AGI is hype. Apple just proved it

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Today, we are going to talk about why Apple thinks AGI Is further than we think and more.

In today’s post:

• Apple on AGI birth

• Sundar Pichai rules for thriving in the era of AI

• Meta $10B investment

• UK banks 🤝 NVIDIA

• AI in HR

• Trending AI tools

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AGI

AGI isn’t just around the corner and Apple has the receipts.

While some CEOs claim AGI is just a few years away, Apple researchers are throwing cold water on that optimism. In a recent study, they tested advanced AI models on reasoning tasks and found serious gaps.

• Apple’s new paper, The Illusion of Thinking, challenges the idea that current large reasoning models (LRMs) are on the path to AGI.

• They tested Claude, OpenAI, and DeepSeek chatbots on logic puzzles, going beyond standard math and code benchmarks.

• Result: performance collapsed when task complexity increased. Models lost accuracy and failed to apply consistent logic.

• Surprisingly, “non-thinking” models outperformed “thinking” ones on simpler tasks raising questions about what reasoning in AI really means.

• The study found signs of overthinking: models often got the right answer early, then reasoned themselves into wrong ones.

• Apple’s takeaway: current models mimic reasoning, but don’t internalize it. That’s not AGI, it's pattern matching at scale.

Here’s what I think:

The hype cycle is running hot, but Apple’s research is a reminder to stay grounded. Real reasoning isn’t about outputs, it's about process. Until AI models can reason with consistency and generalize across domains, AGI remains more vision than reality.

Rules for success

Sundar Pichai’s 8 Rules for Thriving With AI

At Bloomberg’s Tech Conference, Google CEO Sundar Pichai didn’t warn about AI, he welcomed it. For him, AI isn’t a threat to tech professionals, it’s a partner. The challenge isn’t surviving the AI wave;  it’s learning how to ride it.

1. Don’t just use AI tools, rather build them. Learn how LLMs work, not just what they do.

2. Anchor your value in what AI can’t do: empathy, ethics, and real-world judgment.

3. Speak the language of machines. Prompt engineering is the new literacy.

4. Treat your career like a product, iterate, adapt, and stay in beta.

5. Become the ethicist in every AI conversation. Ask “should we?” before “can we?”

6. Lead hybrid teams where humans handle strategy and AI handles scale.

7. Share what you’re learning. Visibility builds resilience.

8. Don’t compete with AI. Collaborate with it. That’s where the future lies.

Here’s what I think:

The skills that keep you relevant won’t be about knowing the latest tech, they’ll be about knowing yourself. Adaptability. Awareness. A willingness to rethink how you work. AI isn’t taking your job but it will change what your job looks like. How do you respond? That’s the real test.

Investment

Meta is finally writing a check and it’s a big one

For years, Meta built its AI in-house while rivals like Microsoft and Amazon wrote billion-dollar checks to startups. That era might be over. Meta is now in talks to invest up to $10 billion in Scale AI; a quiet giant powering the generative AI boom.

Here’s what we know so far:

• Scale AI specializes in data labeling; the backbone of AI training and counts OpenAI and Microsoft as clients.

• Meta’s rumored investment would be its largest ever, surpassing its prior M&A and AI spend.

• Scale made $870M last year and expects $2B in revenue for 2025 a sign of just how critical good data has become.

• Meta and Scale are already collaborating on Defense Llama, a military-focused language model built for the U.S. defense sector.

• This investment marks a shift from Meta’s build-it-yourself AI strategy toward deeper external partnerships.

• Defense is a key angle here: both companies are expanding their work with the U.S. military, including VR, AR, and autonomous AI agents.

Here’s my thoughts:

Meta’s move isn’t just about data, it’s about alignment. As AI becomes infrastructure, power will follow those who control the inputs. Scale AI owns the pipeline. If this deal closes, Meta won’t just be investing in a vendor, it’ll be buying leverage.

AI Finance

Nvidia and the UK Are Creating a Sandbox for AI Finance

Britain’s financial regulator is doing something rare: encouraging AI experimentation with guardrails. In a new collaboration with Nvidia, the Financial Conduct Authority is offering banks a safe space to test AI tools without rushing them into production.

The “Supercharged Sandbox” gives banks access to Nvidia’s computing tools, regulatory guidance, and better data. It’s aimed at the earliest phase of AI development, long before tools go live with customers.

Banks have struggled to deploy new AI tech due to fears over privacy breaches, scams, and compliance risks. Generative AI models, like ChatGPT, often send data abroad, raising red flags for data regulators.

The FCA hopes its framework can balance innovation with oversight, supporting both consumer protection and economic growth.Industry insiders say there's still a lot of “success theater” in AI for finance; more buzz than real breakthroughs.

This sandbox isn’t just a technical play, it’s a policy experiment. Regulators are signaling that safe, slow AI adoption is better than flashy demos with legal landmines. If banks want trust, they'll need to build transparency into their models from day one.

Recruitment

Recruitment is changing but AI might not be the upgrade you think

AI has quietly transformed HR, especially hiring. From resume screening to automated summaries, it's supposed to save time and reduce bias. But behind the scenes, the reality is messier, and job seekers are paying attention.

Here’s everything you need to know:

• AI cuts hiring time by 40%, but 66% of adults won’t apply if it’s making the decisions.

• Bias hasn’t gone away; it’s just been outsourced. AI can help with diversity, but only if it’s used intentionally.

• More companies are using AI in hiring and more are abandoning it too. 42% scrapped their AI plans this year.

• Candidates are using AI too: to write resumes, prep for interviews, and even answer questions.

• But many are overwhelmed. Over half worry about AI in the workplace, even if they’re using it themselves.

• Employers often aren’t transparent about how AI is used. Some don’t even know. That disconnect erodes trust.

• Regulation is coming fast, especially in Europe. But the long-term impact of biased training data? Still unknown.

Here’s what I think:

You can’t automate your way out of human problems. AI can speed things up, but it can’t replace judgment, empathy, or context. The best hiring teams won’t just “use” AI, they’ll partner with it, audit it, and most importantly, stay human.

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