We’ve all heard the refrain: AI is coming for your job. In cybersecurity, the conversation has a sharper edge — analysts, SOC operators, and managed services providers live in a world already defined by automation, scale and razor-thin margins. This point was honed in a recent series of posts I happened on, on LinkedIn, involving Chris Hoff and Atul Tiwary.

Here’s the reality in this exchange: The dynamics at play in cyber are not unique. Many of the arguments that Atul Tiwary and Chris Hoff recently debated online could just as easily be applied to IT writ large — or even beyond IT, to any sector where services delivered by humans make up a heavy slice of the cost structure.

That’s the heart of Tiwary’s framing. Tiwary, formerly of Barracuda Networks and now a venture principal and board member, described the phenomenon as “Service as Software” — a flip of the familiar SaaS acronym that points to a fundamental shift. Instead of hiring more humans to deliver incremental services, organizations are looking at whether AI can deliver those same services as software: infinitely scalable, lower cost, always on. Chris Hoff, one of the security industry’s sharper and more skeptical voices (and someone I’ve known for over 20 years), acknowledges the turn of phrase but challenges its substance.

So, does “Service as Software” hold water? Let’s break it down.

Tiwary’s Case: A Labor Market About to Be Repriced

Tiwary points out that the global cybersecurity market is worth about $250 billion, growing at 10% annually, and still tilted 60% toward services. The logic is straightforward: If AI can automate SOC workflows from Level 1 to Level 3, then the very foundation of that services-heavy cost structure begins to crumble.

• Level 1 triage? What once took minutes is now compressed to seconds with automated enrichment and routing.
• Level 2 correlation and remediation? AI-driven playbooks run tirelessly, day and night.
• Level 3 investigations? Autonomous agents can synthesize global threat intel in real time.

The outcome, according to Tiwary, is an economic reset: Gross margins expanding by 1,000–1,500 basis points, operating leverage improving, free cash flow conversion strengthening. Service businesses start to look like software businesses, only with embedded trust and contractual resilience that MSSPs already enjoy.

And it’s not just theory — he cites a string of well-funded startups and acquisitions (Dropzone AI, BlinkOps, Exaforce, ReliaQuest and more) as evidence that investors are voting with their wallets.

Hoff’s Counter: Hype, FOMO and Unqualified Claims

Chris Hoff, never one to mince words, takes a dimmer view. He argues that Tiwary’s post reads less like analysis and more like inevitability — designed to spark FOMO among investors and executives.

Yes, “Service as Software” is a clever phrase, but Hoff bristles at the way “agentic AI” is invoked as if it’s already a settled, mature category. He reminds us that this isn’t some radical new direction — we’ve been on the automation journey for decades, from the codification of security to the rise of cloud-based SOC tooling. GenAI is an iteration, not a revolution.

And with each iteration comes risk. Automation without full agency can create as many headaches as it solves. Hiring people who understand how to wield GenAI responsibly may actually increase costs — try finding someone who can wrangle KQL, no-code workflows, and privileged AI swarms without commanding a premium salary. Hoff also notes the hype factor: Everyone is slapping the “AI” label on what they’ve been doing for years, muddying the signal with noise.

His skepticism boils down to this: Sweeping claims about labor repricing may describe how MSSPs operate at scale, but they don’t reflect the messy reality of the 80% of SOCs under the curve.

My Take: Somewhere Between the Lines

Here’s where I land. I don’t think SOC analysts are any more irreplaceable than many other highly skilled IT workers. Yes, some of them are going to lose their jobs to AI. But not today, and not all at once.

Agentic AI — whatever we mean by that term this week — is not yet the soup-to-nuts operator its proponents would like us to believe. In fact, in its current state, it often creates more work, not less. Think of an overeager intern: Fast, tireless, occasionally brilliant, but also prone to mistakes that a senior analyst then has to correct. That slows things down, not speeds them up.

But here’s the catch: Every day, it gets a little better. And in technology, “a little better every day” compounds very quickly. What looks clumsy now may feel seamless tomorrow. That means the repricing of labor that Tiwary describes isn’t some fantasy — it’s a trajectory.

For digital transformation leaders, the real challenge is timing. Knowing when to pull the lever — to move from human-delivered service to AI-delivered software — could determine whether your initiative succeeds or fails. Move too early, and you bog down your experts with AI babysitting duties. Move too late, and your competitors will be scaling faster, cheaper and with better margins.

Beyond Cyber: A Broader Pattern

While this conversation centers on cybersecurity, the implications are much broader. Customer service, HR, legal research, financial services — any domain where human expertise is packaged and delivered as repeatable service is ripe for automation. That doesn’t mean we’ll see mass obsolescence overnight. What it does mean is that executives across industries should be pressure-testing their service models.

Ask yourself:

• What portion of our services could be delivered as software without eroding trust or quality?
• How much margin expansion would that unlock?
• What reskilling or upskilling would our workforce require to partner with AI instead of being displaced by it?

These are not abstract strategy questions anymore. They’re operational imperatives.

Final Word

Atul Tiwary is right that the economic incentives here are massive. Chris Hoff is right that the hype machine is moving faster than reality. My view? Both perspectives matter.

The future of “Service as Software” won’t be defined by clever turns of phrase or venture funding announcements. It will be defined by the daily grind of adoption, iteration and timing. AI will replace people in some functions. It will augment people in others. And in almost every case, the transition will be bumpier, slower and more uneven than the slides in a pitch deck suggest.

But make no mistake: Repricing human labor through AI-delivered services is coming. The only real question for digital leaders is whether you’ll be ahead of that curve — or under it.