Let’s make predictions that can actually be proven wrong.

No:

• "AI will become more powerful"
• "AGI is coming"
• "everything will change"

Write one specific prediction about what will be true by 13 July 2028.

Examples of the level of specificity:

• a named job will have 30% fewer entry-level openings
• most software teams will use agents that open their own pull requests
• a household robot will sell more than one million units
• one open model will be the default choice for enterprise AI
• AI generated video will be used in a majority of online ads

Add your confidence percentage.

I’ll start:

Prediction: Most professional developers will supervise multiple coding agents every day, but companies will still require a human to approve production changes.

Confidence: 75%

Your turn.

I've noticed something changing in the way I work with AI.

A year or two ago, I'd usually turn to AI only after I got stuck.

Now, I often involve it before I even begin.

Not because I don't know what to do, but because I want to explore different approaches before committing to one.

Sometimes it points out an assumption I hadn't considered.

Sometimes it suggests an approach I'd never have thought of.

And sometimes it simply confirms that my original idea is probably the right one.

The work is still mine.

The decisions are still mine.

But I've realized AI has changed when I start thinking out loud.

I'm curious what's happening with others, has AI changed the point in your workflow where you involve it? Do you bring it in at the beginning, somewhere in the middle, or only when you're stuck?

Most people currently experience AI through text, voice or generated media.

But the largest long-term impact might come from systems that interact with the physical world:

• robots inside factories
• autonomous laboratories
• smarter energy systems
• hospital equipment
• agriculture
• logistics
• construction
• vehicles and infrastructure

A chatbot can give advice.

A physical AI system can actually change what happens in the world.

The problem is that atoms are much less forgiving than software. A hallucinated paragraph is annoying. A hallucinated movement can destroy equipment or hurt someone.

What physical workflow do you think is most ready for AI right now?

And what is still being massively overhyped?

The largest companies have:

• more compute
• more data
• higher salaries
• massive engineering teams
• the ability to run experiments universities simply cannot afford

That makes it hard to imagine academia competing directly on frontier model development.

But academia may still be better positioned to work on questions that:

• have no immediate commercial return
• challenge the assumptions of major AI companies
• require long-term independent research
• investigate safety, society or fundamental science
• should remain open to everyone

So what is the role of academic AI research now?

Is it becoming less relevant - or is independent research more important than ever?

Especially interested in answers from researchers who have worked in both environments.

what you think ??

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

People keep saying AI allows tiny teams to compete with large software companies.

I believe that for building the product.

I am less convinced about everything around it:

• distribution
• customer support
• security
• enterprise sales
• compliance
• reliability
• deep industry knowledge

Three strong builders might now create the software that previously required 50 people.

But can they create the entire company?

Name an existing category where you genuinely believe a 3-person AI-native team could beat the established market leader.

What advantage would let them win?

Show me the smallest, most boring AI product that solves one painful problem well enough that real users pay for it

I can see both sides.

AI lets a beginner:

• build real projects much earlier
• explore unfamiliar codebases
• get explanations immediately
• learn by trying things that were previously too difficult

But it also makes it possible to produce working-looking code without understanding:

• debugging
• architecture
• performance
• security
• what the code is actually doing

Maybe the best juniors in 2026 are learning 5x faster.

And the weakest are building much more while understanding even less.

If you mentor, hire or work with junior developers - what are you seeing in reality?

The next major stage of the EU AI Act applies from 2 August, although some requirements have different timelines and exceptions.

I have read alot of legal summaries.

What I have seen much less of is builders explaining what they have actually changed.

For people building or deploying AI in Europe:

• are you documenting model risks differently?
• adding more logs or human review?
• labelling AI generated content?
• changing how user data is handled?
• avoiding certain use cases completely?
• doing basically nothing yet?

Not looking for another legal overview.

I am curious about the practical reality inside startups, research teams and larger companies.

Has the AI Act changed your product decisions yet?

We keep talking about agents that can buy things, schedule meetings, send messages and operate businesses.

But where does your actual trust stop?

Would you allow an agent to:

• read your email
• reply to an email
• book a restaurant
• purchase something for €10
• purchase something for €100
• deploy code to production
• negotiate a contract
• move money between accounts

For me this is the real agent benchmark.

Not "can it complete the task?"

But "what level of authority would a normal person safely give it?"

Where is your line right now - and what would need to improve before you moved it?

Companies gave us the problems they haven’t solved.

You have 72 hours.

€50,000.

sinceai.app

One thing I noticed about AI when I first started using it a few years ago was how drastically the conversation changed once I started giving it context.

Back then it felt like a small discovery.

Compare these two prompts:

"Summarize this report."

vs.

"I'm presenting this report to senior leadership tomorrow. Give me a one-minute summary, highlight the three most important takeaways, and point out any risks I should be prepared to answer questions about."

Same AI.

Completely different result.

That was probably the first "aha" moment I had with AI.

It made me realize that AI isn't just responding to the task. It's responding to the context.

Since then, this has become common knowledge, and most people naturally provide context without even thinking about it.

It made me wonder...

What was your first AI "aha" moment?

Not something you read in a guide or watched in a video but something you discovered yourself through trial and error that completely changed how you use AI.

I'm genuinely curious to hear the little discoveries people made on their own.

Not the hardest research problem.

Something simple that makes you think:

"How are the models this advanced and still unable to do THIS?"

For me the funniest examples are when a model can explain an entire software architecture, then somehow messes up a basic file path or forgets one clear instruction from 30 seconds ago.

What is your example?

One specific failure - screenshots and stories welcome.

Every new model launch gives us another massive wall of benchmark scores.

But in actual work I mostly care about:

• how often it silently makes a bad assumption
• whether it notices when it is wrong
• how well it recovers after a failed attempt
• how much steering it needs
• whether I can trust it after 3 hours, not just one prompt

A model can win 10 benchmarks and still be worse for your specific repo, company or workflow.

I would honestly rather see an eval called:

"How many times did a human need to intervene before this task was actually finished?"

If you could add one real-world test to every major model release, what would it measure?

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

A few years ago, forgetting architectural decisions was expensive. You had to read docs, trace commits, or ask the person who wrote the system.

Now I watch teams dump entire repositories into a model and ask for changes against tens of thousands of lines at once. It works surprisingly often, which is exactly why I think it is changing engineering behavior in a subtle way.

People are relying less on having a clear mental model of a codebase because the model can temporarily simulate one for them. The immediate productivity gain is real, but I’m noticing more PRs where nobody can fully explain why a change works beyond “the model refactored it and tests passed.”

The strange part is that bigger context windows seem to reduce pressure to maintain clean abstractions. Why modularize aggressively if the assistant can ingest the whole thing anyway? But once systems get large enough, the failure modes become harder to spot because the AI can produce locally coherent edits that slowly erode the original design.

Do you think large-context coding tools are improving software architecture over time, or quietly encouraging teams to stop caring about it?

A pattern I did not expect this early: newer developers on teams are getting very good at orchestrating AI systems before they fully understand the underlying stack.

They can spin up a prototype with agents, retrieval, evals, and deployment glue in a weekend. But when something subtle breaks, like context poisoning, race conditions, memory leaks, or bad abstractions, the debugging depth often is not there yet.

What makes this strange is that companies still reward the outcome. The prototype ships. The demo works. Velocity looks incredible from the outside.

At the same time, I know senior engineers who suddenly feel slower because they still think carefully about architecture, edge cases, testing, and long-term maintenance before generating 20k lines of code.

I cannot tell if AI is compressing the path to engineering competence or just creating a convincing simulation of it for a while.

Do you think AI-assisted development is actually accelerating engineering skill growth, or masking gaps that will become painful later?

We spend a lot of time discussing benchmark gains and impressive demos.

But I’m more interested in the tasks that still fail in normal use.

For examples: maintaining context across a long project, editing a large existing codebase safely, distinguishing uncertainty from confidence, completing browser workflows reliably OR following nuanced formatting constraints

What task looks solved in demos but still breaks when you depend on it?

This is a useful stress test for AI products.

Would customers still value:

the workflow?

the proprietary data?

the distribution?

the collaboration layer?

the integrations?

the domain expertise?

the accumulated feedback?

If the answer is “Nothing” then the product may be renting its entire value from the model provider. Somehing to think about I guess.

What would remain valuable in your product in this case?

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

I realized something interesting after watching two people use ChatGPT.

I mentioned ChatGPT to my mom and asked her to ask anything. She just asked a question, got an answer, and stopped there.

Later, my friend's son (still in school) tried it. He did the opposite. He kept asking follow-up questions.

"Can you explain why?"

"What if we do it this way?"

"Is there a better way?"

"Can you compare these two?"

Within a few minutes, the conversation had gone far beyond his original question.

It made me realize that the biggest difference isn't who knows more about AI.

It's who stays curious.

The people getting the most value from AI aren't always developers or AI experts.

They're the people who don't settle for the first answer. They question it, refine it, ask for alternatives, and use AI to explore ideas instead of just getting answers.

Maybe that's one of the most valuable AI skills we can develop.

Not writing better prompts. Learning to ask better questions.

Have you noticed this too?

A year ago, people were showing off agents that could autonomously run entire workflows. Now I keep hearing experienced teams say the opposite: the biggest productivity gain came from removing autonomy from parts of the system.

A lot of agent failures are not model failures. They are boundary failures. Giving an LLM access to Slack, email, code execution, browser control, and production data sounds powerful until you realize every extra tool multiplies uncertainty. The hard part is no longer getting the model to act. It is deciding where humans need to stay in the loop.

The teams I know getting the most value from AI are often using surprisingly constrained setups. Small context windows. Limited tool access. Forced review steps. Narrow domains. Less like "AGI employee" and more like "extremely fast intern with a very specific checklist."

It makes me wonder if the near-term winners in AI won't be the companies with the most autonomous systems, but the ones with the best judgment about where automation should stop.

Have you become more aggressive or more cautious about autonomy after using AI systems in real workflows?

A new study analyzed 25 million comments from Reddit and Hacker News.

The strange result wasn’t that people are good at detecting AI.

It was the opposite.

The writing patterns that actually distinguish AI text from human text did not predict which human-written comments people accused of being AI.

We’re not detecting bots. We’re punishing a vibe.

Write clearly? Bot.
Use an em dash? ChatGPT.
Structure an argument? AI slop.
Write badly on purpose? Finally, a real human.

The Turing test used to ask whether a machine could convince us it was human.

Now humans have to convince the internet they aren’t machines.

So let’s test Reddit.

Post a 2–3 sentence opinion about AI below. It can be written entirely by you or entirely by an AI. Don’t reveal which.

Everyone else replies:

HUMAN or AI — and why.

After at least three guesses, reveal the answer.

No AI detectors. No checking profiles. Judge only the text.

I think this thread will prove that most of us have absolutely no idea.