That’s precisely what happened with Anthropic, the AI startup behind Claude, which has quickly risen to become one of the most valuable players in the artificial intelligence race.

The announcement isn’t just about money. It’s a signal of where AI is heading, how investors are betting on the future, and why the AI gold rush shows no signs of slowing down.

But it also raises a pressing question: Is this a genuine transformation, or are we watching the early stages of an AI bubble?

In this article, I’ll break down who Anthropic is, why this valuation matters, and what it means for the broader AI ecosystem.

Along the way, I’ll also connect the dots to related shifts in technology, business, and society that I’ve been covering on Tech It From Me.

Who Is Anthropic?

Anthropic was founded in 2021 by a group of former OpenAI researchers, including siblings Dario and Daniela Amodei.

They left OpenAI over concerns that AI was being commercialized too quickly and without enough focus on safety.

Their mission with Anthropic? To create safer, more reliable AI systems guided by a framework they call “Constitutional AI.”

At its core, Constitutional AI means training models on explicit written principles — a kind of “AI rulebook” — so that the system is less unpredictable and more aligned with human values.

Instead of reacting after something goes wrong, Anthropic bakes safety and transparency directly into the training process.

Their flagship product, Claude, has quickly become a serious competitor to OpenAI’s ChatGPT and Google’s Gemini.

The Claude family has already advanced through several versions, with Claude 3.5 offering one of the most enormous context windows in the market — over 100,000 tokens — allowing it to process massive documents, entire codebases, or extended conversations without losing track.

Claude is designed with enterprise applications in mind: legal research, financial analysis, knowledge management, and other contexts where reliability matters more than novelty.

That focus has helped it gain rapid adoption across industries while also earning attention from everyday consumers through Claude.ai and its subscription service, Claude Pro.

In a crowded field where OpenAI dominates headlines and startups fight for attention, Anthropic’s positioning is precise: AI that’s powerful, but built to be safe, transparent, and enterprise-ready.

Suppose you’re curious about how Claude fits into the larger ecosystem of language models. In that case, I recommend my earlier breakdown on what large language models really are and how they shape our experiences.

The $183 Billion Valuation Explained

So how does a three-year-old startup hit a valuation that rivals century-old corporations? The answer lies in a perfect storm of massive investor backing, enterprise demand, and market psychology.

1. Massive investor backing
Anthropic has raised billions in funding, primarily from Amazon and Google, which each sees the company as a strategic asset in their battle for dominance in the AI and cloud computing wars.

Amazon has committed up to $4 billion to integrate Claude into its AWS offerings, while Google has invested over $2 billion and provides cloud infrastructure for Anthropic’s training workloads.

These aren’t just financial bets — they’re tactical plays to ensure neither Microsoft nor OpenAI monopolize the next generation of AI.

This dual partnership is unusual. It gives Anthropic both financial resources and access to the world’s most advanced cloud infrastructure — but it also means Anthropic’s independence could be tested as these tech giants jockey for influence.

2. Enterprise adoption
While much of the public conversation focuses on ChatGPT, inside the enterprise world, Claude has been gaining traction.

With its larger context windows (over 100,000 tokens), Claude can handle complex use cases such as analyzing long legal documents, combing through financial reports, or managing knowledge bases.

This makes it especially appealing to businesses that need reliability and depth, not just clever conversation.

For CIOs and IT leaders navigating layoffs, outsourcing, and digital transformation, tools like Claude represent a way to do more with less — a theme I’ve covered in how IT departments have evolved from Y2K to AI.

3. Fear of missing out (FOMO)
The current AI market is also being fueled by investor psychology.

With Nvidia’s rise and Microsoft hitting a historic $4 trillion valuation, there’s a widespread belief that AI will mint the next generation of trillion-dollar companies. Investors don’t want to miss out on the “next OpenAI,” and that frenzy drives valuations higher than fundamentals might justify.

In many ways, Anthropic’s $183 billion figure isn’t just a reflection of what it has built today — it’s a reflection of what the market hopes it will become tomorrow.

But unlike Microsoft or Nvidia, Anthropic is not yet profitable.

Its revenues come from subscriptions to Claude Pro and enterprise licensing deals, but they don’t begin to justify a valuation of this scale.

That disconnect is why some analysts are whispering about an AI bubble, drawing comparisons to the dot-com era, where valuations soared far ahead of real-world business models.

Anthropic’s Strategic Position

Anthropic’s staggering valuation makes more sense when you look at its unique position in the AI ecosystem. Unlike most early-stage startups, it isn’t struggling to get noticed — it has the backing of two of the world’s biggest technology companies.

Both Amazon and Google have poured billions into the company, with Amazon pledging up to $4 billion and Google investing more than $2 billion.

These aren’t just financial deals — they’re strategic alliances designed to give each cloud provider a powerful weapon against Microsoft’s partnership with OpenAI.

This unusual dual investment leaves Anthropic in a rare position: financially secure, with access to the world’s most advanced cloud infrastructure, but also under the influence of two giants who may eventually compete for control.

The arrangement highlights how deeply intertwined the AI race has become with the cloud wars, where infrastructure and algorithms are now inseparable.

Beyond its investors, Anthropic differentiates itself through its safety-first philosophy. While rivals like OpenAI and xAI often emphasize speed and scale, Anthropic markets Claude as the “reliable choice.”

Its long context windows — currently over 100,000 tokens, according to Anthropic’s official model card — make it especially attractive to industries like law, finance, and healthcare, where accuracy and consistency are paramount.

For these organizations, Claude isn’t just another chatbot; it’s a business tool designed for high-stakes environments.

This positioning also gives enterprises more choice in an increasingly crowded landscape. Instead of being locked into Microsoft’s ChatGPT ecosystem, businesses can weigh Claude against Google’s Gemini or even open-source alternatives.

For companies worried about regulatory scrutiny or reputational risk, Anthropic’s emphasis on transparency and trust may prove to be its most potent competitive edge.

Taken together, Anthropic’s cloud partnerships, its enterprise-focused product strategy, and its mission-driven brand have positioned it as one of the most credible challengers in the AI arms race.

Whether it can maintain that independence and momentum as the industry consolidates is another question entirely.

The Implications for the AI Race

Anthropic’s rise doesn’t happen in a vacuum. Its sudden growth to a $183 billion valuation changes the dynamics of the AI race in several important ways — from talent pipelines to enterprise adoption to the looming shadow of regulation.

One of the clearest impacts is on the talent competition.

AI researchers are among the most sought-after professionals in the world, with salaries rivalling those of professional athletes or Wall Street bankers. By building a strong cultural identity around “safe AI,” Anthropic has managed to lure away some of the brightest minds from rival firms like OpenAI and Google DeepMind.

For technology professionals watching from the sidelines, it’s a reminder of how valuable expertise in this space has become. It also underscores the importance of keeping skills sharp, whether through career certifications or by building resilience to survive industry layoffs.

Another major shift is happening at the enterprise level. Until recently, many organizations assumed their only real option for generative AI was Microsoft’s ChatGPT, which dominates both headlines and boardroom discussions.

But with Anthropic’s Claude models now widely available, companies have a credible alternative. For small businesses in particular, the opportunity lies in learning how to apply these tools pragmatically — not chasing hype, but finding efficiencies.

I explored this idea further in my guide on how small businesses can use AI to save money. Claude’s combination of long context windows and enterprise-ready design makes it an appealing option for organizations that want reliability without being tied to Microsoft’s ecosystem.

Finally, Anthropic’s rise has intensified the regulatory spotlight. The larger valuations climb, the more pressure governments feel to step in and ensure AI systems are being deployed safely.

Policymakers are already experimenting with measures like AI watermarking to track machine-generated content, while law enforcement agencies warn of the risks of AI-generated scams.

Anthropic’s branding as a “safety-first” company positions it well in these conversations, but whether that narrative holds depends on how effectively its approach to Constitutional AI can stand up to real-world scrutiny.

In short, Anthropic isn’t just another startup riding the AI wave. Its rise is forcing changes across three critical fronts — talent, enterprise adoption, and regulation — that will shape the future of artificial intelligence far beyond this single company.

What Could Go Wrong

Despite the excitement surrounding Anthropic’s meteoric rise, serious risks remain that investors, enterprises, and end-users can’t ignore. A $183 billion valuation may look impressive on paper, but it doesn’t guarantee long-term success.

The first and most obvious concern is the possibility of an AI bubble. Anthropic’s valuation may be more a reflection of market hype than business fundamentals.

This dynamic feels eerily similar to the dot-com era, when unproven companies commanded sky-high valuations before crashing back to earth. History shows that when money floods into a hot sector too quickly, corrections can be brutal.

There’s also the question of dependency on partners.

Anthropic owes much of its current strength to massive investments from Amazon and Google. While this backing gives it access to billions in funding and world-class cloud infrastructure, it also places limits on independence.

If either company changes its strategic priorities — or decides to exert more control — Anthropic could find itself constrained or even absorbed. For a startup that prides itself on a unique “safety-first” culture, this kind of dependency could be destabilizing.

Another challenge is consumer awareness.

While ChatGPT has become a household name almost overnight, Claude remains far less known outside of enterprise and tech circles. That’s a marketing problem as much as it is a product one.

In a competitive market, brand recognition matters. Without it, even the most capable AI risks being overshadowed.

Finally, there’s competitive pressure. The pace of development in AI is relentless, and rivals aren’t standing still.

With OpenAI’s GPT-5 live and Elon Musk’s xAI aggressively positioning itself as a challenger, Anthropic won’t have the luxury of moving slowly. Keeping up with innovation cycles while staying true to its safety-first philosophy will be a difficult balance to strike.

These risks also tie into a broader industry problem: the unsustainable cost of scaling AI models.

Training state-of-the-art systems requires enormous computing power, and the financial burden is becoming a race in itself. I explored this in more depth in my piece on the real cost of AI’s compute arms race, where I outlined how the economics of AI may eventually become as much a limiting factor as the technology itself.

Anthropic may be one of the strongest players in today’s AI race, but its path forward is anything but risk-free.

Conclusion: A Milestone and a Warning

Anthropic’s $183 billion valuation is both a milestone and a cautionary tale.

On one hand, it demonstrates just how quickly the AI race is escalating, with capital flowing at a scale rarely seen outside of the largest tech companies. Investors are betting that a handful of players — Anthropic among them — will define the next technological era.

On the other hand, the speed and size of this valuation raise fundamental questions about sustainability, independence, and whether the AI sector is already showing signs of bubble-like behaviour.

For enterprises, Anthropic offers something rare in the current market: a safer, enterprise-friendly alternative to ChatGPT.

Its focus on transparency, long context windows, and reliability resonates with industries where mistakes carry enormous consequences.

For investors, the company is a bet on the future of AI infrastructure, and by extension, the cloud providers that support it. And for the rest of us, Anthropic is a reminder that technology revolutions aren’t just about algorithms — they’re about money, power, and the values we embed into the systems we build.

If you thought Nvidia’s meteoric rise was fast, keep your eye on Anthropic. Its valuation may prove to be the opening act of an even bigger drama — one that will test not just the limits of technology, but the resilience of the markets, the responsibility of regulators, and the trust of the public.

Frequently Asked Questions (FAQ)

(Images generated with the help of DALL-E.)

The infamous “Nigerian prince” email became a cultural joke. But in 2025, the joke is over.

Today’s scams don’t look like scams. They sound like your boss calling on the phone. They look like your child speaking in a video message.

They arrive as polished, professional emails drafted with flawless English. Artificial intelligence has given criminals a toolset that is cheap, scalable, and alarmingly convincing.

From Clumsy Emails to Convincing Fakes

The most significant shift is credibility. In the past, bad grammar worked in scammers’ favour—it filtered for only the most vulnerable. But generative AI has eliminated that barrier.

Large language models, the same kind of systems I explained in Behind the Buzzwords: What Is a Large Language Model Really?, can now produce text that feels indistinguishable from something a trusted colleague or institution might write.

Pair that with voice cloning and deepfake video, and the con artist no longer needs to bluff. The technology can imitate, and in some cases outperform, human communication.

A scammer doesn’t need to sound like a stranger anymore—they can sound like your manager. They can look like your CFO in a video call.

They can even hold a real-time conversation, powered by a chatbot that never slips up.

Why It’s Happening Now

AI-powered scams aren’t new, but 2025 is the year they’ve become unavoidable. The reasons are layered.

Running advanced AI models has become drastically cheaper, and open-source systems mean criminals don’t need to rent power from the likes of OpenAI.

There’s also the dark economy of “scam-as-a-service,” where criminal groups package ready-made tools—complete with cloned voices and phishing scripts—so anyone can deploy them.

What makes this particularly dangerous is the overlap with the massive pool of stolen personal data already circulating on the dark web.

In How Cybercriminals Really Get Your Info, I described how years of data breaches have created black markets for everything from email addresses to banking records. Now, AI uses that data to tailor scams with unsettling precision.

It isn’t just a generic “Dear Customer” anymore. It’s your name, your employer, your account, woven into a message that feels personal.

The cultural context matters too. Deepfakes are no longer exotic; they’ve appeared in entertainment, advertising, and even politics.

When New Hampshire voters received AI-generated robocalls in President Biden’s voice during the 2024 primaries—something CNN later confirmed—it showed how seamlessly synthetic content could slip into public life.

The Human Cost

Consider the finance worker who wired $25 million after a video call with a “CFO” who didn’t exist. The voice, the face, the mannerisms—all fabricated.

Or think of the Canadian taxpayer who picks up a call from what sounds precisely like a CRA officer, able to answer questions in real time.

In some of the most chilling cases, parents have been sent videos that appear to show their children kidnapped, only to discover later that it was AI-generated extortion.

What connects these stories isn’t just the fraud—it’s the realism. Criminals are no longer asking you to believe the unbelievable.

They’re using technology to create scenarios that trigger urgency, panic, or trust at precisely the right moment.

Fighting Back

Defending against AI-generated scams is far more complicated than deleting spam emails used to be. Technology can help, but human awareness is still the most effective tool.

If a request feels rushed, if a call demands money, if a message tugs too sharply at your emotions, pause before acting. Verify it through another channel.

Even something as simple as calling a known number back can break the illusion.

Businesses, of course, face even higher stakes.

Training employees, enforcing dual approvals for financial transfers, and adopting zero-trust security practices are now essentials rather than best practices.

Many organizations are also beginning to experiment with AI-based defences—systems designed to flag the subtle artifacts that synthetic media leaves behind. It’s an arms race, with one set of algorithms generating deception and another set trying to detect it.

The same cat-and-mouse dynamic also exists in other corners of technology.

In The Real Cost of AI: Who’s Paying for the Compute Arms Race?, I wrote about how the demand for computing power is reshaping industries. Here, the arms race is between criminals and defenders.

Both sides are armed with the same tools.

Looking Ahead

Some researchers hope watermarking techniques can help, embedding invisible signals in AI-generated content so that fakes can be spotted instantly.

Others argue that only regulation will make a difference, though governments are already struggling to keep up with simpler issues, such as Canada’s digital tax rules.

It’s possible that within a few years, your phone or email client will warn you mid-conversation: “This voice may be synthetic.” Until then, the most effective defence is still the oldest one: healthy skepticism.

Conclusion

AI-generated scams aren’t just another chapter in the story of cybercrime. They’re a shift in kind, not just in degree.

By making deception faster, cheaper, and more believable than ever, AI has handed criminals a megaphone.

History shows that every technological leap—from the earliest computers to today’s generative models—brings unintended consequences.

As I explained in The Real History of AI: From Turing to Transformers, progress is never neutral. The challenge for 2025 is ensuring that AI serves as a shield, not just a weapon.

Until then, the best advice is also the simplest: trust cautiously, verify everything, and remember that not every familiar voice belongs to who it claims to be.

FAQs About AI-Generated Scams

(Images generated with the help of DALL-E.)

For many years, the only real option was to grind harder—longer hours, more staff, or cutting back in areas that hurt long-term growth.

But something has changed in the last few years. Artificial intelligence has moved from the boardrooms of Fortune 500 companies into the hands of everyday entrepreneurs.

And the question is no longer if it can help, but how small businesses can use AI to save money without burning more time or resources.

I’ve spent nearly twenty of my thirty years in IT leadership roles, and I’ve seen firsthand how the right technology can make or break a business.

In this article, I’ll walk through the real, practical ways AI is helping small businesses cut costs right now—with examples, tools, and a few personal observations from my own experience.

Automating Repetitive Tasks: Reclaiming Hours Every Week

When I started consulting with small businesses, the same theme came up again and again: owners and their teams were overwhelmed with repetitive tasks.

Answering emails, chasing invoices, and manually scheduling meetings—these tasks ate up hours every week.

Traditionally, the solution was to hire help. But wages, training, and turnover added new headaches. Today, AI-powered automation offers another option.

A chatbot on your website can handle 70% of customer inquiries without human intervention. AI-driven invoicing tools can automatically categorize expenses, send reminders, and reconcile accounts.

Email assistants now prioritize what matters most, so you’re not stuck sorting through clutter.

A few years ago, one café owner I worked with actually eliminated the need for a part-time admin role by switching to an AI-powered booking and payment system.

The savings? Roughly $18,000 a year—enough to invest in new kitchen equipment that boosted both capacity and revenue.

More Innovative Marketing Without Wasted Spend

Marketing is another place where costs can quickly increase.

I’ve seen businesses spend thousands on ads only to discover that most of the clicks came from people who were never going to buy. This is an example of where AI really shines.

AI-driven platforms analyze enormous amounts of data to pinpoint your ideal customers and can automatically adjust campaigns in real time. Instead of paying for impressions that go nowhere, you’re targeting people who are ready to buy.

Small retailers using AI-powered ad optimization regularly cut their budgets by 30–40% while maintaining the same sales volume.

Content creation is also a huge win. Instead of hiring freelancers for every product description or blog post, AI writing tools can generate drafts that you or your team can refine.

That doesn’t just save money—it saves weeks of turnaround time.

When you combine AI with SEO insights, the results get even better. Tools like SurferSEO identify precisely what your customers are searching for, letting you craft content that ranks faster and drives organic traffic.

That means fewer dollars wasted on ads and more free, qualified visitors coming to your site.

Protecting Against Cybercrime (Without a Full IT Department)

Cybersecurity used to be one of those “luxuries” small businesses couldn’t afford. You either rolled the dice or hoped your antivirus subscription would cover you.

The truth? One ransomware attack or data breach can wipe out years of hard work.

Thanks to AI security automation, we can see these tools levelling the playing field. Modern security platforms now include AI that monitors unusual activity and flags threats in real time.

Instead of paying an IT staffer to watch logs 24/7, the system quietly handles it in the background.

I know a law firm that nearly fell victim to ransomware. Their AI-based monitoring tool flagged an unusual login attempt early in the morning and locked the account.

If that had gone unnoticed until the next morning, the recovery costs could easily have reached tens of thousands of dollars. Instead, they paid a few hundred dollars a month for protection. That’s a trade-off any business owner can justify.

Better Inventory Control and Supply Chain Decisions

Inventory is one of those areas where small businesses often bleed cash without realizing it—too much stock ties up working capital. Too little means lost sales and unhappy customers.

AI forecasting tools help solve this problem by analyzing past sales patterns, seasonal demand, and even external factors like weather or economic shifts.

Instead of guessing how much to order, you’re making data-backed decisions.

There are many examples of e-commerce stores being able to trim their excess inventory by 20% after using an AI forecasting system. That frees up cash flow—money they can reinvest into launching new product lines.

Rethinking Hiring and HR Costs

Recruiting is expensive. Job postings, recruiter fees, and the time you spend interviewing add up quickly. And then comes onboarding, training, and paperwork.

AI is making these processes more affordable. Resume screening tools (or Applicant Tracking Systems) can sort through applicants in minutes, flagging the ones most likely to succeed.

Chatbots can guide new hires through onboarding forms and training modules. Some platforms even use predictive analytics to identify employees at risk of leaving—giving you a chance to fix the issue before turnover costs hit.

This can give small businesses the ability to bypass an agency recruiter and use an AI-powered hiring platform to level the playing field with companies that have larger budgets. Imagine as a hiring manager, skipping straight to the final interview and cutting down 90% of the legwork.

Cutting Utility Bills with Smarter Energy Use

This one often surprises people: AI can save small businesses money on utilities. Smart thermostats like Nest or Ecobee learn your patterns and reduce wasted heating or cooling.

AI-driven monitoring tools can identify when equipment is drawing more power than it should, flagging maintenance issues before they become breakdowns.

By learning your habits, such as when you are home during a workweek, and overlaying that with surge energy prices, systems can focus on either saving you money or keeping you at optimal temperatures.

Affordable Cloud AI Services

Finally, many business owners assume AI means expensive software licenses. In reality, cloud providers like Google, Microsoft, and Amazon have made AI tools accessible on a pay-as-you-go basis.

Want to add a chatbot to your site? Translate product descriptions? Analyze customer data? You can do it for pennies per use instead of thousands in upfront licensing.

That’s part of why we’re seeing huge deals at the enterprise level, like Meta’s $10 Billion Google Cloud deal. But the same infrastructure is available to you—scaled down to fit your budget.

Conclusion: Start Small, Save Big

The bottom line is this: AI isn’t some futuristic add-on for businesses with deep pockets. It’s already helping small businesses save money today.

Whether it’s cutting admin costs, reducing marketing waste, tightening up inventory, or avoiding cybersecurity disasters, the opportunities are real.

If you’re asking how small business can use AI to save money, the answer is simple: start with one process.

Automate your emails, try a chatbot, or switch on predictive inventory. Track the savings, reinvest them, and expand from there.

Want more context? Explore The Real History of AI or see GPT-5’s latest features to understand where things are headed next.

(Images created with the help of DALL-E.)

Frequently Asked Questions (FAQs)

The partnership took everyone by surprise. Meta has typically relied on Amazon Web Services (AWS) and Microsoft Azure for cloud services while also investing billions into its own data centers. But as the demand for AI infrastructure skyrockets, Meta has turned to a direct competitor in online advertising: Google.

This deal doesn’t just add revenue to Google’s balance sheet—it reshapes the dynamics of the cloud wars, validates Google’s role as an enterprise-ready cloud provider, and signals how artificial intelligence demand is forcing even the largest technology firms to rethink their alliances.

The $10 Billion Cloud Deal Explained

According to a report by CNBC , and backed up by The Information, Meta has committed to spending over $10 billion with Google Cloud over the next six years. While details are scarce due to confidentiality, sources indicate the deal is focused heavily on AI infrastructure.

That means that Meta isn’t just paying Google for their basic cloud storage or compute power. They are tapping into Google’s most advanced resources, including:

• Tensor Processing Units (TPUs), Google’s custom AI chips built for training large language models.
• High-performance cloud GPUs and networking infrastructure.
• Scalability for massive, distributed AI workloads.

This aligns with Meta’s public roadmap. In its most recent earnings report, Meta projected $114–$118 billion in expenses for 2025, with a significant portion earmarked for AI infrastructure and talent. Meta is all-in on building its LLaMA family of AI models and embedding AI across Facebook, Instagram, WhatsApp, and Threads.

Simply put, Meta can’t build fast enough to keep up with its AI ambitions—and even with AWS and Azure in the mix, Google is now part of the equation.

Why Meta Needs Google Cloud

On paper, Meta has one of the largest data center footprints in the world. Why then would it outsource so much compute power to a rival? Several reasons stand out:

AI Workload Scale

Training and running state-of-the-art AI models requires staggering amounts of compute. Even Meta’s world-class data centers are not enough. Cloud infrastructure provides the flexibility to burst workloads when demand spikes without waiting years to build new facilities.

Diversification of Cloud Providers

Meta is already working with AWS and Azure, but they realize relying too heavily on one partner is risky. Cost, availability, and bargaining power all matter at this scale. By adding Google into the fold, Meta ensures they have redundancy and leverage across all three hyperscalers.

Access to Google’s AI Chips

Google’s TPUs are highly regarded in the technology sector for their efficiency in AI training. By partnering with Google, Meta gains access to one-of-a-kind hardware that they simply can’t replicate in-house or source from AWS/Azure. This has the potential to speed up the training of future LLaMA models.

Faster Time-to-Market

Meta is racing not just against OpenAI and Anthropic but also against Google itself. Offloading infrastructure bottlenecks to Google Cloud allows Meta to bring AI-powered features to users faster.

In short, Meta is doubling down on its competition.

Why This Is a Big Win for Google Cloud

For Google, this is a landmark deal. Historically, Google Cloud has trailed AWS and Azure by a wide margin. But this win delivers multiple benefits:

Financial Impact

The deal adds roughly $1.6B per year to Google Cloud’s revenue stream. In Q2 2025, Google Cloud reported $13.6B in revenue and $2.83B in operating income. Meta’s contract alone represents more than 10% of Google Cloud’s quarterly revenue.

Enterprise Credibility

Winning Meta as a cloud client puts Google Cloud in rare company. AWS may be the default for many enterprises, and Azure is dominant in the Microsoft ecosystem, but this deal proves Google is no longer just the third option—it’s a strategic partner for AI leaders.

Momentum After OpenAI

Earlier this year, Google also secured workloads from OpenAI, a former Azure-exclusive. That’s two high-profile AI wins in less than a year. Together, they suggest Google is becoming the go-to provider for AI workloads, not just a catch-up player.

Validation of TPU Strategy

For years, Google has invested in its custom TPU chips. This deal validates that investment—showing not just internal use but external adoption at massive scale.

This isn’t just revenue; it’s a reputation shift for Google Cloud.

The Cloud Wars: AWS vs Azure vs Google Cloud

For over a decade, the cloud market has been dominated by AWS and Azure, with Google Cloud fighting for a distant third. Let’s see how this deal changes the battlefield.

AWS

• Still the market leader by revenue and customers.
• Provides Meta with significant infrastructure capacity.
• But hasn’t secured exclusivity, showing even AWS can’t monopolize AI deals.

Azure

• Strength lies in its deep integration with enterprise IT and Microsoft’s partnership with OpenAI.
• Azure is critical for organizations betting on ChatGPT and Copilot services.
• Losing exclusivity with OpenAI earlier this year raises questions about its hold on AI workloads.

Google Cloud

• Historically behind in adoption.
• Now carving out a specialty in AI-first cloud contracts.
• Winning both OpenAI and Meta in the same year signals a strategic breakthrough.

The message is clear: the cloud wars are entering a new phase, defined less by general workloads and more by AI-specific infrastructure.

The Frenemies Factor: Meta and Google

One of the most fascinating aspects of this deal is the irony of the partnership.

Meta and Google are bitter rivals in the digital advertising market. Both companies dominate global ad spend, and their competition has often been fierce. Yet here they are, working together on AI infrastructure.

This kind of “coopetition” is becoming more common in tech:

• Rivals compete fiercely in one area while collaborating in another.
• AI is so compute-intensive that it forces companies to set aside rivalries for access to resources.

For Meta, the risk is clear: it’s paying a direct competitor billions. But the benefits of speed, redundancy, and unique hardware outweigh the risks.

The Financial Picture: AI Spending Arms Race

This deal is part of a broader trend: AI is driving record levels of capital expenditure.

• Meta expects $114–118B in expenses for 2025, with AI infrastructure as a major component.
• Alphabet’s Google Cloud unit is growing 32% year-over-year, outpacing Alphabet’s overall 13.8% growth.
• AWS, Azure, and Google Cloud are all pouring billions into GPUs, TPUs, and specialized networking to meet demand.

In many ways, the AI boom is funding the next wave of cloud expansion. Just as SaaS adoption fueled early cloud growth in the 2010s, AI workloads are defining cloud growth in the 2020s.

What This Means for the Future of AI Infrastructure

The Meta–Google deal signals several future trends:

1. Multi-Cloud Will Be the Norm
   Companies won’t put all their eggs in one basket. AWS, Azure, and Google will often share workloads from the same enterprise.
2. Specialized Hardware Will Drive Cloud Choices
   Whoever offers the best chips—whether Nvidia GPUs, Google TPUs, or custom silicon—will win AI contracts.
3. AI Will Reshape Cloud Market Share
   While AWS and Azure remain dominant, Google’s focus on AI could accelerate its climb. Deals like Meta’s will force AWS and Azure to respond with their own AI-focused offerings.
4. “Frenemy” Partnerships Will Multiply
   Expect to see more deals where rivals collaborate out of necessity. The scale of AI makes strange alliances inevitable.
5. Costs Will Continue to Skyrocket
   With Meta alone spending $10B on Google, plus billions on AWS and Azure, the arms race in AI spending is only beginning.

Conclusion: The Cloud Wars Just Got Real

The six-year, $10 billion Meta–Google deal is more than just a headline—it’s a turning point in the cloud wars.

For Meta, it’s about scaling AI faster than competitors like OpenAI, Anthropic, and yes, Google itself. For Google, it’s about proving that its cloud division is ready to compete at the highest level.

The partnership shows that in the age of AI, even bitter rivals can become essential collaborators. And it underlines a broader truth: the future of cloud computing will be written by the demands of artificial intelligence.

(Article feature image and post images generated with the help of Dall-E.)

Tech It From Me

Open-Source AI vs. Big Players: Can Mistral, Ollama and LLaMA Compete?

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Why Open-Source AI Matters

Open-source AI isn’t just a cheaper alternative to big tech models—it’s a philosophical and strategic shift in how artificial intelligence is developed, deployed, and shared. At its core, open-source AI democratizes access to cutting-edge technology, putting advanced tools in the hands of individuals, startups, educators, and researchers who might otherwise be locked out due to cost or licensing restrictions.

When a company like Meta releases LLaMA or a group like Mistral AI shares its model weights, they’re giving the community not just the software but the blueprint. This transparency allows for:

• Full inspection – Developers can review training methods, datasets, and parameters to understand precisely how the model works, a key step in building trust.
• Customization and innovation – Organizations can fine-tune these models for specific industries—healthcare, legal, finance—without sending sensitive data to external APIs.
• Education and research – Universities and independent researchers gain access to real-world, production-grade models for teaching and experimentation, driving academic advancement.

This mirrors the impact of past open-source revolutions. Linux became the backbone of the internet because anyone could contribute to its code and adapt it for their needs. Apache made web hosting accessible without expensive licensing fees. Today, Hugging Face plays a similar role for AI, hosting open models and fostering a collaborative ecosystem.

From a global perspective, open-source AI could help bridge the AI divide between wealthy nations and developing countries. Instead of relying entirely on expensive cloud-based APIs from Silicon Valley, local teams can run their own AI models, adapt them for cultural and linguistic nuances, and build region-specific solutions.

Of course, this openness comes with challenges—security risks, maintenance burdens, and the need for technical expertise—but it also represents one of the most promising pathways to a more equitable AI future.

For a deeper look at how we got to this point, check out The Real History of AI: From Turing to Transformers.

The Big Players vs. the Open Challengers

The AI landscape in 2025 feels like a heavyweight boxing match. In one corner, you have the big players—OpenAI with GPT-5, Google DeepMind with Gemini Ultra, and Anthropic with Claude 3.5—armed with billion-dollar budgets, proprietary datasets, and fleets of high-performance GPUs. In the other corner, a growing roster of open-source challengers—Mistral, Meta’s LLaMA, Falcon, StableLM—who are rewriting the playbook on how AI is built and distributed.

What Gives the Big Players Their Edge?

The giants of AI benefit from:

• Massive Compute Power – Access to thousands of cutting-edge GPUs like the Nvidia H100, often running 24/7 in specialized AI data centers.
• Proprietary Data – Exclusive, high-quality datasets—sometimes acquired through partnerships or licensing—that aren’t available to the public.
• Polished Ecosystems – Seamless integration with tools like Microsoft Office, Google Workspace, and cloud APIs makes adoption easy for enterprise customers.
• Dedicated Research Teams – Hundreds (or thousands) of full-time AI researchers pushing model capabilities to new heights.

These advantages mean that, for now, proprietary models tend to lead in raw performance, reasoning ability, and multi-modal integration (text, image, and audio).

What the Open Challengers Bring to the Table

While the big players dominate in infrastructure, open-source AI brings unique strengths:

• Agility and Innovation – Without corporate red tape, open-source teams can experiment and release updates quickly.
• Community-Driven Development – Thousands of independent contributors worldwide collaborate on model improvements, fine-tuning, and bug fixes.
• Accessibility – Anyone can download, run, and adapt these models, from a single GPU workstation to an on-premise server.
• Lower Cost Barriers – Instead of paying per-token usage fees, organizations can self-host and control expenses.

In some cases, open-source projects have even leapfrogged big tech in niche areas. For example, smaller models optimized for local inference can outperform massive proprietary models when latency, privacy, or cost is a top priority.

The Real Competition Isn’t Just About Speed or Accuracy

While benchmark leaderboards matter, the battle between big tech and open-source is increasingly about trust, control, and sustainability.

• Enterprises are asking: Do we want to send sensitive data to an external API?
• Developers are asking: Do we want to be locked into one provider’s pricing and policies?
• Governments are asking: Who should control the infrastructure of AI?

This growing tension is why both sides are watching each other closely. Big tech companies are beginning to incorporate open-source principles into certain releases, while open challengers are finding ways to leverage commercial partnerships for funding and compute resources.

For a closer look at the cost and infrastructure side of this arms race, check out The Real Cost of AI: Who’s Paying for the Compute Arms Race?.

Where Open-Source AI Wins

While proprietary models often steal the spotlight with jaw-dropping demos and billion-parameter bragging rights, open-source AI quietly wins in areas that matter most to businesses, developers, and researchers looking for control and flexibility.

1. Customization Without Limits

With open-source AI, you’re not stuck with a “one-size-fits-all” solution. You can fine-tune the model on your data, tailor it for a specific industry, or even optimize it for a single task. For example:

• A healthcare provider could adapt an open model for medical terminology and privacy compliance.
• A financial services firm could build a specialized chatbot that understands regulatory constraints without sending data to an external API.

Proprietary AI may offer customization via APIs, but it usually comes with hefty costs, rate limits, and data usage restrictions.

2. Cost Control and Predictability

One of the biggest frustrations with proprietary AI is the unpredictable—and often escalating—per-token billing model. Open-source AI changes that. By self-hosting models on local servers or private cloud instances, you can:

• Avoid per-query fees that pile up at scale.
• Repurpose existing infrastructure, like idle GPU servers.
• Budget for fixed hardware and energy costs instead of variable API charges.

This mirrors the cost-saving mindset seen in other tech areas, such as when some small IT teams choose Cloudflare over traditional firewalls.

3. Data Privacy and Control

In regulated industries—finance, healthcare, legal—sending sensitive data to an external AI provider can be a compliance nightmare. Open-source AI allows you to keep both the model and data inside your organization’s secure environment. This is a massive win for:

• Meeting GDPR and HIPAA requirements.
• Protecting proprietary research and intellectual property.
• Avoiding vendor lock-in that can lead to costly migrations later.

4. Transparency Builds Trust

With open-source models, you can audit the training process, inspect datasets, and verify ethical safeguards. This level of transparency isn’t possible with black-box proprietary systems. And in an era where deepfakes and misinformation are on the rise (see our piece on AI watermarking), that transparency matters more than ever.

5. Community Innovation Outpaces Corporate Timelines

Big tech companies follow roadmaps tied to product launches, investor calls, and quarterly earnings. Open-source AI, however, evolves at the speed of the community—sometimes releasing major updates or breakthrough features within weeks, not months.

Projects like Hugging Face Transformers have shown how a passionate global developer base can maintain, improve, and scale AI tools far faster than traditional corporate cycles.

The bottom line: Open-source AI may not yet beat proprietary models on every benchmark, but it consistently wins on flexibility, cost efficiency, and ethical transparency—and in the long game, those advantages could prove more transformative than raw compute power.

Where They Still Lag

For all its advantages, open-source AI still faces some steep uphill battles before it can fully match the capabilities—and market dominance—of proprietary giants like GPT-5, Claude 3.5, and Gemini Ultra.

1. Performance Gap on Cutting-Edge Benchmarks

While open-source models are making rapid progress, they often trail proprietary models in areas like:

• Complex reasoning across multiple steps or domains.
• Multi-modal integration—seamlessly combining text, images, and audio in a single query.
• Long-context handling, where big players’ models can process hundreds of thousands of tokens without losing coherence.

Many open models are optimized for smaller hardware footprints, which is great for accessibility but can limit raw performance.

2. User Experience & Integration

Proprietary AI platforms invest heavily in user-friendly interfaces and turnkey integrations with popular tools like Microsoft Office, Google Workspace, and enterprise CRM systems.
By contrast, open-source AI often requires:

• Command-line setup or manual configuration.
• Familiarity with Python, Docker, or specialized ML frameworks.
• Additional development work to connect the model to business workflows.

This can be a barrier for non-technical teams who need AI solutions that “just work” out of the box.

3. Resource Demands for Self-Hosting

Running an open-source LLM locally isn’t free—you still need:

• High-end GPUs (or expensive cloud compute) for fast inference.
• Reliable storage and networking infrastructure.
• Ongoing maintenance to keep the model secure and up to date.

This is where many organizations realize that while the model itself may be free, the total cost of ownership can still be significant. For more on the economics behind this, see The Real Cost of AI: Who’s Paying for the Compute Arms Race?.

4. Limited Enterprise Support

If a proprietary AI model goes down, customers can usually call a support line or file a priority ticket backed by an SLA. Open-source AI relies on:

• Community forums
• GitHub issues
• Volunteer maintainers

While these communities can be incredibly responsive, there’s no guarantee of 24/7 coverage or rapid resolution—something enterprise clients often require.

5. Security Risks and Abuse Potential

Open weights make it easier for bad actors to fine-tune models for malicious purposes, from generating deepfake content to creating phishing scripts that evade detection.
This risk is a growing concern for governments, businesses, and security researchers alike—and it’s why many are exploring model watermarking and AI-specific threat detection (see our coverage here).

The takeaway: Open-source AI is closing the gap quickly, but the last mile—performance parity, seamless usability, enterprise-grade support, and security hardening—remains a formidable challenge.

The Role of Compute & Infrastructure

Whether you’re running GPT-5 in the cloud or deploying LLaMA 3 on a local server, AI performance is ultimately limited by the hardware and infrastructure behind it. The difference is in who pays for—and who controls—that infrastructure.

Why Compute Power Is the True Bottleneck

Large language models (LLMs) are hungry. Even “lightweight” open-source models can require:

• High-performance GPUs such as Nvidia A100s or H100s for fast inference.
• Hundreds of gigabytes of RAM and storage for hosting model weights and datasets.
• Robust cooling and power systems to keep data centers running 24/7.

The most prominent players in AI have access to massive, specialized AI clusters purpose-built for model training and deployment. Open-source developers, on the other hand, often rely on a mix of community-donated compute, cloud credits, or smaller-scale local setups.

Cost Isn’t Just About Hardware

Even if you own the GPUs, you still face ongoing costs for:

• Electricity consumption – Running a high-end AI server is like keeping a powerful gaming PC on full blast all day.
• Network bandwidth – AI models often move vast amounts of data between compute nodes.
• Maintenance & updates – From replacing failed components to keeping dependencies up-to-date.

For a deeper look at the economics, check out The Real Cost of AI: Who’s Paying for the Compute Arms Race?.

The Cloud vs. On-Prem Trade-Off

Many organizations choose between:

• Cloud hosting – Faster setup, scalable resources, and lower upfront costs, but ongoing usage fees and potential data privacy concerns.
• On-premises hosting – Higher initial investment in hardware but full control over data, customization, and long-term costs.

Open-source AI offers flexibility here—you decide where to run it. Proprietary AI services typically lock you into their cloud platform.

A Growing Need for Specialized AI Infrastructure

We’re also seeing a rise in AI-optimized hardware beyond just GPUs:

• AI-specific accelerators like Google’s TPUs.
• Edge AI devices that run smaller models close to the data source for faster, offline inference.
• AI networking solutions optimized for high-bandwidth, low-latency communication between servers.

As both open-source and proprietary models scale, compute capacity may become the new competitive currency—and organizations that invest early could gain a significant advantage.

The Security Question

As AI models—both proprietary and open-source—become more capable, security concerns are moving to the forefront. For open-source AI, the same transparency that fuels innovation can also open the door to misuse, manipulation, and exploitation.

The Double-Edged Sword of Open Weights

When an AI model’s weights and architecture are freely available, anyone can:

• Fine-tune it for beneficial, specialized use cases (like medical diagnosis or legal research).
• Or… fine-tune it for malicious purposes, such as generating convincing phishing emails, creating deepfake videos, or bypassing content moderation filters.

While proprietary models also face misuse risks, their closed nature makes it harder for bad actors to directly alter the model’s behaviour at the code level.

Model Poisoning: The Hidden Threat

One of the emerging risks is model poisoning, where malicious actors subtly modify an AI model during training or fine-tuning to introduce hidden biases or vulnerabilities. This can:

• Cause the model to behave incorrectly under specific conditions.
• Leak sensitive information that was never intended to be stored.
• Weaken defences against harmful outputs.

In large, collaborative open-source projects, guarding against poisoning requires strong community oversight, code reviews, and automated testing pipelines.

Data Privacy and Compliance Risks

For enterprises, another concern is how AI handles sensitive data. Without strict controls, an open-source model could unintentionally memorize and regurgitate private information from its training data.
That’s why secure deployment practices—like running AI behind the firewall—are critical for regulated industries. We discussed similar privacy concerns in How Cybercriminals Really Get Your Info.

The Push for AI Watermarking and Traceability

To combat misuse, researchers are exploring watermarking—embedding hidden signals in AI-generated content to help identify its source. This could:

• Assist in detecting misinformation campaigns.
• Help businesses verify the authenticity of content.
• Deter malicious actors by reducing plausible deniability.

For a deeper dive, see Watermarking AI: Will It Change the Way We Write Forever?.

Why Security Could Decide the Winner

In the battle between open-source and proprietary AI, security might become the ultimate differentiator. Enterprises will gravitate toward the option that offers:

• Clear guardrails against misuse.
• Strong governance frameworks.
• Compliance with emerging AI regulations worldwide.

If open-source communities can solve these challenges at scale, they may not just compete with big tech—they could redefine trust in AI.

The Road Ahead

The open-source vs. proprietary AI debate isn’t a zero-sum game—it’s a race toward two very different visions of the future. On one side, big tech will continue building massive, multi-modal AI systems with unmatched scale, integration, and enterprise polish. On the other hand, open-source AI will focus on transparency, adaptability, and putting powerful tools in the hands of the many rather than the few.

A Likely Hybrid Future

Much like the software ecosystem evolved with Linux and Windows coexisting, we can expect a hybrid AI landscape:

• Proprietary AI is leading in general-purpose consumer applications, cloud-hosted enterprise solutions, and high-stakes AI research.
• Open-source AI is thriving in specialized, privacy-sensitive, and cost-conscious use cases where customization and control matter most.

The Catalysts That Will Shape the Outcome

Several factors will determine how this balance plays out:

• Compute accessibility – Will affordable, high-performance AI hardware become mainstream?
• Security breakthroughs – Can open-source AI establish trust without limiting innovation?
• Regulation – How will governments approach AI governance, and will open-source models be regulated differently?
• Community momentum – Will the global developer ecosystem keep pace with proprietary advancements?

What This Means for Businesses and Developers

For organizations, the takeaway is clear: Don’t lock yourself into a single AI strategy. Explore both ecosystems, run pilots, and evaluate where each approach fits your needs.

• If speed to market and out-of-the-box performance are critical, proprietary AI may be the better starting point.
• If cost control, privacy, and domain-specific customization matter most, open-source AI could be the long-term winner.

For developers, the next few years will be an unprecedented opportunity to shape the direction of AI itself—either by contributing to open-source projects, innovating on top of them, or integrating them with proprietary platforms in creative ways.

Final Thoughts

Open-source AI has already proven it can stand toe-to-toe with big tech in certain arenas. The question isn’t whether it can compete—it’s how much of the future it will own.

For more insights on the evolution of technology leadership and AI’s role in business, check out From Y2K to AI: How IT Departments Have Changed and Where They’re Headed, or explore The Real Cost of AI for a deeper dive into the economics driving the AI arms race.

(Feature image generated with the help of DALL-E)

And no, I’m not just talking about R&D salaries or office space in Silicon Valley. I’m talking about the staggering bills for cloud computing, the skyrocketing price of GPUs, and an environmental footprint that’s growing as fast as the models themselves.

The truth is, the AI boom isn’t just a race for innovation — it’s a compute arms race. And as we saw with the recent GPT-5 release, the stakes (and costs) keep climbing. The question we should be asking is: who’s paying for it, and at what price?

Tech It From Me

The Real Cost of AI: Who’s Paying for the Compute Arms Race?

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1. The Money Trail: Cloud Bills and GPU Gold Rush

The financial side of AI isn’t just big — it’s massive. Training a cutting-edge model like GPT-5 is not a hobby project you run on a weekend. It’s an industrial-scale operation requiring thousands of high-end GPUs running non-stop, often for weeks or even months.

And these GPUs aren’t the kind you can pick up at your local electronics store. Nvidia’s H100s — and the soon-to-launch B200s — are the current gold standard. They’re powerful, scarce, and expensive. One H100 can sell for $30,000 if you can even find one. Even the biggest tech companies end up on waiting lists.

For many organizations, the solution is renting GPUs in the cloud from providers like AWS, Azure, or Google Cloud. But that comes at a price. Renting just one top-tier GPU instance can cost hundreds of dollars a day. Multiply that by thousands of GPUs for a month-long training run, and you’re looking at infrastructure bills in the tens of millions — before a single dollar is spent on testing, fine-tuning, or deployment.

It’s the same dynamic as a gold rush. In the 1800s, miners chased gold, but the real winners were the ones selling shovels. In today’s AI rush, Nvidia and the major cloud providers are those shovel sellers. They profit regardless of whether the miners — in this case, AI startups and research teams — ever strike gold. (Nvidia’s recent $4 trillion valuation is proof of just how profitable this role has become.)

And while big tech can absorb these costs, smaller startups often can’t. With limited funding and no guarantee of a return, many are forced to pivot. Instead of training models from scratch, they fine-tune existing ones like OpenAI’s GPT, Anthropic’s Claude, or open-source models like LLaMA and Mistral. This approach can level the playing field, but it also means true ground-up innovation is increasingly reserved for those with the deepest pockets.

If you want to understand why these systems require so much computing power, check out my article: Behind the Buzzwords: What Is a Large Language Model, Really?.

2. The Environmental Price Tag

AI doesn’t just consume money — it consumes massive amounts of natural resources.

Training GPT-3 used an estimated 1,300 MWh of electricity — enough to power 120 U.S. homes for a year. GPT-5 hasn’t had its numbers publicly released, but given its scale, it’s safe to assume the footprint is far larger.

And electricity is only part of the story. According to The New York Times, data centers use millions of litres of water annually to cool high-performance GPUs. In some regions, that means AI development is competing directly with agriculture and household water needs. The resource demands aren’t unlike what I saw during my 15 years in the engineered environmental sector — large-scale industrial operations with measurable local impacts.

The carbon impact of AI is equally concerning. Unless a data center operates entirely on renewable energy, every large-scale training run generates CO₂ emissions. Multiply that by dozens or hundreds of training cycles worldwide, and AI starts to look less like a “clean” technology and more like another heavy industry with a substantial environmental tab.

Even after training is complete, the environmental cost continues. Every query you send to an AI model — every prompt, every request — consumes compute power. The training phase is like constructing a massive factory, but inference (actually running the model) is like keeping that factory in constant operation.

3. The Ripple Effect: How Costs Come Back to You

Even if you’ve never trained an AI model in your life, you’re still part of the equation — because these costs eventually reach you.

The first ripple is subscription creep. AI tools that once offered free tiers are introducing “pro” plans, while existing paid tiers are hiking prices or adding usage caps. This isn’t just about profit — rising infrastructure and energy costs have to be recouped somewhere. If you’ve ever watched a technology service quietly increase pricing year over year, this will feel familiar.

The second is vendor lock-in. Once a company builds its AI systems on AWS, Azure, or Google Cloud, switching isn’t just inconvenient — it’s expensive. This lock-in gives cloud providers enormous pricing power. It’s a dynamic similar to what I discussed in From Y2K to AI: How IT Departments Have Changed and Where They’re Headed — once critical infrastructure is tied to one vendor, your negotiating leverage drops.

The third ripple is market risk. AI right now is in a hype cycle reminiscent of the late 90s dot-com boom. Back then, companies with little more than a website and a pitch were raising millions. When the bubble burst, valuations collapsed, and many companies — along with their customers — were left stranded. We could see something similar here if the cost of building and running AI systems outpaces the revenue they generate. For a look at how this plays out at the top end of the market, read Nvidia Hits $4 Trillion: How the AI Gold Rush Changed Everything.

Finally, there’s the indirect consumer cost. Even if you don’t pay for AI directly, it may be built into the products and services you buy. A retailer might use AI for demand forecasting, a bank might use it for fraud detection, and when their costs go up, they get passed along. In some cases, these increases are so subtle you won’t notice them — just like when cybersecurity investments (a topic I cover in How Cybercriminals Really Get Your Info) quietly become part of the price of doing business.

4. Can AI Be Cheaper and Greener?

The good news is that AI doesn’t have to be prohibitively expensive or environmentally damaging. There are paths to make it both cheaper and greener — but they require changes in both technology and business practices.

One approach is to rethink model design. Today’s most powerful AI models are generalists, designed to handle almost anything you throw at them. But smaller, specialized models can often perform just as well for specific tasks while using far less energy and compute. This kind of targeted efficiency is something I’ve seen work in other areas of tech, and it’s a recurring theme in discussions about open-source AI, where leaner architectures often win on cost.

Another promising direction is Mixture of Experts (MoE) architectures. Rather than running the entire model for every request, MoE systems activate only the “experts” relevant to a specific query. This selective activation significantly reduces compute requirements, which means lower costs and a smaller carbon footprint.

Then there’s infrastructure strategy—location matters. Data centers in cooler climates require less energy for cooling, and those built near renewable energy sources can operate with dramatically lower emissions. Google, Microsoft, and Amazon are all making public commitments to expand renewable-powered data centers, and smaller players can follow suit. For a deeper dive on the strategic side of tech decisions like this, see Build vs. Buy: Making the Right Tech Call Without Regret.

Open-source innovation is also part of the answer. Models like LLaMA, Mistral, and Falcon may not match closed systems like OpenAI’s GPT-5 in every benchmark, but they can be fine-tuned and deployed for a fraction of the cost. This democratizes AI development, making it accessible to startups, research labs, and even individuals with modest budgets.

Finally, policy and market pressure could accelerate the shift toward greener AI. In the same way government incentives boosted renewable energy adoption, policymakers could require transparent carbon reporting for large-scale training runs or offer benefits for using renewable-powered infrastructure.

The question is whether these sustainable practices will scale quickly enough. If not, we risk repeating the same pattern we’ve seen in other areas of tech: rapid growth followed by a reckoning over costs and impact. For context on how hype cycles can distort priorities, check out Watermarking AI: Will It Change the Way We Write Forever?.

Final Thoughts

The AI revolution isn’t just about algorithms, innovation, or flashy product launches — it’s also about the real costs hiding beneath the surface. From GPU shortages and cloud infrastructure bills to the environmental footprint of massive data centers, every leap forward has a price tag.

As we’ve seen, the compute arms race is pushing costs higher and making it harder for smaller players to compete. These expenses don’t just stay in the boardroom — they eventually show up on your subscription bill, your electricity grid, or even your local water supply.

But it doesn’t have to be this way. The solutions are already on the table: smaller, specialized models; energy-efficient Mixture of Experts designs; open-source alternatives like those in the open AI community; and a shift toward green data centers powered by renewables. The challenge is in getting both industry leaders and policymakers to adopt them at scale.

If we fail to address the financial and environmental realities, we risk building an AI future that’s innovative on the surface but unsustainable at its core. The winners of this race won’t just be those with the most advanced models — they’ll be the ones who can keep the lights on without burning through the planet’s resources.

So the next time you read about a breakthrough like GPT-5 or the latest AI startup making headlines, ask the real question: Who’s paying for all this compute? Because in one way or another, the answer might be you.

(Feature image generated with the help of DALL-E.)

Sam Altman says the leap from GPT-4 to GPT-5 is so big that going back feels “miserable.” But is GPT-5 really the iPhone moment for AI—or is it just the latest upgrade in an already fast-moving race?

I’ve already spent some time using the new version, and I can confirm: many of the improvements are noticeable right away. The model feels faster, more confident in its answers, and much better at staying on track in more extended conversations. It’s still not perfect, but it’s a more enjoyable and productive experience.

Let’s unpack what’s new, what’s useful, and whether this release changes the game.

What’s New in GPT-5?

OpenAI officially rolled out GPT-5 to all users on Friday, August 8, 2025, marking what the company calls its most advanced ChatGPT release yet. For Pro subscribers, the upgrade is even bigger—you now get access to GPT-5, GPT-5-Pro, and GPT-5-Thinking, each tuned for different levels of depth and reasoning.

So, what exactly has changed? Quite a bit:

1. Smarter, Context-Aware Reasoning
GPT-5 takes “chain-of-thought” logic to the next level, breaking down complex problems into smaller, logical steps—without you having to spell it out. This means more accurate answers, fewer logical gaps, and better handling of multi-part queries.

2. Dramatically Fewer Hallucinations
While no AI is perfect, GPT-5 shows a noticeable reduction in factual errors. In my testing, I’ve seen cleaner, more consistent outputs that stay true to source material—especially when summarizing technical or niche topics.

3. ‘Vibe Coding’ for Developers
This is a game-changer for coders. Instead of simply producing functional code, GPT-5 can now interpret how you want your code to feel—whether that’s clean and minimalist, performance-optimized, or playful and experimental. It bridges the gap between intent and execution.

4. Unified Model Routing
Say goodbye to manually switching between browsing mode, code interpreter, or GPT-4 for different tasks. GPT-5 automatically chooses the right capabilities in the background, making the experience seamless and letting you focus on your work.

5. Deeper App Integrations
You can now connect GPT-5 directly to tools like Gmail, Google Calendar, and potentially more productivity platforms in the near future. This turns ChatGPT from a standalone assistant into something closer to a true personal AI workspace.

6. Custom Personalities with Memory
Want an AI writing partner with your exact tone? Or a coding assistant who remembers your preferred frameworks? GPT-5 now allows you to create named GPTs with persistent memory, so each assistant gets better the more you use it.

From my own use, two features stand out: Unified Model Routing, which makes every interaction feel frictionless, and Vibe Coding, which is almost eerie in how well it understands intent. For anyone who works with AI regularly, these aren’t just quality-of-life improvements—they’re workflow accelerators.

Is This Really the iPhone Moment?

Every major AI release seems to get compared to a tech milestone—Apple’s original iPhone launch, the debut of the Retina display, or even the first personal computer. With GPT-5, the buzz is no different. Some have already called it the “iPhone moment” for AI, suggesting it could be a turning point in how we interact with technology (source: The Verge).

But is that really the case?

If we look at the AI timeline, GPT-3 was arguably the real “wow” moment for the public—it introduced mainstream users to just how human-like a language model could feel (see my breakdown of AI’s history here). GPT-4 refined that experience, making it more reliable and capable. GPT-5? It’s more of a polished evolution than a radical leap. Think going from an iPhone 13 to an iPhone 15 Pro Max—sleeker, faster, with smarter features, but not a reinvention.

Here’s where I think the hype meets reality:

• Yes, GPT-5 is better. You’ll notice faster responses, better reasoning, and smarter integrations right away.
• No, it’s not magic. The leap isn’t as dramatic as some suggest—at least not yet.
• The AGI hype needs perspective. We’ve been here before with Web3, metaverse promises, and blockchain revolutions that fizzled (see my take on AI hype cycles).

That said, I can tell from my testing: it’s hard to go back to GPT-4 once you’ve used GPT-5 for a while. It feels like a more responsive, more intuitive assistant—one that’s inching closer to being a proper daily driver for professionals.

Where GPT-5 Actually Helps

While the headlines focus on bigger, brighter, and more human-like, the real question for most of us is: Where does GPT-5 actually make a difference in day-to-day work?

After using GPT-5 extensively across IT, writing, and research tasks, I’ve seen several clear advantages over GPT-4:

1. For Developers
With vibe coding and improved reasoning, GPT-5 doesn’t just spit out functional code—it writes in your preferred style, anticipates potential pitfalls, and suggests optimizations before you even ask. This means fewer rewrites and cleaner commits (see my breakdown on coding with AI here).

2. For Writers & Content Creators
From blog posts to video scripts, GPT-5 delivers clearer drafts that stay on-topic and require less editing. Its improved contextual memory also helps keep tone and style consistent across longer pieces—something GPT-4 often struggled with.

3. For Researchers & Analysts
Whether you’re summarizing dense reports or pulling insights from large datasets, GPT-5 processes and condenses information faster, with fewer hallucinations. This means you can spend more time thinking critically about the results instead of verifying every line (related: How AI can mislead if unchecked).

4. For Busy Professionals
The new Gmail and Google Calendar integrations mean you can draft emails, plan meetings, and generate follow-up notes—all without leaving ChatGPT. It’s not just answering questions; it’s helping you act on them.

5. For Long, Complex Requests
One of GPT-5’s most underrated upgrades is focus retention. Even in multi-step prompts, it stays on track instead of drifting into irrelevant tangents. This is especially valuable for IT workflows where one missed step can cause significant delays (see my AI adoption advice for IT leaders).

Bottom line: GPT-5 isn’t replacing knowledge workers anytime soon—but it shifts your workflow into a higher gear. You still need to steer; GPT-5 makes the road smoother and the trip faster.

You still need to drive the process. GPT-5 shifts it into a faster gear.

What This Means for IT Leaders & Businesses

For IT leaders, CIOs, and business owners, the GPT-5 release isn’t just another AI headline—it’s a signal that practical AI adoption is accelerating. But here’s the key takeaway: you don’t need to overhaul your entire stack tomorrow. Instead, now is the time to strategically experiment, govern, and integrate.

Here’s what I’d be thinking about right now:

1. Employee Readiness & Training
Your teams may have access to GPT-5 already, but do they know how to use it effectively—and securely? AI productivity gains come when staff are trained not just in prompt writing, but in understanding limitations, bias, and data privacy (see my AI job impact breakdown).

2. Governance & Compliance
Smarter AI doesn’t mean safer AI. Attackers will adapt to new models just as quickly as businesses adopt them (Gartner warns about AI security risks). Consider:

• Do you need a formal policy for AI-generated code and content?
• How will you audit AI-assisted decisions for compliance and accuracy?
• Who is responsible for AI usage oversight in your org?

3. Integration into Existing Workflows
With GPT-5’s new app integrations (Gmail, Google Calendar, and more to come), you can start embedding AI where your teams already work. Early pilot projects can uncover efficiency wins without a massive system change.

4. Cost-Benefit Analysis
Upgrading to GPT-5 Pro for your team could be a productivity multiplier—but you need to weigh it against licensing costs, usage limits, and security trade-offs. Many organizations are experimenting with a “center of excellence” model where a small AI-proficient group leads adoption and shares learnings internally.

5. Industry Benchmarking
If your competitors are using GPT-5 to speed up product development, improve customer support, or automate analysis, waiting too long could mean falling behind (see my Y2K-to-AI leadership lessons).

In short, GPT-5 is an opportunity, not a mandate. The businesses that will benefit most are those that experiment deliberately, govern wisely, and train their people first.

Are We Closer to AGI?

Whenever OpenAI drops a major update, the AGI (Artificial General Intelligence) conversation comes roaring back. GPT-5 is no exception—some headlines are already hinting that we’re on the brink of machines that “think” like humans (MIT Technology Review covers the debate).

But let’s pump the brakes.

OpenAI itself defines AGI as “highly autonomous systems that outperform humans at most economically valuable work” (source). GPT-5, while more capable than its predecessors, is still a specialized AI—brilliant at certain language and reasoning tasks, but not independently creative, curious, or self-motivated in the human sense.

Why GPT-5 Feels Closer to AGI

• Improved reasoning chains mean it can handle more complex, multi-step requests.
• Better memory and personalization make it feel more “aware” of you over time.
• Unified tool routing gives it the illusion of adaptability—choosing the best mode automatically.

Why We’re Still Not There

• GPT-5 has no true understanding of the world—only statistical associations.
• It lacks generalized goals outside of responding to prompts.
• It can still produce confident but factually wrong answers (hallucinations), even if less often than GPT-4 (I’ve covered the risks here).

In other words, GPT-5 is a powerful narrow AI with more polish and adaptability, but AGI remains a moving target—one that experts can’t agree will arrive in 5 years or 50 (see AI timeline debates from Stanford HAI).

The danger in treating GPT-5 like AGI is complacency: over-trusting its outputs, underestimating bias, and skipping human oversight. The real opportunity is using it as an augmented intelligence tool—boosting human decision-making without replacing it.

Final Thoughts

GPT-5 is, without question, the most capable version of ChatGPT yet. It’s faster, smarter, more focused, and—thanks to integrations—more useful in everyday workflows. For developers, writers, researchers, and IT leaders alike, the leap in practical productivity is real.

But it’s also important to keep perspective. GPT-5 isn’t magic, and it’s not AGI. Like every AI upgrade before it, the novelty will eventually fade, and what will matter most is how effectively you integrate it into your work. The real winners in this AI race won’t be the companies with the newest tools—they’ll be the ones that train their people, set strong governance, and adopt AI deliberately.

If you’re curious about where AI might head next, I’ve explored that in The Real History of AI: From Turing to Transformers and my breakdown of AI’s impact on the future of work.

In the meantime, GPT-5 is here, and it’s ready to work—if you are. Whether it becomes the iPhone moment for AI or just another step on the road to AGI will depend on how we, as users and leaders, choose to apply it.

Related Posts from Tech It From Me:

• Behind the Buzzwords: What Is a Large Language Model, Really?
• Will AI Really Replace All Mundane Jobs? My Take on Geoffrey Hinton’s Bold Claim
• The Real History of AI: From Turing to Transformers

(Feature image generated with the help of DALL-E.)

Rather than pushing for a single, centralized artificial general intelligence (AGI) — the “one AI to rule them all” approach — Zuckerberg outlined a bold new vision: a personalized super-AI for every individual.

Welcome to the age of personal superintelligence — and possibly, a new chapter in the AI arms race.

Tech It From Me

The Case for Personalized Superintelligence: Zuckerberg vs the AI Mainstream

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Not One AI, But Billions of Them

For years, the prevailing narrative in AI has been the pursuit of a single, centralized artificial general intelligence — the “superbrain” that could answer any question, solve any problem, and serve the entire planet. Think of it as the Google Search of intelligence — one source, everyone taps in.

Zuckerberg’s vision flips that model on its head. Instead of one AI serving billions of people, he imagines billions of AIs serving one person each.

“Why share a general-purpose brain,” the logic goes, “when you could have one that knows you better than anyone else?”

Your personal AI could:

• Understand your schedule, routines, and energy patterns
• Mirror your tone, vocabulary, and sense of humour in every message
• Automatically sync with your social feeds, work tools, and AR glasses

Meta’s infrastructure already provides the connective tissue for this vision. Between WhatsApp, Messenger, Instagram, and Threads, they have a global user base of over 3.2 billion people — and all the cross-platform data streams to feed a highly personalized model.

It’s a leap from generic intelligence to deeply contextual intelligence — closer to a lifelong digital twin than a disposable chatbot session.

And if that sounds like something ripped from a sci-fi script, it’s because it is. From Her to Black Mirror, the idea of a constant AI companion has been a recurring trope. The difference now? The infrastructure to make it real already exists — from large language models to wearable displays and even early brain-computer interface research.

Why This Is a Big Deal

Most of today’s best-known AI tools — ChatGPT, Google Gemini, Anthropic Claude — are brilliant generalists. They can write an essay, debug code, or explain quantum physics. But here’s the catch:

They don’t know you. Every conversation starts from scratch. You’re reintroducing yourself over and over, like meeting a stranger with amnesia.

Zuckerberg’s personalized superintelligence vision flips that model. Instead of an AI that knows everything in general, you’d have one that knows you in particular:

• Context-aware: It remembers your goals, tone, and preferences.
• Memory-rich: It recalls last week’s conversation about your upcoming trip or that tricky project at work.
• Proactive: It takes initiative — preparing a presentation before you ask, flagging conflicts in your schedule, or suggesting a weekend plan based on your past choices.

It’s not just a smarter assistant — it’s the beginnings of a second brain.

If you want a deeper understanding of how these systems work at the model level, I break that down in Behind the Buzzwords: What Is a Large Language Model, Really?.

The possibilities are huge:

• Summarizing your meetings while you’re still in them
• Writing reports in your voice and style
• Coaching your presentations in real time
• Helping your kids with homework based on their learning styles

But here’s the price tag for that level of personalization: your AI would need access to almost everything about you — your files, private messages, biometric data, location history, even your voice and facial ID.

That level of intimacy is both its superpower and its Achilles’ heel. With that much of “you” inside one system, the question becomes:

Who do you trust to hold that much of yourself?

Meta, Microsoft, Apple, Google… and OpenAI

Zuckerberg’s personal superintelligence pitch doesn’t exist in a vacuum. It’s part of a much bigger battle for AI dominance — one that will define not just who leads the market, but how we interact with machines for the next decade.

Here’s how the other tech titans are playing their cards:

• Microsoft is betting on workplace supremacy. Its Copilot is embedded across Excel, Word, Outlook, and Teams, making AI an invisible productivity layer in the daily workflow of millions. As I explored in Microsoft Joins the $4 Trillion Club, this AI-first pivot is already paying off in valuation and market share.
• Apple is leaning into privacy as a selling point. With Apple Intelligence, processing happens on-device whenever possible, minimizing cloud exposure and appealing to users who distrust big data collection.
• Google is trying to weave Gemini into everything — from Search to Gmail to Android — but its strategy is split between innovating for users and protecting its ad revenue machine.
• OpenAI is staying the course toward AGI — a single, universal intelligence that can do everything for everyone. I unpack the history and ambition behind this in The Real History of AI: From Turing to Transformers.

Meta’s gamble is that people won’t just want a chatbot that answers questions — they’ll want a lifelong AI companion. A digital twin that evolves with them, remembers their milestones, and adapts to their changing needs.

If Microsoft’s Copilot is a co-worker, Apple’s AI is a privacy-first concierge, and Google’s Gemini is a search-enhanced assistant, then Meta wants to be your second self — always learning, always present, and always in your pocket (or on your face via AR glasses).

Goodbye Open Source?

Here’s where Zuckerberg’s vision takes a sharp turn.

For years, Meta positioned itself as the champion of open-source AI, releasing LLaMA models that researchers, startups, and even competitors could adapt freely. That openness was a major differentiator from the walled gardens of OpenAI, Google, and Apple.

Now, Zuckerberg says that era is over.

The reason? “Safety.” Meta claims that keeping future models closed will reduce the risk of misuse — such as generating deepfakes, automated cyberattacks, or large-scale disinformation campaigns.

But critics aren’t buying the whole story. Some argue this pivot is less about safety and more about control — keeping Meta’s next-gen models exclusive to its ecosystem and locking in a competitive edge.

This debate isn’t new. The tension between innovation and control has been simmering for years, especially as AI becomes more powerful. I’ve explored similar dynamics in Watermarking AI: Will It Change the Way We Write Forever?, where we looked at how transparency tools can both protect users and stifle creativity.

The irony is hard to miss:

• Open source fuels innovation — countless AI breakthroughs came from publicly available research and models.
• Closed systems protect IP — and keep dangerous capabilities out of the wrong hands.

The question is whether Meta can convince the world that closing the door on open AI is a step toward safety, not monopoly.

The Future: Helpful or Creepy?

The idea of a personal AI that knows you inside out sounds like a dream to some — and a nightmare to others.

On one hand, it’s the ultimate productivity tool: a tireless assistant that anticipates your needs, manages your time, and even shields you from digital clutter. Imagine waking up to an AI that has already sorted your inbox, rescheduled your meeting to avoid traffic, and prepped a personalized workout based on your sleep data.

On the other hand, it’s the perfect surveillance device — one that sees, hears, and remembers everything you do. And once that data exists, the risk of misuse, hacking, or corporate overreach becomes very real.

We’ve already seen how technology can cross the line:

• Facial recognition systems are deployed without consent in public spaces
• Algorithmic bias is influencing job offers, policing, and even medical treatment
• Phishing campaigns that exploit personal data — something I break down in How Cybercriminals Really Get Your Info

This is where Zuckerberg’s vision straddles the line between empowering and manipulative.

A personal AI could feel like an ally — or like a Black Mirror episode waiting to happen. The difference will come down to:

• Governance: Who sets the rules for how these systems operate?
• Consent: Do users truly understand what they’re agreeing to?
• Control: Can you turn it off — and can you delete everything it knows about you?

Because once an AI becomes your “second self,” the stakes aren’t just technical. They’re personal.

Conclusion

Whether you’re fascinated or unnerved by Zuckerberg’s “AI for everyone” vision, one thing is clear: the AI race is no longer just about building smarter models — it’s about controlling the interface between humans and machines.

The real power isn’t in the code alone. It’s in who gets to mediate your decisions, shape your habits, and filter your reality.

The next decade will be defined by choices being made right now — in corporate boardrooms, in policy discussions, and in the way each of us adopts (or rejects) these tools.

If your company still treats AI as “just another chatbot,” it’s already behind. If you’re still thinking of AI as something separate from your daily life, you may be surprised how quickly it becomes woven into it — by choice or by default.

Because whether your AI lives in your phone, your glasses, or someday in your neural implant, the most important question will remain the same:

Is it working for you — or are you working for it?

(Feature image generated with the help of DALL-E)

This isn’t just a number. It’s a signal—and in this post, I’m breaking down why this moment matters, how Microsoft pulled it off, and what it tells us about where the tech world is headed next.

Tech It From Me

Chasing the $4 Trillion Club: Microsoft’s Moonshot & the Tech Power Shift

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Microsoft’s Massive Move: Why $4 Trillion Matters

This wasn’t a quick intraday pop or speculative hype rally.

Microsoft’s valuation crossed the $4T mark after blowout earnings, analyst upgrades, a ‘right-sizing exercise‘, and a surge of institutional investor confidence. It capped off a full-day rally that placed Microsoft at the center of a new tech era—one driven not by consumer hardware, but by enterprise-scale artificial intelligence.

To put it in context:

• Microsoft added $1.5 trillion in market value in just two years.
• That’s more than the entire market cap of Meta, Netflix, or Tesla.
• And they did it not by disrupting their core business, but by evolving it, with AI at the center.

The Four Pillars Behind Microsoft’s AI-Powered Ascent

Microsoft’s success isn’t accidental. It’s built on four tightly integrated pillars:

1. Azure: The AI Cloud Engine

Azure has gone from being the runner-up to AWS to becoming the go-to platform for AI workloads. With exclusive access to OpenAI’s models like GPT-4 Turbo, Azure is the foundation for developers building the next generation of intelligent apps.

2. Copilot: AI Where People Work

Microsoft embedded generative AI directly into Word, Excel, Outlook, Teams, and more. Users don’t have to learn a new tool—it’s just there, helping them write, build, and analyze faster. And it’s all tied to Microsoft 365, creating recurring revenue and deeper stickiness.

3. OpenAI Partnership: The Masterstroke

In 2023, Microsoft made a $10 billion bet on OpenAI. That gave them first-mover advantage and exclusive commercial rights—allowing them to integrate OpenAI’s best models into their cloud and productivity platforms before competitors could catch up.

4. Enterprise Trust: The Boring Superpower

Microsoft is the safe bet for CIOs. It already powers identity systems, compliance frameworks, and collaboration tools. So when Microsoft says, “Here’s an AI upgrade,” IT leaders don’t hesitate—they adopt.

These four pieces reinforce each other. Azure runs Copilot. Copilot uses OpenAI. OpenAI is hosted on Azure. And the enterprise already trusts Microsoft to deliver it securely.

Nvidia: The Engine Behind Microsoft’s AI Stack

Behind every Copilot interaction or Azure AI workload is something you don’t see—Nvidia’s GPUs.

Microsoft is now one of Nvidia’s biggest customers, relying on H100 and H200 chips for large-scale training and inference. This partnership is not transactional—it’s symbiotic.

• Nvidia provides the compute horsepower.
• Microsoft brings the platform, the customer base, and the integration.

Together, they form the backbone of enterprise AI—one supplying the engines, the other building the roads and vehicles.

What This Means for the Tech Industry

The $4T milestone signals a broader shift in tech leadership:

• Apple still dominates consumer hardware but has been slow to lead in enterprise AI.
• Amazon remains strong in cloud but hasn’t delivered a breakthrough AI platform.
• Google has raw AI talent but struggles with product rollout and clarity.

Microsoft? It’s shipping real, integrated, profitable AI today.

The playbook is simple but powerful:

“Don’t make people switch platforms. Just enhance what they already rely on.”

Where We’re Headed Next

We’re entering an era of enterprise intelligence scale, where companies succeed by deploying AI across every workflow, team, and business process.

And the winners will be those who:

• Integrate AI securely
• Deliver it at scale
• And do it without disrupting the user experience

Microsoft and Nvidia are doing precisely that. Others may follow, but the bar is now higher than ever.

Want the Full Breakdown?

You can listen to the full episode—“Chasing the $4 Trillion Club”—on your favorite podcast platform or right here on the site. I go deeper into:

• How Microsoft’s AI ecosystem works under the hood
• Why Nvidia is more than just a chip company
• What CIOs and IT leaders should take from all this

Let’s Talk

Are you using Microsoft’s AI tools yet? Has your business adopted Copilot or Azure OpenAI services?
Drop a comment or connect with me on LinkedIn—I’d love to hear how you’re navigating this new era of enterprise AI.

(Feature image generated with the help of DALL-E)

Tech It From Me

Behind the Buzzwords: What Is a Large Language Model (LLM), Really?

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We’ve all heard the phrase “large language model” (or LLM) thrown around lately. It shows up in meetings, on product pages, in keynote presentations—everywhere. And if you’ve nodded along while someone said, “We should be using an LLM for this,” but secretly wondered what they were talking about, this post is for you.

What Is a Large Language Model?

At its core, a large language model is a system trained to recognize patterns in human language. You give it a prompt, and it predicts what’s likely to come next based on everything it’s seen before—books, websites, documents, code, support tickets, Reddit threads…you name it.

The power of these models comes from their scale. GPT-4o, for example, has been trained on datasets containing hundreds of billions—if not trillions—of words. And because of that, it has seen nearly every way humans express themselves: formally, casually, helpfully, sarcastically, and everything in between.

So when you ask it a question, it doesn’t “think” like a person does. It uses statistical models to predict what a human-like response should sound like. That prediction engine is surprisingly good—so good, in fact, that many people start to mistake it for real understanding.

But LLMs don’t know anything. They just recognize patterns at scale.

Tokens, Transformers, and Embeddings (Explained Simply)

Let’s break down how this works.

Tokens

LLMs don’t read whole words. They read tokens—small bits of text like prefixes, roots, or suffixes. For example, the word “unbelievable” might be broken into “un,” “believ,” and “able.”

This helps models handle spelling mistakes, slang, and unfamiliar words. It also determines how much content the model can process at once. ChatGPT-4o has a token limit of 128,000 tokens—roughly the length of a 300-page book.

If you’ve ever had a long chat with an AI and noticed it “forgot” something you said earlier, it probably hit that token limit.

Transformers

Transformers are the engine of modern AI. Introduced in 2017 by Google, the Transformer architecture enables a model to comprehend context across large blocks of text. That’s why it can make sense of your whole paragraph instead of reacting word-by-word like older chatbots.

The transformer utilizes a technique called self-attention to emphasize key relationships. For example, in the sentence “She didn’t like the movie because it was too long,” the model needs to understand that “it” refers to “the movie.”

Without this architecture, LLMs wouldn’t be capable of the nuanced, responsive conversation we see today.

Embeddings

Embeddings are how the model understands meaning in math. Every word gets mapped into a multi-dimensional space. Words used in similar ways—like “nurse” and “doctor”—end up near each other.

This enables the model to respond to you with answers that are not only grammatically correct but also semantically relevant. It also powers many enterprise AI features like intelligent search, document similarity, and classification.

One famous embedding example: “king” – “man” + “woman” = “queen.”

Not Just Bigger—Smarter (With the Right Fit)

It’s easy to assume that bigger = better, but that’s not always true.

Yes, GPT-4o and Claude 3 Opus are incredibly powerful. However, they also require more computing power, are more expensive to run, and may introduce latency.

In many cases, a smaller model like Claude Instant or GPT-3.5 can be just as effective, especially when paired with tools like RAG (Retrieval-Augmented Generation). With RAG, the model pulls data from real sources (like your company docs or product manuals) in real time.

Think of it like this:

• Big model = “read everything once and make your best guess.”
• RAG-enhanced model = “go look it up before you answer.”

This is where AI becomes both useful and accurate.

How Businesses Are Using LLMs Today

You don’t need to be a tech company to get value from large language models. Every department has use cases:

• IT: Script writing, technical documentation, troubleshooting assistance
• HR: Job description creation, onboarding guides, policy summaries
• Marketing: Copywriting, brainstorming, SEO optimization
• Legal: Contract summaries, risk flagging, compliance checklists
• Finance: Invoice classification, expense categorization, data cleanup
• Customer Service: Chatbots, help desk responses, multilingual support

If your organization touches text (and let’s face it—whose doesn’t?), AI can help. But it needs the right governance, the right tooling, and clear human oversight.

Hallucinations Are Real. So Is Risk.

Despite how intelligent LLMs appear, they occasionally make mistakes. These errors—known as hallucinations—can be subtle or glaring. A model might misstate a fact, invent a statistic, or fabricate a source.

This is why the “intern” analogy works so well: treat the AI as a capable but untrustworthy assistant. Review everything it creates. And never put it in charge of final decisions without human validation.

Leaders should also consider:

• Data privacy: What are you sending to the model?
• Model bias: Is the output reinforcing stereotypes or gaps in training data?
• Reputation risk: What happens if it gives the wrong answer to a customer or partner?

Understanding how LLMs work helps you ask better questions and avoid costly mistakes.

Final Thoughts

Large language models aren’t magic. But they are one of the most important technologies of our time. And like any tool, they’re only useful when you understand how to use them.

You don’t need to code. You don’t need to build your own model. But you do need to be able to spot the difference between hype and real value. That’s the edge that separates reactive teams from future-ready ones.

Are you interested in learning more about the history of AI? Be sure to check out my article titled The Real History of AI: From Turing to Transformers.

Tech It From Me

From Y2K to AI: The Evolution of IT Departments

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Intro: What 30 Years in IT Taught Me

I started my IT career in the mid-1990s, back when “tech support” meant crawling under desks, configuring IRQs, and praying that Windows 95 wouldn’t crash during a presentation. Fast forward to today, and we’re dealing with cloud orchestration, cybersecurity frameworks, and AI-powered everything.

As I look back on nearly three decades of change, I ask a simple question:

Where is IT going next?

Breaking Into IT in the 90s

I got my start at New Horizons Computer Learning Centres in Calgary, thanks to a gentleman named Doug Thompson. Doug had the foresight to leave oil and gas and dive into tech — and he, along with a lady named Roma, gave me my first shot. I started as a computer instructor, which taught me how to explain complex things simply — a skill that’s served me every day since.

From there, I joined Shopplex, an early online shopping mall that eventually became Teilhard Technologies — a B2B dot-com company that grew fast… and failed faster. The dot-com crash was brutal, but it taught me the real difference between hype and value.

The 2000s: From Tech Support to Strategic Partner

The 2000s were the golden era of in-house IT maturity. We implemented Active Directory, stood up Exchange servers, and learned how to run IT like a service.
Frameworks like ITIL brought structure, while the rise of the PMO brought us into strategic business discussions.

IT wasn’t just about “fixing things” anymore.
We were optimizing workflows, leading digital transformations, and becoming trusted partners in growth.

2010s: Cloud, Mobility, and Outsourcing

Then came the cloud — and everything changed.

Suddenly, we weren’t racking servers… we were managing vendors.
Instead of configuring Exchange, we were deploying Office 365.
Instead of controlling every switch and firewall, we were negotiating SLAs with managed service providers.

Outsourcing ramped up. Help desks went offshore. Infrastructure teams got lean.
And while some organizations gained efficiency, others lost depth, context, and innovation capability.

The AI Era: Why I Call It Augmented Intelligence

I have a personal preference: I don’t call it Artificial Intelligence.
I call it Augmented Intelligence — because that’s what it should be.

AI shouldn’t replace us — it should amplify us.
It should free us from repetitive tasks, improve our insights, and help us move faster.
But too many companies don’t know how to implement it well. They’re buying the tools, but not building the culture.

Smart IT teams know: AI isn’t a magic bullet. It’s a powerful toolkit — and it needs leadership.

The Risk of Deprioritizing IT

Today, I’m seeing a troubling trend: IT is often treated like a cost center — not a value driver.

It’s being outsourced, minimized, and deprioritized in the name of efficiency.
But when you weaken your IT function, you don’t just save money — you lose agility, security, and internal innovation.

IT is your nervous system. If you neglect it, the whole organization suffers.

So What’s Next?

I believe the AI future is hybrid:

• Smaller, high-impact internal IT teams
• Strategic use of automation and AI
• Vendor partnerships managed by people who understand both tech and business
• And an IT function that’s not buried in service tickets, but sitting at the leadership table

The best companies won’t eliminate IT — they’ll elevate it.

Want the Full Story?

You can listen to the full episode of Tech It From Me – Episode 7 here:

Listen to the Episode »
Also available on Spotify, Apple Podcasts, and other platforms.

Join the Conversation

Have you lived through some of these changes in IT?
Are you navigating AI, automation, or a cloud migration right now?

I’d love to hear from you.

Email me at inspire@techitfromme.com
Or leave a comment wherever you listen to podcasts.

Tech It From Me

The Real History of AI: From Turing to Transformers

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Artificial Intelligence didn’t begin with ChatGPT — and it’s not just about futuristic robots or sci-fi fantasies. The story of AI spans nearly a century, marked by breakthroughs in logic, computing, and neuroscience that have gradually brought machines closer to mimicking human reasoning.

In this post, we’ll take a journey through the real history of AI — not the hype, but the turning points that shaped the field from its earliest days to the generative AI explosion we’re living through now. I’ll also reflect on how some of the concepts I’ve studied — from machine learning to AI ethics — tie back to these foundational ideas.

Let’s go.

The Birth of the Idea: Turing, Shannon, and the Dartmouth Spark

Our story begins in the early 20th century. Long before smartphones or neural networks, people like Alan Turing laid the groundwork with abstract ideas about computation. In 1936, Turing introduced the concept of a universal machine — what we now call the Turing Machine — which could simulate any other machine’s logic.

By 1950, he was asking the big question: Can machines think? His proposed answer — the now-famous Turing Test — became the first real framework for measuring machine intelligence.

Meanwhile, Claude Shannon was pioneering information theory, and John von Neumann laid out the architecture that modern computers still use today.

But the real milestone came in the summer of 1956, at the Dartmouth Conference. That’s where the term Artificial Intelligence was coined, and a bold proposal was made: every aspect of human intelligence could, in theory, be simulated by a machine.

It was a moment of genius — and hubris. The field of AI was born.

Early Wins and the First AI Winter

The years following Dartmouth were filled with excitement — and some surprisingly impressive early projects.

One standout was The Logic Theorist (1956), a program that could prove mathematical theorems, sometimes even better than the humans who originally wrote them.

Then there was ELIZA (1966), a text-based chatbot that simulated a therapist. While simple, it felt eerily human to many users, so much so that some formed emotional connections. That was one of the first ethical dilemmas AI ever presented: if a machine sounds intelligent, does that make it so?

But these systems couldn’t handle ambiguity or real-world complexity. Machine translation efforts failed spectacularly. By the 1970s, disillusionment set in, and funding dried up.

This period became known as the first AI winter, when progress slowed, skepticism rose, and trust was lost.

The Expert Systems Era: Smarts Without Learning

In the 1980s, AI staged a comeback through expert systems — software that encoded the decision-making logic of human experts.

One of the most well-known was MYCIN, a medical system that diagnosed infections. It performed well but wasn’t used in hospitals because it couldn’t explain why it made its decisions. That trust gap proved critical.

Expert systems experienced a brief surge in corporate environments. But they had a fatal flaw: they didn’t learn. Every rule had to be programmed by hand, and updating them became unsustainable. As complexity grew, these systems collapsed under their own weight, triggering the second AI winter.

Still, they taught us key lessons about explainability, scale, and the limits of static logic.

Machine Learning: Letting Data Do the Talking

Out of the second winter came a new idea: what if machines could learn from data instead of being told what to do?

This was the dawn of machine learning, and it shifted the game entirely.

Instead of hard-coding rules, you trained models on examples. Spam filters became adaptive. Fraud detection evolved to spot patterns, not just fixed thresholds. Recommendation engines got smarter.

Three factors made this possible in the 1990s and 2000s:

• More data, thanks to the internet
• Better algorithms, like support vector machines
• Faster processors, especially with GPUs

AI was no longer a lab curiosity. It was quietly reshaping industries — and setting the stage for something even bigger.

Deep Learning and the ImageNet Breakthrough

While traditional machine learning was powerful, it still required manual feature engineering — identifying what aspects of the data mattered.

Deep learning changed that.

Using neural networks with many layers, deep learning models could extract patterns from raw data. They didn’t need to be told what a cat looked like — they figured it out themselves.

The turning point came in 2012, when a model called AlexNet outperformed the competition in the ImageNet challenge, reducing error rates by half. The ingredients? Massive datasets, GPU acceleration, and new training techniques.

Suddenly, AI could:

• Recognize faces and objects
• Understand and transcribe speech
• Translate languages in real time
• Even detect tumours and drive cars

But deep learning had a dark side: it was accurate, but opaque. These “black box” systems couldn’t always explain their decisions — a challenge we’re still grappling with today.

Transformers and the Generative AI Revolution

Then came 2017.

Google published the now-legendary paper: “Attention Is All You Need.” It introduced the transformer architecture, and it changed everything.

Transformers could analyze entire sequences of text at once using self-attention, allowing them to understand language with greater context and accuracy than ever before.

This gave rise to:

• BERT (Google)
• GPT (OpenAI)
• And many others (Meta, Mistral, Anthropic)

By 2020, OpenAI had released GPT-3, and in late 2022, ChatGPT put generative AI in the hands of the public.

Now, AI could generate:

• Blog posts, emails, and reports
• Meeting notes and summaries
• Code, artwork, and even videos

Suddenly, AI wasn’t just something in the background. It was sitting at the table with us, helping us work, write, and create.

But this newfound power also raised urgent questions:

• What happens when AI makes things up?
• How do we deal with biased or misleading outputs?
• Who owns the content that AI generates?

Where We’re Headed: Two Possible Futures

Today, we’re standing at another inflection point — and two paths lie ahead.

1. Smarter, More Integrated AI

AI is becoming a layer in every tool we use — from Microsoft Copilot to Google Gemini. Multimodal models can now understand text, images, and audio together. Open-source models are gaining traction, making powerful tools more accessible.

This democratization is exciting, but it also makes responsible development and oversight more complicated.

2. Toward Artificial General Intelligence (AGI)

Some labs are pursuing AGI — machines that can reason across domains, much like humans. Others are skeptical, pointing out that today’s models still rely on pattern recognition, rather than genuine understanding.

Regardless of who’s right, one thing is clear: the risks are no longer theoretical. Deepfakes, misinformation, job displacement — they’re already here. That’s why policy, ethics, and public awareness matter more than ever.

Final Thoughts: What Should We Build?

We’ve gone from Turing’s abstract machines to today’s generative models that can mimic language, vision, and reasoning.

But the future of AI isn’t just about what we can build — it’s about what we should make.

Every leap in capability brings new blind spots. Let’s focus not just on pushing the boundaries of possibility, but on building systems that are transparent, trustworthy, and genuinely beneficial.

Because AI isn’t just a technical challenge. It’s a human one.

**Transcript** >> Click here to view or download the full episode transcript