We’ve all seen the headlines. “AI is taking over.” “The future is now.” And if you’ve been anywhere near a product roadmap in the past 18 months, odds are someone has scribbled “AI” into a Q3 objective and called it innovation.

But here’s the truth: the AI revolution won’t happen overnight — and that’s not a failure. It’s just how revolutions actually work.

Harvard Business Review just published something that cuts through the noise. In “The AI Revolution Won’t Happen Overnight,” Paul Hlivko—a CIO with three decades of tech implementation experience—delivers a reality check every company chasing AI dreams needs to hear.

His opening assessment of McKinsey’s $17.1–$25.6 trillion prediction? “A seductive vision. It’s also a hallucination.”

That’s not pessimism talking. That’s pattern recognition.

The Same Movie, Different Decade

“I’ve seen this movie before. It rarely ends the way the trailer promises.”

Anyone who’s been in tech long enough knows this feeling. Remember when blockchain was going to revolutionize everything? When VR was going to replace physical reality? When the metaverse was going to be the next internet?

The pattern is always the same: revolutionary technology emerges, consultants create trillion-dollar projections, companies panic about being left behind, massive investments follow, reality hits, and the real value emerges slowly, quietly, in practical applications nobody predicted.

AI is following this exact playbook.

Hype Is Outpacing Reality

Here’s what Hlivko nails: AI capabilities are improving exponentially, but most organizations are adopting it in a straight line—slowly, hesitantly. That gap between what’s possible and what’s practiced? It’s a problem. And it’s widening.

We’ve all seen the slide decks that throw around “AI-powered X” like it’s a checkbox. However, integrating AI is not as simple as flipping a feature flag. It demands rethinking systems, data governance, team dynamics, and customer interactions.

The tech might be magical, but deploying it isn’t.

It’s Not Just About the Tech

There’s a false comfort in treating AI as a tool you install, rather than a force that reshapes how your organization thinks. Culture, training, governance—all of it matters.

You don’t need a CTO with a ChatGPT wrapper. You need people who understand what the model does, what it doesn’t, and how it fits into your product and process ecosystem.

What Actually Works

The companies winning with AI today aren’t chasing trillion-dollar visions. They’re solving specific problems:

• Development teams cutting debugging time by 40% with AI-assisted code review
• Customer service operations handling 3x more inquiries with AI-powered response suggestions
• Marketing teams producing personalized content at an unprecedented scale

These aren’t headline-grabbing use cases. They won’t generate trillion-dollar economic impact projections. But they’re real, measurable, and happening now.

Measured Beats Magical

The revolutionary applications everyone’s predicting—AI running entire businesses, solving world hunger—require infrastructure, regulatory frameworks, and social changes that take decades to develop. However, productivity applications are available today for companies that are smart enough to implement them thoughtfully.

So, before slapping AI on the next sprint board, ask:

• What’s the problem we’re solving?
• How will AI actually help?
• Are we ready to wield this tool with the care it demands?

If the answer’s “maybe,” then good—you’re thinking clearly.

The Three C’s That Actually Matter

Want to change how your organization approaches AI? Focus on three fundamentals:

Culture - Stop treating AI adoption like a technology rollout. It’s an organizational shift. The teams succeeding with AI aren’t just deploying models—they’re building environments where experimentation is safe, failure is learning, and iteration is expected.

Critical Thinking - Question everything. Not just the AI outputs, but the problems you’re trying to solve. Why this use case? Why now? What happens if it doesn’t work? The companies getting AI right are the ones asking hard questions before, during, and after implementation.

Curiosity - The best AI implementations come from teams genuinely curious about what’s possible, not just what’s profitable. They’re exploring edge cases, testing assumptions, and discovering applications nobody planned for. Curiosity drives the experimentation that turns hype into value.

These aren’t soft skills. They’re competitive advantages. The AI revolution is happening. It’s just not happening the way the consultants predicted. It’s happening one practical use case at a time, one efficiency gain at a time, one solved problem at a time.

And it’s happening fastest at companies that prioritize how they think over what they deploy.

The revolution is here. It’s just not turnkey.

A growing consensus among long-time Apple watchers is that something’s broken in Cupertino.

And it’s not just bugs or another “meh” OS update. It’s deeper — a strategic drift, a credibility problem, and a serious lack of AI infrastructure. If Apple wants to stay relevant in the next era of computing, it needs to make bold moves. It needs to fix the foundation and build something real on top of it.

Here’s the move: Apple should acquire Cerebras.

But first, let’s look at where we are — and how we got here.

The Red Flags Are Everywhere

John Gruber’s Something Is Rotten in the State of Cupertino is required reading. He lays out how Apple overpromised a “more personalized Siri” and then quietly admitted that none of it was ready. No demos. No hands-on. Just a flashy WWDC concept video and months of silence. As Gruber puts it, this wasn’t a delay — it was bullshit.

Meanwhile, Timothy R. Butler’s Apple Needs a Snow Sequoia points to something more troubling: Apple’s core platforms feel neglected. Messages that can’t copy text. UI bugs across macOS and iOS. System settings that feel like they were designed blindfolded. It’s not just the future that’s in question — the present is already a mess.

Then comes Rui Carmo’s The Emperor’s New Clothes — a developer’s view from inside the machine. Carmo breaks it down technically: Siri hasn’t meaningfully changed since 2021. Apple Intelligence is more marketing than product. And perhaps most damning, Apple lacks the automation layer and internal architecture to pull any of this off. It’s not just behind on AI — it’s structurally unprepared to catch up.

Enter Cerebras: Proof That Real AI Performance Exists

Now compare all this to what Cerebras demonstrated — not in theory, but in production.

In a recent blog post, Cerebras detailed their work powering Mistral AI’s “Le Chat” assistant — a ChatGPT-style app that now delivers answers at over 1,100 tokens per second. That’s not just competitive. It’s faster than GPT-4o. That’s not running on GPUs. That’s running on Cerebras’ own wafer-scale AI chips — hardware they designed and built for this exact moment in computing.

Let that sink in.

While Apple is releasing concept videos of features it can’t demonstrate, Cerebras is powering production LLMs faster than anyone else. Mistral didn’t need a flashy ad campaign. They needed infrastructure that works. Cerebras delivered, highlighting the stark contrast between Apple’s promises and Cerebras’ actual performance.

This is exactly what Apple is missing: real AI performance, real scale, and real infrastructure.

Why Buying Cerebras Isn’t Optional

Here’s the thing: Apple doesn’t have time to build this from scratch.

Even if Apple fixes its OS bugs and gets its software teams back in shape (which it absolutely should — thanks, Tim Butler), it still doesn’t have the infrastructure to build and run serious models. Apple Silicon is great for phones and laptops. But training foundation models? Or serving inference at scale with real privacy and speed?

That’s not what M-series chips are for. That’s what Cerebras chips are for.

Buying Cerebras gives Apple:

• AI hardware supremacy — with chips designed to train and deploy massive models efficiently.
• End-to-end control — from training on private cloud infrastructure to deploying on-device.
• Credibility — no more vaporware promises. Just working systems, built on real silicon.

And unlike NVIDIA, Cerebras is acquirable. It’s the kind of bold move Apple used to make.

The Playbook Is Obvious

Here’s what Apple should do:

1. Buy Cerebras — make AI infrastructure a first-party capability.
2. Ship a “Snow Sequoia” OS release — fix the fundamentals, clean house.
3. Rebuild Siri from scratch — using actual AI, not glued-together shortcuts and App Intents.
4. Stop pretending — and start delivering.

Do all that, and Apple isn’t playing catch-up anymore. It’s leading again — on its own terms.

Final Thought

Gruber called out the vapor. Butler showed the bloat. Carmo exposed the rot. And Cerebras? They showed what it looks like to ship real AI.

Apple, the clock is ticking.

You don’t need a better keynote. You need Cerebras.

📚 Further Reading

• Something Is Rotten in the State of Cupertino – John Gruber
• Apple Needs a Snow Sequoia – Timothy R. Butler
• The Emperor’s New Clothes – Rui Carmo
• Cerebras + Mistral Le Chat – Real-time AI performance at scale

The promise of generative AI is clear: it can dramatically accelerate how teams work. Yet many companies, from early-stage startups to established enterprises, are struggling to capture this value. While some teams ship new features in days instead of weeks, others spend months debating which AI model to use or navigating approval processes.

The Small Company Paradox

For small companies, the barriers often come down to uncertainty. When you’re running lean, every tool choice matters. Teams can get stuck evaluating endless options, worried about committing to the wrong platform. Meanwhile, without clear guidance, different departments might adopt conflicting tools, creating future technical debt.

This hesitation is particularly ironic because small companies stand to gain the most from AI adoption. A marketing team of two can now produce content at the scale of a much larger organization. A solo developer can generate test cases and documentation that would typically require a dedicated QA team.

The real game-changer? AI lets startups compete directly with industry giants. A small e-commerce store can now generate SEO-optimized product descriptions at the scale of Amazon—without hiring a dedicated content team. A boutique SaaS startup can use GenAI-driven chatbots to provide 24/7 customer support that feels as responsive as a Fortune 500 help desk. These aren’t future possibilities—this is happening today, and the companies that embrace AI early are the ones closing the gap fastest.

Enterprise Gridlock

Large organizations face a different challenge. Their size and regulatory requirements mean any new technology needs extensive vetting. A simple tool adoption that might take days at a startup can stretch into months of security reviews, compliance checks, and stakeholder approvals.

This caution isn’t entirely misplaced. Enterprise data security and regulatory compliance are serious concerns. But many organizations have overcorrected, creating approval processes so stringent that meaningful innovation becomes nearly impossible.

The result? Speed is outsourced to smaller, more agile competitors:

• While one Fortune 500 retailer debates AI-powered customer insights, a smaller competitor deploys it, refining marketing campaigns in real-time and gaining a 20% market advantage
• A legacy software giant spends months getting legal approval for AI-assisted coding, while a new SaaS disruptor ships faster, better-tested features in half the time

By the time slow-moving companies act, they’re not just behind—they’re losing market share, talent, customers, and revenue. AI adoption isn’t a theoretical risk, it’s a business risk. The biggest danger isn’t AI itself—it’s standing still while competitors move forward.

Moving Forward: A Practical Approach

The path to successful AI adoption requires balancing speed with thoughtful implementation. Here’s what works based on real-world observations:

Start with a Clear Use Case

Instead of trying to transform everything at once, pick a specific problem where AI can show immediate value. For example:

• Automating test case generation for developers
• Creating first drafts of marketing copy
• Summarizing customer feedback themes
• Generating product descriptions for e-commerce
• Building automated customer support workflows

Make Smart Decisions Quickly

When evaluating AI tools, focus on these key criteria:

• Solves a real problem – Does this tool address an actual pain point?
• Fits your stack – Can it integrate smoothly without disrupting existing processes?
• ROI-positive – Is the cost justified by efficiency gains or revenue impact?
• Secure & compliant – Does it meet basic security and compliance requirements?

If a tool checks these four boxes, test it. If not, move on. The biggest mistake isn’t choosing the “wrong” AI tool—it’s spending months debating and choosing nothing.

This framework helps cut through analysis paralysis.

Scale What Works

Success with focused pilot projects builds confidence and creates internal advocates. A single successful implementation—whether it’s cutting development time by 30% or doubling content output—provides the evidence needed to expand adoption.

Implementation Strategies

For small companies, the priority should be quick experimentation in areas where resources are stretched thin. A small marketing team might start with AI-assisted content creation, measure the impact on output and quality, then expand to other marketing functions based on results.

For enterprises, the key is creating safe spaces for innovation within governance frameworks. Some effective approaches include:

• Establishing fast-track approval processes for low-risk AI pilots
• Creating sandboxed environments for controlled testing
• Forming dedicated AI innovation teams to evaluate tools and establish best practices
• Building clear guidelines for department-level AI adoption

The technology landscape will continue evolving rapidly. Forward-thinking companies are already seeing results: faster development cycles, more efficient operations, and better customer experiences. The gap between these early adopters and those waiting on the sidelines grows wider each month.

Starting small doesn’t mean moving slowly. It means being deliberate about where and how you implement AI, measuring the impact, and scaling what works. The companies that thrive will be those that find this balance between thoughtful implementation and decisive action.

AI adoption isn’t a luxury—it’s now a competitive necessity.

If you’re exploring GPUs and their role in AI or machine learning, check out Modal’s GPU Glossary. It’s a simple, no-nonsense guide to GPU basics—great for beginners or anyone needing a quick refresher.

From CUDA cores to TensorRT, the glossary explains key terms clearly and concisely. Perfect for cutting through the noise and getting the essentials.

Take a look—it’s worth it!

Xcode has come a long way. An excellent trip down memory lane.

After using Classic Mac OS at school for some time, it took me several years to return to the Mac. It was 2004, and I was happy to discover Mac OS X 10.3 Panther. It was completely new and better. Been using it until today (not Panther, though) and will, for the coming years. Some behind the back usages of other OSes, but let him who is without sin cast the first stone.

Today we are announcing support for a new proposed DNS standard — co-authored by engineers from Cloudflare, Apple, and Fastly — that separates IP addresses from queries, so that no single entity can see both at the same time. Even better, we’ve made source code available, so anyone can try out ODoH, or run their own ODoH service!

They say old habits die hard. That must be the reason why so many of my developer colleagues like using Vim (mostly) to develop these days. I must admit I never understood the why of it, but if you’re ok with it, use whatever makes you happy.

This year’s AWS conference, re: Invent, is a fully virtual one. If you never heard of re: Invent, it’s the conference where AWS announces what they’ve been working on, and what the competition has to catch-up with (the list is long).