By the mid-2000s, it became about scaling networks and integrating ERP systems. Today, in 2025, the role of IT has transformed once again — from a support function to the strategic core of organizational resilience.

The next decade will accelerate this evolution. Organizations that thrive in 2030 will do so because they’ve aligned around a new IT North Star built on three critical pillars:

• Always-On Operations – technology services that never sleep, protecting revenue and customer experience.
• Data Trust – ensuring information is accurate, ethical, and transparent in an era of AI-driven decision-making.
• Security by Design – embedding protection into every layer of architecture, process, and culture.

These aren’t buzzwords. They are survival strategies for a digital economy that demands both speed and resilience.

Why Organizations Need a New North Star

History shows that IT is in constant flux. From the Y2K scare to the dot-com bubble, and on through cloud migration to today’s AI revolution, IT has reshaped itself again and again (see the full evolution here). What’s different now is the scale and velocity of change.

Digital systems are no longer just operational backbones—they’re mission-critical assets. Downtime isn’t just inconvenient; it’s destabilizing. In 2024, the average minute of downtime cost $14,056.

According to an article from Zenduty, for large enterprises, that number hit $23,750 per minute, with some Fortune 500s exceeding $5 million per minute in losses, totalling a staggering $400 billion annually across Global 2000 firms.

A recent resilience survey, as per Cockroach Labs, revealed that every tech executive surveyed experienced outage‐related losses, with individual incidents shattering expectations—losses ranged from $10,000 to upwards of $1 million, and average annual downtime was 86 hours.

That’s why the market now ranks resilience, trust, and security as competitive differentiators. The meteoric valuations behind Nvidia’s AI boom, Microsoft’s $4 trillion milestone, and strategic moves like Meta’s $10B Google Cloud deal aren’t just about innovation—they’re baked on consistent, trusted, secure service.

The days of IT as a back-office function are over. Today, technology decisions are business decisions. For leaders, the challenge is no longer whether IT should be at the table, but how to ensure it’s setting the direction.

Always-On Ops: Moving Beyond “Keeping the Lights On”

A decade ago, downtime was measured in hours. Customers might grumble, employees might lose productivity, but operations eventually resumed. Today, just five minutes of downtime can cost large enterprises millions of dollars. Gartner estimates the average cost of IT downtime at $5,600 per minute — and that figure continues to climb.

“Always-On Ops” isn’t just about uptime; it’s about building resilience and continuity into the DNA of every system.

That means:

Cloud and Hybrid Infrastructure

Cloud platforms enable global scale, but hybrid approaches remain critical. Many enterprises are learning the hard way that relying on a single hyperscaler creates lock-in and resilience risks. Deals like the Meta–Google Cloud partnership highlight the pressure to balance performance, cost, and redundancy (see analysis).

Automation and AIOps

Human IT teams alone can’t keep up with the complexity of today’s infrastructures. AIOps tools are now essential, using machine learning to detect anomalies, auto-remediate issues, and predict failures before they cascade. This is the evolution of monitoring from reactive alerts to predictive intelligence.

Redundancy and Failover by Design

True resilience means assuming failure will happen. Whether it’s global CDN networks, redundant cloud zones, or decentralized architectures like direct-to-cell satellite coverage (see SpaceX’s spectrum strategy), organizations must build systems where a single point of failure no longer exists.

Uptime alone is no longer the measure of resilience. The real test is whether systems can adapt, recover, and preserve trust when failure is inevitable. That’s where the conversation turns to data.

Data Trust: The Most Valuable Currency of the Digital Economy

In 2025, every organization claims to be “data-driven.” But without trust, data is less an asset than a liability. Poor quality, siloed, or manipulated information doesn’t just slow down decision-making — it actively misleads. According to IBM, bad data costs U.S. companies over $3 trillion annually, through wasted time, compliance errors, and misguided strategy.

The explosion of generative AI has only raised the stakes. As I’ve written in the real history of AI, the value of advanced models depends entirely on the integrity of the data they ingest. Even the most sophisticated large language models will amplify bias, hallucinations, or misinformation if governance is weak.

Building data trust requires attention to three core dimensions:

• Accuracy – ensuring information reflects reality, with rigorous validation and cleansing.
• Transparency – making data sources, transformations, and AI training processes auditable and explainable. This is why debates around AI watermarking are central to public trust.
• Ethics – protecting privacy, reducing bias, and complying with regulations like GDPR, CCPA, and Canada’s evolving digital policies (see the digital tax debate).

Why does this matter? Look at how Anthropic’s $183 billion valuation hinged on confidence in its ability to deliver safe, trusted AI. Conversely, breaches remain one of the fastest ways to destroy trust. As I explained in how cybercriminals really get your info, attackers are becoming more creative in stealing and weaponizing personal data. A single breach can erase years of brand equity overnight.

When data can’t be trusted, neither can the decisions built on it. That’s why accuracy, transparency, and ethics in data management aren’t optional — they’re the foundation of competitive advantage.

Security by Design: From Afterthought to Core Strategy

For years, security was an afterthought — added at the end of a project in the form of firewalls, antivirus tools, or compliance audits. That worked when threats were slower to evolve.

But in today’s environment, where attackers are agile and automated, bolting on protection at the last minute is no longer viable. Security must now be designed into every layer of IT, from infrastructure to application code.

Zero Trust and Identity-First Security
The old model of a hardened perimeter has collapsed. Employees work remotely, data lives in multiple clouds, and applications talk to each other through APIs. The only effective way forward is Zero Trust — assuming no user, device, or transaction is safe until verified. Identity has become the new perimeter, and continuous verification replaces the static password.

AI as a Double-Edged Sword
Artificial intelligence is amplifying both sides of the security equation. Attackers are using generative AI to create convincing deepfakes, scale phishing campaigns, and probe systems faster than ever before (see my breakdown of AI-generated scams). But defenders are responding in kind, deploying AI-driven tools to spot anomalies, triage alerts, and automate response. The result is an escalating arms race.

Embedding Security in Development
Software no longer ships once every few years. Continuous integration and delivery pipelines mean new code is moving into production daily, sometimes hourly. That makes DevSecOps essential: embedding code scans, penetration tests, and policy checks directly into the development process so vulnerabilities are caught before they reach customers.

Regulators, Insurers, and Customers Are Watching
The push for security by design isn’t just about fending off hackers. Insurers are raising premiums for companies with weak controls. Regulators are tightening privacy and cybersecurity requirements worldwide. And customers themselves increasingly demand proof of strong security before signing contracts. Security has become a trust signal — a differentiator in the marketplace as much as a safeguard.

Security Is Now a Culture
The most important shift is cultural. Security is no longer a department tucked away in IT. It has to be embedded into the way organizations design products, serve customers, and make decisions. In the next decade, the organizations that thrive won’t just have secure systems — they’ll have a security mindset built into every part of the business.

Connecting the Three Pillars: Why They Work Together

It’s tempting to treat Always-On Ops, Data Trust, and Security by Design as three separate workstreams, each with its own tools, budgets, and owners. But in practice, they are inseparable. Weakness in one area quickly undermines the others.

Take uptime, for example. An organization that delivers flawless availability but neglects security isn’t truly resilient. All that “always-on” infrastructure simply provides more opportunity for attackers to exploit vulnerabilities. Similarly, Data Trust without resilience is hollow.

Accurate, well-governed information is useless if it becomes unavailable during an outage or a ransomware event. And Security by Design without transparency may keep threats out, but it risks alienating regulators, partners, and customers who increasingly demand to know how data is collected, stored, and used.

What these examples show is that the pillars don’t just complement each other — they depend on each other. Together, they form a system of resilience:

• Always-On Ops ensures continuity of service.
• Data Trust ensures continuity of confidence.
• Security by Design ensures continuity of protection.

Without one, the other two collapse. With all three working in concert, organizations create a durable foundation for innovation and growth.

This is why the three pillars should not be treated as line items on a budget or isolated IT projects. They represent a strategic philosophy: a commitment to building technology that is resilient, transparent, and secure by default.

For IT leaders, this integrated view is the true North Star for 2030 — not just keeping systems running, but ensuring the entire digital ecosystem supports the business with reliability, integrity, and trust.

Looking Ahead: The IT North Star for 2030

By 2030, IT will look very different from today. Networks will be more decentralized, AI will be embedded in operations and security, and quantum computing may begin to reshape the foundations of cryptography. The boundary between “business strategy” and “IT strategy” will have disappeared entirely.

Amid that change, the North Star for IT leaders remains constant:

• Always-On Ops to deliver resilience and continuity.
• Data Trust to enable confident decisions and safeguard integrity.
• Security by Design to protect organizations in an era of relentless threats.

These aren’t optional priorities — they are interdependent pillars that define whether technology supports growth or becomes a liability. Together, they form a compass for IT leaders navigating the decade ahead.

The real question for CIOs, CTOs, and technology executives is simple: Are our investments, culture, and strategy aligned with this North Star? Because the future won’t wait until 2030. The organizations that adapt now will not only withstand disruption — they’ll set the pace for competitors to follow.

Frequently Asked Questions (FAQ)

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

Earlier this week, SpaceX agreed to acquire spectrum licenses from EchoStar in a transaction valued at $17 billion dollars! It’s a deal that goes beyond satellites and rockets. This is a direct play at the wireless industry itself. And if successful, it could mark the beginning of a world where cell towers are no longer the primary gatekeepers of connectivity.

A Deal That Redraws the Map of Connectivity

The mechanics of the deal are straightforward on paper but the ripple-effect across the industry is massive. SpaceX will gain control of EchoStar’s AWS-4 and H-Block spectrum bands, along with 50 MHz of S-band frequencies in the U.S. and global Mobile Satellite Service (MSS) rights. These licenses are rare, tightly regulated, and extremely valuable because they dictate who is legally allowed to transmit signals over certain frequencies.

Unlike satellites or rockets, spectrum can’t be manufactured. Governments allocate it, auction it, and guard it as a national resource. Once claimed, it rarely changes hands. By securing these licenses, SpaceX has effectively bought a long-term stake in the invisible infrastructure of wireless communications.

EchoStar, weighed down by debt and regulatory pressures, found relief in this deal. SpaceX, flush with Starlink momentum and private investment, found an opportunity to secure spectrum at a price that would have been far higher if it came up for public auction again.

In short, EchoStar survives, SpaceX advances, and the rest of the telecom world takes notice.

Spectrum as Strategic Real Estate

To understand why this matters, think of spectrum as real estate — only instead of land, it’s invisible lanes of airwaves. Every call, text, and gigabyte of mobile data travels over one of these lanes. Some lanes are fast but short-range. Others stretch farther but carry less information.

The spectrum SpaceX just acquired is strategically important because it sits in what engineers call the “goldilocks zone.” These mid-band frequencies balance speed with reach, making them ideal for mobile broadband. Combined with MSS bands, which were specifically designed for space-to-ground communication, SpaceX now has a portfolio that blends the best of both worlds: the performance of mobile networks and the reach of satellites.

For over a century, telecom networks have been built the same way: towers connected by cables and fibre, expanded one region at a time. SpaceX’s new spectrum gives it the ability to challenge that model — delivering service not from the ground up, but from orbit down.

(For a recent parallel in tech valuations, see how Anthropic surged to a $183 billion valuation in AI — another industry betting on scarce, high-value resources.)

Direct-to-Cell: A Different Kind of Disruption

Starlink has already made waves by delivering broadband through dish terminals, serving more than 5 million customers across 100+ countries. But direct-to-cell is the next frontier.

Instead of relying on specialized hardware, direct-to-cell would allow ordinary smartphones to connect directly to Starlink satellites overhead. No dish. No modifications. Just the phone already in your pocket.

If SpaceX can scale this, the implications are enormous. Remote areas that carriers have long ignored could gain coverage overnight. Travelers could move across borders without worrying about roaming charges. Communities hit by hurricanes, wildfires, or earthquakes could stay connected when towers fail.

The first deployments will be modest: basic texting, emergency alerts, and low-bandwidth messaging. But over time, as the network grows and devices adapt, the vision is full-fledged broadband delivered directly from orbit to handsets. That would represent the most significant disruption to wireless connectivity since the invention of the cell tower itself.

For more on the telecom side of the equation, see T-Mobile’s direct-to-cell partnership announcement with SpaceX.

The Competitors Who Should Be Nervous

Traditional telecom carriers have reason to worry. Companies like AT&T, Verizon, Rogers, and Bell have poured billions into building towers, laying fibre, and lobbying regulators. Their business model depends on controlling coverage and pricing through physical infrastructure.

SpaceX, by contrast, is offering a way around that infrastructure. No tower? No problem. Satellites don’t care if you’re in downtown Toronto, the Grand Canyon, or the middle of the Atlantic.

The impact doesn’t stop with telcos. Cloud providers like AWS, Microsoft Azure, and Google Cloud have been investing heavily in partnerships with carriers to deliver 5G and edge computing. If SpaceX controls both the spectrum and the satellite delivery network, it could become a competitor instead of a customer.

Even the satellite industry itself feels the pressure. AST SpaceMobile and Lynk Global have demonstrated direct-to-phone connections, including live video calls and texting trials. But they lack the scale. SpaceX has thousands of satellites already in orbit — and now, the spectrum licenses to back them up.

Obstacles on the Path Ahead

As transformative as this vision is, it comes with real challenges.

Regulatory hurdles are at the top of the list. Spectrum transfers are subject to intense scrutiny, particularly in the U.S., where the FCC will want to ensure competition and prevent monopolization. Established carriers, with deep lobbying power, won’t make approval easy.

Finances are another concern. Along with spectrum, SpaceX is taking on EchoStar’s debt obligations — roughly $2 billion in interest payments through 2027. While SpaceX has strong momentum, it also has expensive ambitions: reusable rockets, Starship development, and Mars exploration all compete for resources.

Then there’s the technology itself. Smartphones were designed to talk to towers, not satellites traveling at orbital speed. Latency, interference, and seamless handoffs between satellites are not trivial problems. Early service will likely feel limited, and it could take years before it competes head-to-head with carrier-grade 5G.

Finally, the competitive landscape is shifting quickly. Apple already offers satellite-based emergency SOS on the iPhone, and other handset makers are exploring similar features. If these become standard, carriers and satellite startups alike could blur the lines before SpaceX reaches maturity with direct-to-cell.

Why This Deal Matters

This deal is bigger than SpaceX or EchoStar. It signals a shift in how we think about networks themselves. For more than a century, communications infrastructure has been built from the ground up: towers, cables, and regional rollouts. Now, the future may be built from space down.

If SpaceX succeeds, it will reshape who controls access to the internet, force telcos to rethink their models, and create a new class of competition that cuts across industries — from cloud to hardware to government regulation.

Connectivity is no longer a luxury. It’s a basic utility, as essential as electricity and clean water. This $17 billion deal is a bet that the next generation of that utility won’t be anchored to towers at all, but to satellites orbiting overhead.

And while you’re here — if this story makes you think about your own digital safety, check out our Password Breach Checker. It uses Have I Been Pwned’s database to see if your password has been exposed in a data breach. Because the future of connectivity also depends on trust and security.

Frequently Asked Questions (FAQ)

Final Thoughts

Elon Musk’s companies are known for making bold bets, and this may be one of the boldest yet. By acquiring spectrum worth $17 billion, SpaceX isn’t just building rockets or satellites. It’s staking a claim on the future of global wireless connectivity.

Whether this gamble pays off remains to be seen. But if history is any guide, it’s unwise to bet against SpaceX turning the impossible into the routine.

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

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.)

But here’s the reality: not all certifications pay off. A recent study by the Burning Glass Institute found that only 1 in 8 certificates result in significant pay increases within the first year.

Still, that doesn’t mean certifications are worthless. When chosen wisely, they can be the very edge you need in your next job search.

Tech It From Me

Do Certifications Really Help Your Career? The Truth About Credentials in 2025

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When Certifications Actually Pay Off

Despite headlines suggesting most credentials don’t deliver a big payday, the reality is that some certifications consistently open doors to higher salaries and better opportunities. The difference comes down to industry demand, skill relevance, and the type of credential pursued.

Healthcare: High Demand, High ROI

Healthcare is one of the clearest examples where certifications pay off. Credentials for nursing, radiology, and medical technicians not only improve hiring chances but also result in an average $5,000 salary increase within the first year (WSJ). With labour shortages across hospitals and clinics, employers actively seek certified professionals to fill critical roles.

IT & Cybersecurity: Job Market Winners

Technology certifications are another area with strong returns. Programs in cybersecurity, cloud computing, and data analytics continue to be among the most requested by employers. For example, graduates of the University of Colorado Boulder’s Cybersecurity Certificate had a job-switching rate nine percentage points higher than peers without the credential.

In an era where cybercriminals exploit weak security, certifications like CompTIA Security+, Certified Ethical Hacker (CEH), or AWS Cloud Practitioner show employers that candidates have validated, job-ready skills.

Business & Project Management: A Mixed Bag

Business-related certifications can also deliver benefits—but with nuance. Project Management Professional (PMP) and Six Sigma remain highly respected in operations and management roles. Employers often use these certifications as benchmarks for leadership potential.

However, not all business certificates carry weight. As the WSJ highlights, even a Harvard project management certificate didn’t guarantee significant salary gains. The lesson? Employers value certifications tied directly to in-demand skills over brand names alone.

Emerging Fields: Sustainability, AI & Data

Finally, emerging industries are starting to show strong certification ROI. Fields like green energy, artificial intelligence, and data science are experiencing rapid growth. Earning credentials in AI fundamentals or sustainability management may not provide an immediate pay bump. Still, they strategically position job seekers in industries projected to expand dramatically over the next decade.

Why Prestigious Certifications Don’t Always Pay Off

It’s easy to assume that a certificate stamped with the name of an Ivy League or other prestigious university will automatically fast-track your career. After all, who wouldn’t be impressed by “Harvard” or “Stanford” on a résumé? But the data shows otherwise: brand recognition doesn’t always translate into career growth.

The Wall Street Journal points out a surprising example—graduates of Harvard’s project management certificate program saw only a 3.7% improvement in advancement opportunities and no meaningful salary bump compared to peers without it (WSJ). That’s a disappointing return on what can be a significant financial investment.

This reflects a broader truth: employers don’t hire brand names—they hire skills. If the certification doesn’t demonstrate mastery of a skill or knowledge the company urgently needs, the prestige factor won’t matter.

Why Prestige Can Disappoint:

1. Misalignment with Job Market Demand – A certificate in a generalized field (like project management) may not stand out when thousands of other candidates have similar credentials. By contrast, specialized certifications in fields like cloud security or data analytics often translate directly into job postings.
2. High Cost, Low ROI – Big-name universities often charge a premium for certificates that don’t outperform community colleges, online providers, or employer-partnered programs. Workers may end up with more debt than opportunity.
3. Credential Inflation – As I discussed in From Y2K to AI: How IT Departments Have Changed, the labour market has shifted from valuing degrees alone to prioritizing practical, verifiable skills. Today, even advanced degrees are no longer enough without applied experience—certificates are subject to the same inflation.

What Matters Instead

Rather than focusing on prestige, professionals should prioritize:

• Industry Recognition – Certifications like CISSP in cybersecurity or PMP in project management are trusted benchmarks across industries, regardless of where they’re obtained.
• Employer Partnerships – Credentials co-created with employers (like Google’s Career Certificates or AWS training) carry weight because they map directly to job requirements.
• Relevance to Job Postings – A mid-career professional will get more traction from a specialized certificate in cloud computing than from a generic leadership program—even if the latter comes from a top school.

A recognizable university logo may look nice on LinkedIn, but employers care about whether your certification proves you can solve their problems.

Certifications as Evidence—Not Magic

Getting a certification doesn’t guarantee you a new job, promotion, or pay raise. Instead, think of certifications as evidence of your skills and initiative—proof points that employers can see at a glance. They can help you get in the door, start a conversation, and stand out in a stack of resumes.

But on their own, they aren’t enough.

Why Certifications Matter

• Skill Validation – A certificate demonstrates that you’ve invested time in learning a new competency. For a hiring manager, this can be the deciding factor between two candidates with similar résumés.
• Career Signal – Certifications show initiative. They signal to employers that you’re committed to continuous learning—something essential in fast-moving fields like IT and AI.
• Resume Boost – In competitive job markets, certifications can get your résumé past automated filters (ATS systems), which often scan for keywords like “AWS Certified,” “PMP,” or “CompTIA Security+.”

Why Certifications Alone Aren’t Enough

Employers aren’t hiring the certificate—they’re hiring you. Without the ability to apply that knowledge in real situations, a credential is just a line on paper. As I discussed in Behind the Buzzwords: What Is a Large Language Model, Really?, knowledge without application often falls flat. The same goes for credentials.

To make certifications work for you:

• Pair them with projects – If you earn a cloud computing certificate, spin up a real AWS or Azure environment and document what you’ve built.
• Tell the story in interviews – Don’t just say “I have a PMP.” Instead, explain how project management principles helped you lead a team to deliver results.
• Keep them updated – Tech evolves fast. Certifications that were hot five years ago may already be obsolete.

The Real Value: Competitive Edge

Certifications give you an edge—but they’re part of a larger toolkit that includes experience, networking, and adaptability. As the Bureau of Labor Statistics notes, the fastest-growing roles often require both formal training and on-the-job practice.

In short, certifications are not magic bullets. But used strategically—as proof of skills, initiative, and adaptability—they can set you apart from the competition and help you land opportunities that may otherwise be out of reach.

How to Pick the Right Certification

With hundreds of thousands of certifications on the market, the real challenge isn’t earning one—it’s choosing wisely. Too many job seekers waste time and money on credentials that sound impressive but don’t improve their career prospects. Here’s how to make sure yours does.

1. Research ROI Before You Enroll

Not every certification delivers a financial return. According to the Burning Glass Institute, only about 1 in 8 certificates leads to significant pay increases within the first year. Before signing up, check whether employers recognize your target certification and whether it translates into real-world salary growth.

2. Scan Job Postings for Skill Demand

Look at the job descriptions for the roles you want. Do you consistently see mentions of AWS, PMP, SQL, or cybersecurity? Those keywords are a direct clue to the certifications that matter most. Aligning your credentials with in-demand skills dramatically increases your chances of getting noticed by recruiters and Applicant Tracking Systems (ATS).

(Pro tip: Tools like LinkedIn Jobs or Indeed can help you spot recurring skills.)

3. Look for Employer Partnerships

Programs created in collaboration with employers tend to carry more weight. For example, Google Career Certificates in fields like IT support or data analytics are co-designed with industry input, which makes them far more practical for hiring managers. Similarly, AWS and Microsoft certifications are widely trusted because they tie directly to the platforms companies already use.

4. Prioritize Relevance Over Prestige

As we discussed above, big-name universities don’t guarantee results. A community college cybersecurity certification or a CompTIA credential may be more valuable than an Ivy League badge—simply because they map directly to what employers are hiring for.

5. Stack Certifications Strategically

Don’t stop at one. Layering certifications can create a compelling profile:

• IT Example: CompTIA Security+ → AWS Solutions Architect → CISSP.
• Healthcare Example: Medical technician certificate → radiology specialization → advanced imaging credential.
  Each step signals career progression and growing expertise.

The Takeaway

Choosing the proper certification is less about prestige and more about employer demand, ROI, and practical application. If you align your learning path with industry needs and back it up with hands-on experience, certifications can become powerful tools for career growth.

Before you invest, ask yourself: Will this certification prove my skills in a way that employers value right now? If the answer is yes, you’re on the right track.

Related Reading:

• From Y2K to AI: How IT Departments Have Changed—and Where They’re Headed
• Will AI Really Replace All Mundane Jobs? My Take on Geoffrey Hinton’s Bold Claim
• Behind the Buzzwords: What Is a Large Language Model, Really?

(Feature image 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)

Every day, regular people—students, small business owners, retirees, even IT professionals—fall victim to cybercrime. And it’s not because they’re stupid, careless, or unlucky. It’s because today’s cyberattacks are automated, convincing, and wildly effective.

In this post, I’m breaking down how cybercriminals get your info, based on real-world experience from almost three decades in IT leadership roles. I’ll also give you 5 simple things you can do today to reduce your risk—no technical expertise required.

Tech It From Me

Tech It to the Bank: How Cybercriminals Really Get Your Info

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The Most Common Ways Hackers Steal Your Data

Let’s ditch the Hollywood imagery. Nobody is hammering away at your firewall by hand. Cybercriminals use automation to compromise thousands of people at once—and they’re incredibly efficient at it.

Here’s how:

1. Phishing

Fake emails or texts that mimic trusted brands like Amazon, PayPal, or Microsoft. They get you to click a link, log in, and boom—your credentials are stolen.

2. Social Engineering

You get a phone call from someone claiming to be IT support. Or HR. Or even your CEO. It sounds urgent. They need your info now. You panic. You give it.

3. Credential Stuffing

If you’ve reused the same password across multiple sites, this one’s for you. Hackers take stolen credentials from past breaches (like LinkedIn or Dropbox) and try them on hundreds of other platforms. It works a lot.

4. Fake Public Wi-Fi

Ever connected to “Free Airport Wi-Fi”? Sometimes it’s legit. Sometimes it’s a hacker’s laptop. Once you’re on their network, they can intercept your data.

5. Malicious Apps and Extensions

Shady mobile apps or browser extensions ask for more access than they need, then abuse it. They log your keystrokes, scan your clipboard, or upload your data without you knowing.

Why You’re More Vulnerable Than You Think

You don’t need to be rich or famous to get hacked. You need to:

• Reuse passwords
• Skip MFA
• Click one bad link
• Be on the wrong Wi-Fi network

Cybercriminals aren’t targeting you—they’re targeting everyone.

They run bots that test leaked credentials from old data breaches across platforms like Gmail, Amazon, and PayPal. If you’ve ever used the same password twice, you could be exposed.

Go check haveibeenpwned.com to see if your email has been in a breach. Spoiler: it probably has.

5 Things You Can Do to Protect Yourself

1. Use a Password Manager

I use 1Password—and I’m not compensated or affiliated with them in any way.

One of the reasons I use it is that it includes built-in support for MFA codes, so I don’t need a separate app like Google Authenticator. It syncs across all my devices and makes secure logins effortless.

Other great options include:

• Bitwarden
• Dashlane
• iCloud Keychain (for Apple-only users)

2. Turn on MFA

Multi-factor authentication (MFA) adds a second layer of protection to your accounts. If someone steals your password, they still need your phone or device to get in.

Turn this on for:

• Email
• Banking
• Social media
• Cloud storage

3. Stop Reusing Passwords

Every account should have a unique, strong password. If one gets compromised, the rest stay safe. Password managers make this easy.

4. Be Skeptical of Public Wi-Fi

Don’t trust open networks. They’re convenient, but risky. Avoid logging into sensitive accounts when you’re on them. I have a separate article that talks about issues around wi-fi, and how you can resolve them.

5. Use a VPN—Like Cloudflare WARP

A VPN encrypts your internet traffic, making it unreadable to snoops on public networks.

I personally use Cloudflare WARP. It’s free, fast, and easy to install—not just on your phone, but on your Mac or Windows PC too.

WARP isn’t a full VPN—it doesn’t spoof your location—but it does encrypt your data using Cloudflare’s secure network.
Perfect for cafés, airports, or anywhere with sketchy Wi-Fi.

I’ll be doing a full episode soon on VPNs: how they work, which ones to avoid, and how to choose the right one.

Lessons from the IT Trenches

As someone who’s spent their career in IT leadership, I’ve seen this stuff go sideways.

The most significant security breaches I’ve witnessed didn’t happen because of bad software.
They happened because of one person:

• Clicking a fake DocuSign email
• Reusing an old password
• Ignoring a security warning

Technology doesn’t get hacked—people do.

So my job as an IT leader is to make secure behaviour easier than insecure behaviour. That’s why I advocate for tools like 1Password and Cloudflare WARP. They reduce friction, and they encourage good habits.

One good decision can stop an entire breach chain.

Final Thoughts

Let’s recap:

• You are a target, even if you don’t feel like one.
• Cybercriminals count on laziness, reuse, and distraction.
• Password managers, MFA, and VPNs aren’t optional anymore—they’re essential.

You don’t have to fix everything overnight.
Just start with one thing.

Add MFA to your email.
Start using a password manager.
Try a VPN next time you’re on public Wi-Fi.

Security doesn’t have to be scary. It just has to be intentional.

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Listen to the full episode of Tech It From Me wherever you get your podcasts from.

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