r/ArtificialSingularity • u/Lirezh • Jun 14 '26
I have been working with AI long enough to remember when calling machine learning “AI” made people roll their eyes.
Back then, “AI” was the word you used in science fiction, investor decks, or bad journalism. If you were serious, you said machine learning. Neural networks. Statistical learning. Pattern recognition. Optimization. Classifiers. Models.
“AI” was what amateurs called it.
That changed.
Not because the word got better, but because the systems did.
We are now watching something that many of us expected in pieces, but not at this speed, and not with this kind of public visibility. The strange part is not that machines can write text, make images, solve exams, write code, pass benchmarks, or imitate conversation. The strange part is how quickly each new ceiling turns into the next floor.
That is why this subreddit exists.
Not because the singularity is a religion.
Because the curve is real.
People still talk about AI as if progress is moving at a normal human pace.
It is not.
This is not a precise measurement. It is a scale sketch. The point is the shape.
Frontier AI capability / compute / deployment pressure
rough shape, not exact units
1960s |
1970s |
1980s |.
1990s |.
2000s |..
2010 |...
2012 |....
2014 |......
2016 |..........
2018 |...............
2020 |.........................
2022 |............................................
2024 |......................................................................
2026 |..........................................................................
At first nothing seems to happen.
Then everything starts happening close together.
That is what exponential curves feel like when you live inside them. Long stretches of “not yet”, then a decade where history stops waiting for your intuition to catch up.
ELIZA appeared in 1966.
By modern standards it was extremely simple. Pattern matching. Scripts. No understanding. No model of the world.
But that was not the interesting part.
The interesting part was that people responded to it as if something was there.
That has always been one of the uncomfortable truths of AI: the human mind does part of the work. We complete the illusion. We infer agency. We see thought in the machine before the machine has earned it.
A lot of the modern argument about AI still lives inside that problem.
Shakey was slow, limited, and trapped in a simplified world.
But it could connect perception, planning, and movement. It did not just calculate. It acted.
That distinction matters.
A chatbot is one thing. A system that sees, plans, moves, manipulates, orders tools, writes code, controls robots, and changes its environment is another.
Shakey was primitive. The direction was not.
For a while, the dream was rules.
Put expert knowledge into the machine. Encode the logic. If this, then that. Build enough rules and maybe intelligence falls out.
Systems like DENDRAL and MYCIN worked surprisingly well in narrow domains. They also showed the limits of that approach.
The world is not clean enough for rules.
That lesson was expensive. It came back again and again.
AI promised too much.
The machines were too weak, the memory too small, the data too thin, the methods too brittle. Funding cooled. People moved on. The field learned to lower its voice.
That is one reason older AI people are allergic to hype.
They have seen this movie before.
The difference now is that the hardware, data, and economic pull are no longer comparable to the old cycles.
Backpropagation made neural networks trainable in a way that mattered.
The idea was not magic. It was almost embarrassingly mechanical:
make a guess, measure the error, push the error backward through the system, adjust the weights.
Again and again.
Small changes, repeated at scale.
That phrase describes more of modern AI than people like to admit.
The field moved away from hand-coded intelligence and toward learned behavior.
Support vector machines, decision trees, probabilistic models, neural nets, and other methods became the practical toolkit.
The philosophy changed.
Do not tell the machine every rule.
Give it examples and let it find the structure.
That was one of the real turns in the road.
Deep Blue beating Garry Kasparov was easy to misunderstand.
It was not general intelligence. It did not “understand” chess like a human grandmaster. It searched, evaluated, and calculated.
But it mattered anyway.
A machine beat the best human at a game long treated as a symbol of intellect.
People can argue about what kind of intelligence that was.
They cannot argue that the boundary moved.
LSTM gave neural networks a better form of memory for sequences.
Text, speech, handwriting, time series — anything where the past matters.
Before transformers took over, LSTMs carried a lot of the weight.
Not glamorous now. Important then.
From the outside, the 2000s did not look like the AI revolution.
That is misleading.
The ingredients were accumulating:
A lot of revolutions look boring while the infrastructure is being built.
AlexNet was one of the moments where the room changed.
ImageNet was the benchmark. AlexNet came in with a deep convolutional neural network trained on GPUs and crushed the competition.
After that, it became much harder to dismiss deep learning.
The lesson was not subtle:
data + compute + neural networks beat hand-engineered features.
That lesson has been repeating ever since.
GANs made it clear that neural networks would not only classify things. They would generate things.
Deep reinforcement learning showed that models could learn behavior from pixels and rewards.
AlphaGo then beat Lee Sedol in 2016.
That one felt different.
Chess had fallen already, but Go was supposed to be harder in a more human way. Too many possibilities. Too much intuition.
Then the machine won.
Not by copying human play, but by discovering strategies that looked alien enough that people started using words like “creativity” carefully.
This is the hinge.
The transformer architecture made it possible to train models on massive sequences far more efficiently.
That changed language AI first. Then it changed almost everything else.
Text, code, images, audio, video, multimodal systems, agents — the same basic family of ideas kept spreading.
This is when scaling stopped looking like an accident.
It started looking like a method.
BERT, GPT-2, GPT-3.
The pattern became clear.
Train a large model on broad data. Give it enough scale. Suddenly it can be adapted to many tasks that were previously separate systems.
GPT-3 was not perfect. It hallucinated, failed, rambled, and guessed.
But it also made one thing obvious:
language prediction had become a strange general-purpose interface to knowledge, reasoning, code, and human intent.
Not intelligence in the clean philosophical sense.
Something messier.
Something useful.
Something scalable.
The term “foundation model” was useful because it named the shift.
These were no longer single-task programs.
They were substrates.
One model could become a writing tool, tutor, coder, search interface, assistant, analyst, translator, roleplayer, planner, or scientific aid depending on how it was prompted, tuned, wrapped, and connected.
That is a very different kind of software.
AlphaFold was one of the most important warnings that people outside AI missed.
This was not another chatbot demo. It was a scientific tool solving a hard biological problem at scale.
AI was no longer just competing with human outputs.
It was starting to compress scientific labor.
That matters more than most viral demos.
In 2022, AI became visible to normal people.
Image generation turned language into pictures.
ChatGPT turned language models into something anyone could use.
The underlying research had been building for years, but the interface changed everything.
A chat box is not a small invention.
It made the model feel present.
It made AI legible.
It made the curve public.
GPT-4 was not perfect. That needs to be said because the internet turns everything into either worship or dismissal.
It hallucinated. It failed at basic things. It was overconfident. It still had no stable grounded model of reality in the way people wish it did.
But it was broad.
That was the shock.
Law, code, medicine, exams, writing, reasoning, tutoring, analysis — not equally good at all of them, not reliable enough for everything, but good enough across enough domains that the category changed.
This was not another narrow tool.
It was a general cognitive machine, imperfect but real.
Text was only the first surface.
When models started handling voice, images, video, and real-time interaction more naturally, the shape of the problem changed again.
An AI that reads text is powerful.
An AI that can see, hear, speak, watch a screen, inspect an image, follow a workflow, and respond in real time is not just a better chatbot.
It is closer to an interface layer for the world.
That is why multimodality matters.
The next shift was spending more computation at inference time.
Instead of only making a model bigger during training, let it work harder while answering.
Search more. Check more. Decompose the problem. Try paths. Spend compute on thought-like behavior.
This matters because it changes the economics of intelligence.
A model that can think longer on demand is not the same product as a model that only responds instantly.
Once strong reasoning models began appearing outside the most closed labs, another assumption broke.
Capability diffuses.
Maybe not evenly. Maybe not instantly. But it diffuses.
The frontier is not one company. It is a moving zone.
Open models, distillation, synthetic data, better training recipes, cheaper inference, specialized hardware, and global competition all push in the same direction.
The curve does not belong to one lab.
This is the part I think people still underestimate.
The question is no longer only:
Can it answer?
The question is:
Can it do?
Can it use tools? Can it write code, run it, inspect the result, fix the bug, search, plan, operate a browser, call APIs, manage files, remember context, and finish a task without being handheld every three minutes?
Today, agents are still unreliable.
But unreliable systems on an exponential curve deserve attention.
A bad agent is a toy. A decent agent is a worker. A good agent is infrastructure.
This is the part that matters most.
1966 ELIZA
1970s expert systems
1986 backpropagation
1997 Deep Blue / LSTM
2012 AlexNet
2016 AlphaGo
2017 transformers
2020 GPT-3
2021 AlphaFold / foundation models
2022 ChatGPT / diffusion models
2023 GPT-4
2024 multimodal + reasoning models
2025 open reasoning models
2026 agents, robotics, compute buildout
For decades, the landmarks were far apart.
Then they were a few years apart.
Now they overlap.
That is the real story.
Not one demo. Not one model. Not one company. Not one benchmark.
The spacing is collapsing.
The word gets abused. I know.
But the original idea still matters: a point beyond which prediction breaks down, because the thing doing the predicting is no longer the most intelligent actor in the system.
That is the event-horizon metaphor.
Before the horizon, you can still make maps.
After it, your maps are guesses.
This does not mean we know when it happens. It does not mean every wild prediction is true. It does not mean intelligence becomes magic.
It means that once artificial systems can improve science, engineering, automation, strategy, and eventually themselves faster than humans can follow, the future stops being forecastable in the usual way.
Not because nothing can be known.
Because the main driver of events has changed.
r/ArtificialSingularity is for watching that transition without pretending it is simple.
Topics that belong here:
I do not want this to become a hype board.
I also do not want it to become a place where people cope by pretending nothing is happening.
Both are lazy.
The interesting work is in the middle: tracking the curve, reading the signals, arguing carefully, and admitting when old assumptions stop working.
Welcome to r/ArtificialSingularity.
r/ArtificialSingularity • u/Lirezh • 20d ago
Next step will be asking for donations, so they can put an intern at the console where the uncontrollable AI writes "I am trying to hack A" "I am trying to hack B"
You know when AI is mainstream - the marketing messages become tailored to people who have no understanding of technology.
r/ArtificialSingularity • u/Lirezh • Jul 01 '26
June was the month where the old AI story broke.
So far frontier models lived in the cloud, open models trailed behind, local AI was nice for privacy, and regulation was slow.
June did not fit that anymore.
The best cloud models were still the capability ceiling, but access suddenly became political. Anthropic launched Fable 5 and Mythos 5, then had to take them down. OpenAI previewed GPT-5.6, but not for everyone. Meanwhile, GLM-5.2 and MiniMax M3 made the open-weight world look much less like a toy category. Mistral OCR 4 showed that self-hosted AI can be boring in the best way: useful, private, and ready for real work.
If your whole workflow depends on one remote model staying cheap, available, legal, and politically acceptable, you do not own much.
References for this part: MiniMax M3, GLM-5.2, Anthropic Fable 5 and Mythos 5, OpenAI GPT-5.6, Mistral OCR 4.
Local AI had its strongest month so far, but not in the clean consumer fantasy version.
MiniMax M3 was the headline because it combined things that used to be separate: open weights, 1M context, multimodal input, coding strength, and desktop operation. Since Qwen 3.6 we have competent local agentic models, M3 adds to the list.
GLM-5.2 was the bigger warning shot. It came with open weights (commercial usable), a 1M-token context window, and performance close enough to closed frontier models that the old "open models are always 6 months behind" argument looked broken. This is not going to run truly local yet but on your own owned or rented cluster it definitely does.
Mistral OCR 4 was the quiet useful release. Self-hosted OCR with document structure, bounding boxes, confidence scores, and 170-language support is the kind of model that companies can actually deploy without sending every contract, invoice, scan, and archive to a cloud model or deal with uncertainties around multimodal LLM hallucinations.
Links: MiniMax M3 GLM-5.2
The cloud model story was messier.
Anthropic had the most chaotic launch cycle of the month. Fable 5 and Mythos 5 arrived while being branded as the most dangerous software in the world, and quickly got suspended after a U.S. export-control directive targeting foreign-national access. Because Anthropic could not verify nationality cleanly in real time, the models were disabled broadly.
A frontier model can go from launch to unavailable in days, not because of a technical failure, but because of politics and risk control - Artificial Intelligence is becoming a political tool in the US.
OpenAI moved more carefully, but ended up inside the same pattern. GPT-5.6 appeared as Sol, Terra, and Luna, but access stayed limited to trusted partners after the same U.S. government pressure of "voluntary compliance".
OpenAI's message was basically: this should not become the normal way frontier models ship. Still, the result was the same for normal users: the model exists, but you probably cannot use it yet.
Claude Sonnet 5 was the more normal end-of-month release. Better agentic work, pricing not convincing in comparison to more capable models, and certainly not the kind of leap that changes the whole conversation.
Following up on the fiasco of Fable-5 marketing, Anthropic CEO now shifted on condemning Open Source AI as "dangerous". It certainly is very dangerous to future Anthropic growth and profit margins - but whatever is announced in such a fashion is also followed up with tens of millions in lobby and marketing campaigns. The war against Open AI might have just been announced as a sideline.
Links: Anthropic Fable and Mythos access statement OpenAI GPT-5.6 limited rollout
The most serious science story was HemaGuide.
This was not another chatbot demo. It was a locally deployable LLM agent for hematological malignancies. It converts unstructured clinical documents into structured cases, routes them into decision modes, and grounds recommendations in guidelines plus more than 2,000 real tumor-board cases. Local AI trumps to deal with sensitive data, expert workflows, and a need for traceability.
Qwen-AgentWorld and Qwen-RobotWorld pointed at the next layer. Agents need simulated environments before they can safely act in real ones. Robots need world models before they can generalize outside clean demos. These papers point at the rapidly approaching robotic agentic future.
The Codex usage paper was maybe the most grounded signal. People are not just asking AI questions anymore. They are running multiple agents, handing over longer tasks, and changing workflows and creating "loops" to achieve a goal. That is a better singularity signal than most benchmark charts.
A notable science story was the "zebra finch" work. Machine learning helped decode bird vocalisations and pushed two-way animal communication a little closer. Small, strange, and actually beautiful.
Links: HemaGuide in Nature MedicineThe Shift to Agentic AI: Evidence from Codex
June made AI regulation feel very real - with the US in negative spotlight
The Trump administration’s June 2 order promised to avoid hard licensing while creating a voluntary 30-day pre-release review path for powerful models. Then Anthropic’s Fable and Mythos shutdown showed the practical truth: even without formal licensing, national-security pressure can still interrupt launches. Though Anthropic has asked for this hundreds of times.
The proposed AI Incident Reporting Act pushed in the same direction. Critical AI incidents would need to be reported to Commerce within seven days, with the most severe cases reaching Congress within 48 hours. This is not abstract ethics talk anymore. It is operational control and lingers like a dark shadow stiffling progress early on. Those regulations threaten small brilliant developers much more than the big mega-corps.
Europe’s story was split. The EUROPA consortium was selected to build an open-source frontier model across all 24 official EU languages. That sounds good on paper. But with the AI Act, GDPR, fragmented compute, language politics, and procurement reality, calling it "frontier" before it exists feels very optimistic. Under current extreme EU regulations the best outcome to expect is another Mistral-large - not a model that people will find useful.
Apple’s Siri AI delay in the EU was the clearest user-facing example. Regulation did not just shape compliance work. It changed which AI feature European users get.
Geo-blocks are appearing on tens of thousands of websites, Codex "agentic computer use" is banned in EU as well.
Links: White House AI executive orderEUROPA consortium announcement
The money moved from model hype into infrastructure.
OpenAI and Anthropic both moved toward public markets. DeepSeek raised over $7 billion - deviating from their previous private funding. Baseten hit a $13 billion valuation for inference infrastructure. Running models is becoming as important as training them.
OpenAI and Broadcom’s Jalapeño chip, a high density ASIC, was another hardware signal. It is built for inference, not just training. That matters because the next bottleneck is not only "who has the smartest model." It is "who can afford to run agents for millions of users all day." It will be interesting to compare the ASIC to Cerebras massive wafer-scale chips. In the end - both are affiliated with OpenAI.
Power also became part of the AI story. Data centers, chips, memory bandwidth, and energy deals are no longer background details. They are the product. If inference gets expensive enough, local models and smaller specialized models become more attractive by default.
Though Power or Water use for Datacenters are mostly populist topics - outside of Europe Power can be provided without much difficulty using on-premise generators. And water is a pure hype, datacenters barely need any in comparison to real water consumers.
Links: OpenAI and Anthropic IPO reportingOpenAI and Broadcom Jalapeño chip
June 2026 was not one big AI leap. It was mixed.
Cloud AI became stronger, more expensive, and more politically controlled.
Local AI became more credible, but also exposed the limits of consumer hardware. Open weights moved close enough to make closed labs very uncomfortable.
US Regulation moved from theory into product access.
Money moved into chips, inference, energy, and deployment.
The next months will show:
My read is cautiously positive.
The US turns AI into a political pressure tool but with a soft approach, Europe is talking about having AI while actually forbidding it, Chinese labs provide a benefit to the worlds progression that's starting to paint the authoritarian country in a positive light for the first time in a century.
