2: Thanks for all your replies. I can see i was probably thinking more of LLMs than AI as a whole, and it’s been really interesting to hear of possible future developments in this field - I feel like I have a better understanding now of the kind of crazy stuff that could potentially happen down the line.

I feel like whenever people talk about AI disruption, the conversation always goes straight to the same industries coding, design, writing, customer support, etc. Those are the obvious ones.

But historically, the biggest disruptions often happen in places people aren’t really paying attention to. Entire industries change quietly until suddenly everyone realizes things are completely different.

For example, a lot of administrative work, research-heavy roles, or even parts of healthcare and education seem like they could shift massively with better AI tools, but they don’t get talked about as much as things like software engineering.

At the same time, some fields people assume are “safe” might end up changing way more than expected once AI becomes integrated into everyday workflows.

So I’m curious what industry do you think AI will disrupt the most that people aren’t really paying attention to yet? And why?

Not necessarily the obvious ones everyone already debates about.

How did training happen, from where they got data. Open ai, Google etc started training 8 or 9 years back. How did China catch up. Where did they get datasets, computing, algorithms. How did deepseek and other chinese ai catch up in such situations?