MY TAKE: Are we ‘Super f**ked’ by AI — or finally able to take charge of what comes next? – Source: www.lastwatchdog.com

By Byron V. Acohido

When VC mogul Chris Sacca declared AI is the death knell for professional services, I flinched. Not because he’s wrong — but because it’s only half the story.

Related:  GenAI grows up – at RSAC 2025

As a journalist who’s covered multiple technology shifts from the inside, I’ve learned to distinguish hype from real inflection points.

The AI disruption isn’t theoretical. I saw it firsthand this spring at the RSAC 2025, the cybersecurity industry’s mega-conference in San Francisco. But what’s emerging isn’t just dislocation. It’s something deeper — dare I say, even hopeful.

In a widely circulated YouTube Short first posted in January, Sacca, the legendary venture capitalist, didn’t pull punches. The clip — viewed over a million times and shared across platforms — captures a quote that’s since become a touchpoint in AI discourse: “We are super f**ked.”

Appearing on the Tim Ferriss Show, he leaned into the kind of candor that only someone with billions under management can afford. He offered a bleak forecast for professional life in the age of generative AI. Asked whether he’d encourage his kids to go to law school, he laughed. Not just a “no”—but a hard no.

Distributing competence

Software coding, law, copywriting? ” All obsolete, thanks to agentic AI, he declared. Then, the kicker: “We are super f**ked.”

That line went viral. And it resonated. Because Sacca wasn’t just talking about automation. He was talking about professional dislocation—about the brutal efficiency with which generative AI is eroding entire middle-class career paths.

And let’s be honest: he’s not wrong. A lot of systems are being exposed. A lot of professions are being compressed.

But here’s where I disagree: that’s not the whole story.

Because alongside the disruption, there’s a parallel force rising. A kind of distributed competence. A grassroots curveball that the incumbents—Sacca included—may be underestimating.

Clearly, in just a couple of years, generative AI has fractured the foundation of the knowledge economy. Many of us — myself included — share Sacca’s unease about where this leads. There’s no shortage of evidence that the sky, indeed, may be falling.

AI as scaffolding

But I’ve been reporting from a different vantage point — not the glass towers of venture capital, but the operational trenches. Specifically, in the cybersecurity and infrastructure sectors where AI adoption isn’t hypothetical. It’s already happening. And what I’m seeing tells a more layered, more surprising story.

This spring, I spent several days embedded at the RSA Conference in San Francisco. The usual buzz of acronyms and funding chatter was there, but it was underscored by a sharper urgency: How do we integrate large language models into critical systems — without sacrificing visibility, control, or trust?

A grassroots push toward AI as infrastructure, not just interface — and one that many in the investment class, Sacca included, may be underestimating.

I’ve seen it firsthand. In the enterprise security world, AI isn’t replacing human decision-making — it’s scaffolding it.

At Simbian, agentic AI now triages thousands of security alerts in real time, allowing humans to focus on actual risk. Their AI SOC Agent autonomously investigates and responds to alerts 24/7, learning from organizational context and analyst feedback to reduce mean time to respond by up to 5x.

At Corelight, AI powers flexible decision modeling, replacing brittle, hardcoded workflows with adaptive logic. Their Guided Triage feature simplifies complex network data into plain-language summaries, accelerating threat investigations and reducing analyst fatigue.

At Anetac, identity permissions are continuously mapped — enabling teams to spot lateral movement paths before attackers do. Their platform provides real-time visibility into service accounts and privilege chains, helping organizations identify dormant or over-privileged accounts that could be exploited.

Even Salesforce is quietly piloting AI-driven containment tools, automating policy decisions in flight — without waiting for human bottlenecks to clear. Their Einstein Trust Layer ensures data privacy and security by incorporating features like data masking and zero data retention, allowing AI to operate within strict compliance frameworks.

Kristen’s quest

In each case, what’s most striking isn’t displacement. It’s elevation. These aren’t stories of job loss. They’re stories of judgment — sharpened, not sidelined.

And this pattern isn’t limited to security operations or corporate infrastructure. It’s showing up in much more personal places — sometimes in families, living rooms, and unexpected quests for identity.

Ferguson

Take Kristin Ferguson. A professional musician. My talented daughter-in-law, who lives alongside Apple Tree Cove, here in Kingston, Wash. Kristen found herself at the center of a family effort to help her mother, Lynne Ferguson, and Aunty Jocelyn, obtain Greek citizenship after Italy tightened its dual nationality rules. With only fragmentary oral history and scattered documents about her great-grandfather — a stowaway from Smyrna — Kristin turned to ChatGPT.

The breakthroughs came quickly — and they ran deep. With ChatGPT as her research partner, Kristen began piecing together a far more detailed picture of her great-grandfather’s journey. She used contextual clues — like his father’s job as a schoolteacher and the presence of a basement stocked with alcohol — to infer social class and narrow her focus to parts of Smyrna where formal records might exist. From there, she built a case around family structure and geography.

ChatGPT helped her interpret complex DNA results, specifically Y-chromosome data tied to a rare haplogroup passed down from her grandfather. Based on that analysis — and a surprising 97% Aegean Islander match from a distant cousin — the model pointed her to three specific islands with longstanding ties to Smyrna, redirecting her search to relevant archives she might otherwise have overlooked.

Methodical prompting

It also became her translator and legal aide. Kristen fed the model screenshots of Greek-language census forms and registry pages — documents her relatives found difficult to decipher. ChatGPT returned accurate translations, explained Greek bureaucratic norms, and even helped her draft inquiry letters to multiple archival agencies. The letters included culturally appropriate phrasing and structure, right down to the inclusion of her mother’s name and her own middle name — a nod to naming conventions common in Greek society.

At one point, when Aunt Jocelyn worried that Lynne’s lack of travel to Greece might hurt their application, Kristen asked ChatGPT directly. The model not only clarified that travel history was irrelevant under Article 11 of Greek nationality law — it cited the legal logic and recommended how to reframe that concern in correspondence with the consulate.

She even used it to simulate the interview itself — creating a list of practice questions in Greek to help her mother prepare. This wasn’t shortcut work. It was excavation. A methodical, intelligent inquiry — one that moved faster and more thoroughly because Kristen, not the machine, stayed in charge of the process.

Talented and clever as she is, Kristen is not alone.

Preserving presence

In India, a tenant faced challenges recovering a Rs 35,000 security deposit from an unresponsive landlord. Turning to ChatGPT, the tenant crafted a compelling and legally sound letter, which prompted the landlord to return the deposit. This incident sparked discussions online about the potential of AI tools in resolving minor legal disputes, especially for individuals without easy access to legal aid. economictimes.indiatimes.com

In Seattle, Madi Young, an autistic consultant, figured out how to utilize ChatGPT for therapeutic conversations and as a brainstorming companion. ChatGPT has become a valuable tool for many autistic individuals who find social interactions challenging, offering support in daily routines, expressing emotions, and navigating conflicts. ChatGPT’s constant availability contrasts with limited access to traditional mental health services. wired.com

In Berlin, Michael Bommer, a 61-year-old software designer diagnosed with terminal colon cancer, saw in AI a way to transcend mortality. With limited time left, Bommer partnered with the startup Eternos.life to create a digital replica of himself — one that could continue offering comfort, stories, and even advice to his loved ones after he was gone.

Bommer

To do this, Bommer recorded over 150 personal anecdotes, capturing everything from childhood memories to his deepest values. He also read aloud 300 scripted phrases to help train a neural model in his unique vocal tone and cadence. The goal wasn’t to create a chatbot version of himself that could talk about the weather — it was to preserve the emotional essence of his voice, his humor, his way of responding to life’s questions.

This AI-powered replica could answer questions like, “What was your favorite memory with Mom?” or “What would you say about this decision I’m facing?” Not with generic prompts, but with phrases and reflections grounded in the specific stories he had chosen to record. His wife, Anett, said she found genuine comfort in the experience — sometimes asking “Michael” to read her poetry, sometimes just listening to him recount moments from their past.

The technology isn’t perfect. It doesn’t learn or evolve beyond the data it was given. But it offers something that felt impossible just a few years ago: a digital continuation of presence. “For Bommer, this wasn’t about achieving immortality. It was about preserving presence — a quiet form of agency. And for his family, it’s become a quiet miracle — a way to keep listening, even after goodbye.

Epistemological shift

What these use cases make clear—from enterprise pilots to kitchen-table hacks—is this: The intelligence isn’t in the system alone. It’s in how we wield it.

Yes, agentic AI is compressing rote work. But it’s also surfacing what we actually value: judgment, timing, context. The traits automation can’t replicate—only spotlight.

And some of the most meaningful uses aren’t coming from technologists. They’re coming from people on the margins—tenants, autistic consultants, terminally ill creators—using AI not to scale, but to reclaim. Time. Dignity. Connection.

That shift is quiet, but profound. Because while it’s tempting to fixate on what AI does, the real disruption is in what humans now believe is possible.

This isn’t just a technical leap. It’s an epistemological shift—a reordering of how knowledge is created and trusted. Trust—not just output—is the terrain being redrawn. And nowhere is that more visible than in how we define agency.

Ours to shape

Sacca sees a decimation event—job loss climbing the pyramid fast. Fair. But that assumes value lies in task execution.

What I’m seeing? A shift. From output to judgment. From doing to discerning.

At NTT Data, engineers are training AI to redact live video—not just based on what it sees, but why it’s being used. That’s not automation. That’s interpretation.
And interpretation implies intention.

We’re not just building tools. We’re building collaborators. Which means the nature of work is evolving—not vanishing.

So the real question isn’t: Is AI coming for our jobs? It’s: Will we design systems that support human agency—or replace it?

The window’s closing. Speed is winning. Nuance is at risk.

But what I saw at RSA gives me hope—not because vendors were cautious, but because they were deliberate. This isn’t just technical. It’s moral. It’s human. It’s ours to shape.

My daughter-in-law’s quietly fierce initiative makes that clear. Kristen didn’t just use AI. She guided it.

That’s not just survival. That’s agency. I’ll keep watch – and keep reporting.

Pulitzer Prize-winning business journalist Byron V. Acohido is dedicated to fostering public awareness about how to make the Internet as private and secure as it ought to be.

(Editor’s note: A machine assisted in creating this content. I used ChatGPT-4o to accelerate research, to scale correlations, to distill complex observations and to tighten structure, grammar, and syntax. The analysis and conclusions are entirely my own—drawn from lived experience and editorial judgment honed over decades of investigative reporting.)

June 3rd, 2025 | My Take | Top Stories