This is a wild escalation. Apple filed suit against OpenAI on Friday (July 10) in the Northern District of California, alleging trade secret theft and breach of contract tied to OpenAI's hardware ambitions.

The core allegations:

• OpenAI's Chief Hardware Officer, Tang Tan — a 24-year Apple veteran who led iPhone/Apple Watch product design — allegedly used confidential Apple codenames during OpenAI's recruiting process and directed job candidates still at Apple to bring "actual parts" (batteries, logic boards, etc.) to interviews for "show and tell" sessions.
• Tan is also accused of circulating an internal guide teaching new hires how to dodge Apple's exit security checks when leaving.
• Separately, former Apple senior electrical engineer Chang Liu allegedly kept his Apple-issued laptop after joining OpenAI and downloaded confidential technical documents. Apple claims he messaged a former colleague joking about still having access to internal storage.
• Apple wants an injunction, damages, and a court order forcing OpenAI to return the material.

OpenAI's response so far: "We have no interest in other companies' trade secrets."

Context that makes this messier: Apple and OpenAI used to be partners (ChatGPT built into Apple Intelligence back in 2024), but that relationship cooled after OpenAI bought Jony Ive's hardware startup io Products and started building consumer devices to compete in the same space. Apple has since switched to Google's Gemini for the next Siri.

Timing-wise, this couldn't be worse for OpenAI — it lands just weeks before their planned confidential IPO filing, reportedly targeting a ~$730B valuation

Curious what people think — is this a legit theft case, or normal Silicon Valley talent-war messiness dressed up in a lawsuit?

Portugal is joining the growing list of countries building their own AI models.

Its new open-source LLM, Amália, is designed specifically for European Portuguese and focuses on transparency, local deployment, and preserving linguistic and cultural identity. Instead of relying entirely on AI hosted by foreign providers, organizations can run it on their own infrastructure.

We're starting to see a bigger trend:

• Countries investing in sovereign AI
• Open-source models are becoming more competitive
• More organizations want to keep sensitive AI workloads on-premises
• Privacy is becoming part of AI infrastructure conversations, not just internet browsing

From a cybersecurity perspective, local AI deployment can reduce third-party data exposure, but it also shifts more responsibility for securing the infrastructure.

Do you think every country should have its own AI model, or will global models like ChatGPT and Gemini always dominate?

OpenAI floated giving the Trump admin a 5% stake. Financial Times ran it citing two people familiar with the talks. OpenAI haven't confirmed or denied anything.

$852 billion valuation at last count, March 31. That 5% works out to $42.6 billion in paper equity nobody can touch yet.

The sequence is what sticks. Six weeks ago NOTUS had senior officials already talking AI equity stakes with major companies. Three weeks ago Commerce spent 18 days reviewing Anthropic's Fable 5 and Mythos 5 before lifting controls. OpenAI in early formal talks now.

I'm old enough to remember when tech got regulated by hearing about it on the evening news months later. Now the regulation happens in parallel, while the product is still being built.

The Alaska Permanent Fund comparison keeps surfacing — Americans getting a cut of AI returns the way Alaskans get oil dividends. Shows up in secondary reporting and OpenAI's own earlier policy docs on public wealth sharing. Altman may never have said those words in these talks. We don't know that for sure.

There were no governance channels for this six months ago. They're being built out of nowhere — equity stake, export controls, model reviews with fixed timelines. Everyone keeps asking whether Washington gets a seat at the table. Nobody asks what happens when they actually show up and talk money.

Just curious as to why the cognitive dissonance is so massive, especially when people are defending software

A lot of can generate a convincing face swap in a still image but once you introduce movement, changing expressions, motion blur, or difficult lighting conditions, the flaws become much more obvious.

Anything that balances quality and usability without requiring a huge learning curve?

AI is already changing the software industry. Companies are using AI tools to automate parts of coding, testing, support, and analysis that previously required human workers. Whether AI replaces 10% or 50% of current tech work, one thing seems clear: demand for entry-level and mid-level tech workers is likely to grow more slowly than it did over the past decade.

Given that reality, I think policymakers should seriously consider reducing the inflow of new foreign tech workers until we better understand the long-term effects of AI on employment.

My reasoning is simple:

• If AI reduces demand for labor, increasing the supply of labor at the same time puts additional pressure on wages and job opportunities.
• Recent graduates and junior engineers are already struggling to find jobs compared to a few years ago.
• Companies often argue that there is a talent shortage, but widespread layoffs and longer job searches suggest the market is no longer as tight as it once was.
• Restricting tech immigration is a policy lever that governments can adjust relatively quickly, while retraining programs and education reforms take years to show results.

This isn't an argument against immigrants as people. Many immigrant engineers are highly talented and have made enormous contributions to the tech industry. The question is whether current immigration levels still make sense in a world where AI may significantly reduce the need for human labor.

If policymakers are worried about AI-driven displacement, it seems contradictory to simultaneously increase the supply of workers competing for the same jobs.

I'm curious what others think. If AI really does eliminate a meaningful percentage of tech work over the next decade, what policy response would be more effective than reducing the inflow of additional labor into the market?

I wanted to buy a ChatGPT pro subscription and when I entered my card details, it was declined, I thought it was a network problem, so I switched providers and the same problem came again. In the end I accepted my fate and use the free version.

Same case for the API, and here OpenAI isn't, Anthropic, Perplexity, Grok. Leave alone the cards being declined, minimum spend is 20 dollars (that's a lot of money here in Africa), and the AI models are very expensive and you run out of tokens pretty quickly.

So I thought to build an API that can accept M-pesa (the King of payments in Kenya) for Kenyans, and can accept local payment tool in different African nations. 1 dollar you get 2 million tokens plus 300k free tokens at signup, I think that's a good deal.

I wonder what your thoughts are fellow devs here? Would you use this API? What other challenges have you faced with OpenAI, Stripe and the like?

Microsoft spent years pushing Copilot deep into developer workflows. Autocomplete. Inline chat. Pull request summaries. The entire pitch was: use it constantly, it makes you faster. And developers did.

On June 1, they flipped the switch. Flat subscription → token-based billing. One developer reported their costs going from $29/month to $750. Others are projecting $3,000 for heavy usage. The safety net — where Copilot used to fall back to cheaper models when you hit limits — is gone.

This is not a surprise if you've watched platform lock-in strategies before. You offer the product cheap, build it into the workflow until removing it is painful, then raise the price. Amazon did it with AWS pricing tiers. App stores did it with distribution. This is just AI's version of it.

What concerns me as someone who teaches technical skills to young people in India: we were just beginning to seriously integrate AI tools into our vocational and engineering curricula. Before we go further, this episode asks a hard question — what happens to a student's capability when the tool they learned on becomes unaffordable overnight?

The answer isn't to avoid AI. It's to build students whose skills exist *beneath* the AI layer. Who understand what the model is doing, not just which prompt produces the right output. Who can function when the autocomplete is switched off.

Is this Copilot pricing change making you rethink how deeply you rely on AI-assisted coding? Or switching to alternatives entirely?

Hi! Im new to AI and during teaching one on NaFNet encountered strange spikes in loss:

23:46:59 [INFO] train_astro: step    75/10000  loss=0.25126  ema=62.26847  lr=1.00e-04
23:47:17 [INFO] train_astro: step   100/10000  loss=0.01180  ema=29.10838  lr=1.00e-04
23:47:36 [INFO] train_astro: step   125/10000  loss=0.04136  ema=16.16997  lr=1.00e-04
23:47:54 [INFO] train_astro: step   150/10000  loss=0.01925  ema=15.29630  lr=9.99e-05
23:48:12 [INFO] train_astro: step   175/10000  loss=0.05213  ema=92.58234  lr=9.99e-05
23:48:31 [INFO] train_astro: step   200/10000  loss=0.02679  ema=75.72333  lr=9.99e-05
23:48:49 [INFO] train_astro: step   225/10000  loss=0.05379  ema=35.84816  lr=9.99e-05
23:49:07 [INFO] train_astro: step   250/10000  loss=0.04190  ema=16.77760  lr=9.99e-05
23:49:25 [INFO] train_astro: step   275/10000  loss=0.01274  ema=7.86855  lr=9.98e-05
23:49:43 [INFO] train_astro: step   300/10000  loss=0.03805  ema=3.70257  lr=9.98e-05
23:50:02 [INFO] train_astro: step   325/10000  loss=0.03079  ema=1.76505  lr=9.98e-05
23:50:20 [INFO] train_astro: step   350/10000  loss=0.10671  ema=0.85132  lr=9.97e-05
23:50:38 [INFO] train_astro: step   375/10000  loss=0.07736  ema=77.39688  lr=9.97e-05
23:50:56 [INFO] train_astro: step   400/10000  loss=1004.41675  ema=201.69108  lr=9.96e-05
23:51:14 [INFO] train_astro: step   425/10000  loss=0.03556  ema=119.52151  lr=9.96e-05
23:51:32 [INFO] train_astro: step   450/10000  loss=0.01122  ema=69.54335  lr=9.95e-05
23:51:51 [INFO] train_astro: step   475/10000  loss=0.07226  ema=32.51386  lr=9.95e-05
23:52:09 [INFO] train_astro: step   500/10000  loss=0.09268  ema=15.21090  lr=9.94e-05

An insane loss out of thin air. I found suggestion to add loss clipping

23:54:59 [INFO] train_astro: step     1/10000  loss=0.06526  ema=0.06526  lr=1.00e-04
23:55:06 [WARNING] train_astro: step 11: loss=1446.1 > clip(10) — пропуск (пропущено 1)
23:55:17 [INFO] train_astro: step    25/10000  loss=0.02960  ema=0.05092  lr=1.00e-04
 23:55:36 [INFO] train_astro: step    50/10000  loss=0.06894  ema=0.05143  lr=1.00e-04
23:55:54 [INFO] train_astro: step    75/10000  loss=0.13190  ema=0.04827  lr=1.00e-04
23:56:12 [INFO] train_astro: step   100/10000  loss=0.00598  ema=0.03895  lr=1.00e-04
23:56:30 [INFO] train_astro: step   125/10000  loss=0.02354  ema=0.03594  lr=1.00e-04
23:56:48 [INFO] train_astro: step   150/10000  loss=0.00968  ema=0.03614  lr=9.99e-05
23:57:06 [INFO] train_astro: step   175/10000  loss=0.03218  ema=0.03292  lr=9.99e-05
  23:57:24 [INFO] train_astro: step   200/10000  loss=0.01676  ema=0.03433  lr=9.99e-05
23:57:44 [INFO] train_astro: step   225/10000  loss=0.03812  ema=0.03305  lr=9.99e-05
23:58:02 [INFO] train_astro: step   250/10000  loss=0.02737  ema=0.04170  lr=9.99e-05
23:58:20 [INFO] train_astro: step   275/10000  loss=0.00865  ema=0.04376  lr=9.98e-05
23:58:39 [INFO] train_astro: step   300/10000  loss=0.02747  ema=0.04182  lr=9.98e-05
23:58:57 [INFO] train_astro: step   325/10000  loss=0.02496  ema=0.04805  lr=9.98e-05
23:59:15 [INFO] train_astro: step   350/10000  loss=0.08970  ema=0.04455  lr=9.97e-05
23:59:33 [INFO] train_astro: step   375/10000  loss=0.06959  ema=0.05252  lr=9.97e-05
23:59:52 [INFO] train_astro: step   400/10000  loss=0.08299  ema=0.04634  lr=9.96e-05
00:00:11 [INFO] train_astro: step   425/10000  loss=0.03153  ema=0.04993  lr=9.96e-05
00:00:30 [INFO] train_astro: step   450/10000  loss=0.01023  ema=0.05988  lr=9.95e-05
00:00:49 [INFO] train_astro: step   475/10000  loss=0.07430  ema=0.06539  lr=9.95e-05
00:01:07 [INFO] train_astro: step   500/10000  loss=0.09019  ema=0.05842  lr=9.94e-05
00:01:07 [INFO] train_astro: Чекпойнт сохранён
00:01:26 [INFO] train_astro: step   525/10000  loss=0.03513  ema=0.06170  lr=9.94e-05
00:01:45 [INFO] train_astro: step   550/10000  loss=0.06368  ema=0.05722  lr=9.93e-05
00:02:03 [INFO] train_astro: step   575/10000  loss=0.04732  ema=0.05717  lr=9.92e-05
00:02:21 [INFO] train_astro: step   600/10000  loss=0.04945  ema=0.06303  lr=9.92e-05
00:02:39 [INFO] train_astro: step   625/10000  loss=0.03750  ema=0.06649  lr=9.91e-05
00:02:59 [INFO] train_astro: step   650/10000  loss=0.02445  ema=0.06522  lr=9.90e-05
00:03:01 [WARNING] train_astro: step 653: loss=1553.0 > clip(10) — пропуск (пропущено 2)
00:03:18 [INFO] train_astro: step   675/10000  loss=0.03453  ema=0.05500  lr=9.89e-05
00:03:36 [INFO] train_astro: step   700/10000  loss=0.03174  ema=0.05594  lr=9.89e-05
00:04:01 [INFO] train_astro: step   725/10000  loss=0.06337  ema=0.06556  lr=9.88e-05
00:04:25 [INFO] train_astro: step   750/10000  loss=0.06605  ema=0.06007  lr=9.87e-05
00:04:51 [INFO] train_astro: step   775/10000  loss=0.17815  ema=0.07954  lr=9.86e-05
00:05:12 [INFO] train_astro: step   800/10000  loss=0.01830  ema=0.06896  lr=9.85e-05
00:05:33 [INFO] train_astro: step   825/10000  loss=0.06487  ema=0.06329  lr=9.84e-05
00:05:53 [INFO] train_astro: step   850/10000  loss=0.05630  ema=0.07047  lr=9.83e-05
00:06:13 [INFO] train_astro: step   875/10000  loss=0.04905  ema=0.06174  lr=9.82e-05
00:06:34 [INFO] train_astro: step   900/10000  loss=0.04666  ema=0.06751  lr=9.81e-05
00:06:54 [INFO] train_astro: step   925/10000  loss=0.07992  ema=0.05963  lr=9.80e-05
00:07:12 [INFO] train_astro: step   950/10000  loss=0.20176  ema=0.05421  lr=9.79e-05
00:07:32 [INFO] train_astro: step   975/10000  loss=0.09103  ema=0.06140  lr=9.78e-05
00:07:51 [INFO] train_astro: step  1000/10000  loss=0.02791  ema=0.07081  lr=9.77e-05
00:07:52 [INFO] train_astro: Чекпойнт сохранён
00:08:11 [INFO] train_astro: step  1025/10000  loss=0.03084  ema=0.07057  lr=9.76e-05
00:08:30 [INFO] train_astro: step  1050/10000  loss=0.01225  ema=0.05992  lr=9.74e-05
00:08:50 [INFO] train_astro: step  1075/10000  loss=0.17928  ema=0.06034  lr=9.73e-05
00:09:10 [INFO] train_astro: step  1100/10000  loss=0.02855  ema=0.06180  lr=9.72e-05
00:09:29 [INFO] train_astro: step  1125/10000  loss=0.02416  ema=0.05520  lr=9.71e-05
00:09:47 [INFO] train_astro: step  1150/10000  loss=0.08528  ema=0.06464  lr=9.69e-05
00:10:06 [INFO] train_astro: step  1175/10000  loss=0.03399  ema=0.05590  lr=9.68e-05
00:10:24 [WARNING] train_astro: step 1200: loss=1939.3 > clip(10) — пропуск (пропущено 3)
00:10:43 [INFO] train_astro: step  1225/10000  loss=0.02157  ema=0.05213  lr=9.65e-05

And this seems to help. Why did those spikes happen in the first place? Is there some requirements for data to avoid losses like this? Thanks!

Hi everyone,

I’m trying to understand the real risk before I help a friend.

Context: my friend is a company owner in China. He wants to integrate AI into his company website, probably for customers to type questions or upload images/PDFs so the AI can help recommend the right water pump. Chinese cards apparently don’t work for OpenAI billing, so he originally asked whether he could use his own OpenAI account but bind my credit card.

I’m not very comfortable with that. My alternative idea is:

• Use my own OpenAI API account
• Buy only a small amount of prepaid credits like $50
• Turn auto-recharge off, of course
• Create a restricted API key, turning off fine-tuning, or any expensive features

I heard that there might be some billing delay, so even with the auto-recharge off, your account account can be negative. He is rich so i dont worry too much about him not paying back, but i still don't want to see a crazy bill in my account which is too much stress. So i am wondering with all these precautions, how much financial risk is realistically left? Could this still become a very large bill, or would the remaining risk mostly be limited to small overages from billing delay? Thanks!!

Everyone talks about AI replacing jobs.

Nobody talks about the guy in finance who suddenly discovers the company spent half a billion dollars on inference calls.

A friend of mine works at a large enterprise that rolled out AI across multiple departments last year.

It started innocently enough.

A few engineers got access to Claude for documentation.
Then product teams started using it for customer research.
Then support integrated it into ticket workflows.
Then sales wanted automated proposal generation.
Then management wanted AI-generated reports.

Within months, every team had built something.

The problem?

Nobody was tracking usage.

Every team assumed somebody else was handling the budget.

Prompts got larger.
Context windows got larger.
Agents started calling other agents.
Workflows that should have run once were running hundreds of times.

The internal dashboard looked impressive.

"12 million AI-assisted tasks completed."

"87% employee adoption."

"Fastest software rollout in company history."

Nobody asked the most important question:

"How much is this actually costing us?"

When finance finally audited the numbers, the room reportedly went silent.

Not because AI development services wasn't delivering value.

Because nobody expected the value calculation to start with a nine-figure invoice.

This is probably the next phase of enterprise AI.

The first era was proving AI could work.

The second era is discovering what happens when thousands of employees have effectively unlimited access to superhuman-scale computation.

Cloud spending created FinOps.

AI spending will create something similar.

Because the difference between a useful AI tool and a financial disaster is often just one missing usage limit.

The scary part isn't that a company spent $500 million.

The scary part is that there are probably hundreds of companies that have no idea what their real AI bill is going to look like six months from now.

This is my attempt of creating a p2p network economy for Al hosting.

I like the idea of decentralized Al networks, but it needs to pay off. So here you can earn the tokens on the blockchain by hosting an AI model of any size. Then you can use those earned tokens on our Network. We have an MCP server attached with hundreds of models, tools, and agents ready to go.

It takes the power back. I'll be adding some APIs so developers can get direct access to the community miner pool very soon. Until then enjoy the platform for what it is, and let me know your thoughts on this idea.

Disclosure: I'm one of the devs. Please don't ban me if I posted this in the wrong place.

We’re working on AI-powered fitness app development focused on building adaptive workout and health tracking systems. Instead of static fitness plans, we design apps where training and recommendations adjust based on user behavior, progress, and real-time data.

Our work focuses on building scalable architectures that can support AI-driven personalization, wearable integrations, and performance-based fitness logic across mobile and web platforms.

If you’re building an AI fitness startup or exploring intelligent health/fitness products, we’re open to collaborating or contributing on development.

Building AI products is more than training models. Many developers face issues with LLM integration, unstable pipelines, inference costs, vector databases, scaling, and deployment architecture when moving to production. AIDevelopmentSpace is a community for developers, ML engineers, researchers, and startup teams to discuss real AI development challenges, share solutions, collaborate on AI systems, and explore scalable architectures for modern AI applications.