🚨 Almost Got Scammed… By a Recruiter Who Was Too Good

Hey everyone,

I nearly fell for one yesterday.

Not a crypto rug-pull or a fake invoice — this was way more sophisticated. A recruiter reached out about a confidential Chief AI Officer role reporting directly to the CEO. The CEO? Someone I actually know. The positioning was flawless. The questions spot-on. The conversation felt natural, relevant, and genuinely exciting.

Everything checked out… except two tiny details I almost ignored:

- They were in no rush to jump on a call (unusual — most recruiters treat a phone call like it’s oxygen).
- Zero phone number in the signature.

After three emails, the alarm bells got loud enough. So I picked up the phone and called the CEO myself.

Her response?
“No… we’re not hiring for that.”

Just like that, the whole thing evaporated.

I lost nothing but a PDF of my (public) profile, an inflated pay range and a few minutes of my time. But it left me thinking:

What if I didn’t know the CEO?
What if they’d used an AI voice agent that sounded 100% human?
What if they had every detail perfectly scripted by tools we’re all still learning to use?

The social engineering was next-level. These people are masters of psychology. You can’t help but wonder — if they put this much talent into something legitimate, what could they build?

The bigger point though:

We can’t walk around assuming every message is a trap. That’s exhausting and depressing.

But we also can’t stay naive.

High vigilance is now table stakes:
- Notice the small things
- Ask the awkward questions
- Keep asking until the answers feel right
- Verify when something feels too perfect

The AI era isn’t just making builders more powerful — it’s making scammers terrifyingly good too.

Stay sharp out there. And if something feels off… trust that instinct and make the call.

#AIScams #RecruiterRedFlags #StayVigilant

Just published a new LinkedIn post introducing the SAFE Framework and its generic pseudocode for continuous monitoring in any agent-based environment.

You can read the full post here: https://www.linkedin.com/posts/davidrh_agenticai-aigovernance-responsibleai-share-7446376584741072896-yGXH?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAC11PUB6bXVB2Khjqo0ABlxtMcjJuzhmtw

The full commented pseudocode (with all adjustable parameters clearly marked) is attached.

Practical Considerations for Running It Continuously

Lightweight vs. Heavy Evaluation:
In production, you would split assessments:
-> Fast path (every few seconds/minutes): lightweight observability/controllability checks using live telemetry.
-> Heavy path (less frequent or on triggers): full stability/robustness simulations (Monte-Carlo, red-teaming) that may run in parallel or on a separate monitoring node.

Triggers for Re-evaluation (beyond the timed loop):
Model update or fine-tuning events
Drift detection (performance degradation, distribution shift)
High-severity disturbances or anomalies
Manual or scheduled reviews

Mitigation Actions (when score drops):
Automatic reduction of autonomy level (e.g., from Level 4 → Level 2), insertion of human-in-the-loop gates, rate limiting, or full pause.

Performance Impact:
The monitoring loop itself should have very low overhead if you hook into existing telemetry (tracing, logging, Prometheus metrics, etc.). Heavy simulations can be offloaded.

Multi-Agent / Hierarchical Support:
The same controller can run at the fleet/supervisory level to detect emergent instabilities across many agents.

How to Deploy It in a Real Agent-Based Environment

1. Start the SAFEController alongside your main agent runtime.
2. Launch enforce_safe_operation() in a background thread, process, or separate microservice.
3. Integrate the required adapters (_measure_reconstruction_rate(), _run_adversarial_stress_test(), etc.) with your platform’s tracing, simulation, and override APIs.
4. Load SAFEConfig from external files/databases so thresholds can be updated without code changes.

SAFE - Stability-Assured Framework for Entities # Generic, agent-agnostic implementation for any agent-based system

At its core, sovereign AI addresses the deeper question: who says what, when, for whom?

My recent article on OpenGov Asia

https://opengovasia.com/sovereign-ai-and-data-control-the-new-strategic-imperative-for-asia/?c=sg

Always eager to hear views. Otherwise, read! 😁

Sorry no 😂... using AI automation in your plant factory for sustainable vegetable production does not make the Vegetable AI 🤪

Let's talk about it.

https://www.linkedin.com/posts/davidrh_neurodiversity-neurodivergence-ai-ugcPost-7448547850185052160-WHw1

🌿 Nature and biology continue to blow my mind.

The hidden complexities, the elegant solutions, the “why this works” questions we usually overlook — it’s all quietly reshaping my thinking on every level.

This new piece is a direct outgrowth of that fascination: Predatory Transformation: Using nature’s evolutionary arms race to make real, lasting change in organisations.

Read it here

Curious what you think.

🚨 New Article Out

The rise of AI politics

Whether leaders admit it or not, we have entered the era of AI politics. AI has become a potent multiplier layered on top of classic state power — energy, resources, demographics and ideology. Compute, data, talent and energy are the new chokepoints.

From US national infrastructure buildout to China’s self-reliance, India’s inclusive growth model and middle powers hedging with sovereign AI — this is reshaping everything from productivity to defense and diplomacy.

🔗 Full article: https://www.waterstechnology.com/emerging-technologies/7953041/the-rise-of-ai-politics

Are you a fan of Dune, Warhammer, Foundation, or just generally cautious of post apocalyptic futures.

After meeting an ex regulator colleague, I could help diving into this thought experiment. What can we learn from sci-fi future.

https://www.linkedin.com/posts/davidrh_what-if-the-executives-you-see-on-a-video-activity-7450705111418396672-9ekB?utm_source=share&utm_medium=member_android&rcm=ACoAAAC11PUB6bXVB2Khjqo0ABlxtMcjJuzhmtw

🧐 Did you know there are more than 900 AI Governance frameworks today?

Most of them focus narrowly on the AI itself. Very few look at the full picture: the hybrid human-AI systems that actually make decisions in the real world.

Boards, risk officers, and regulators increasingly find themselves asking "Which framework do I follow?" instead of "How do I govern effectively?"

An attempt to address the gap. Work in progress 🙏

https://www.linkedin.com/pulse/seeking-feedback-whitepaper-global-agentic-ai-unified-hardoon-trfyc/?trackingId=KbZVL8csSjabqONxSBqZhQ%3D%3D

📡 Thoughts on Uploaded Intelligence after binge-watching Pantheon

I’ve been deep in Pantheon lately, and it’s genuinely rewired how I think about the future. While the world is obsessed with Artificial Intelligence right now, we’re seeing this wild re-genesis of Neural Computing (neuromorphic chips, spiking networks, the whole “brain-like” hardware revival under fresh branding). The show keeps pulling me toward the next frontier: Uploaded Intelligence (UI).

Mind uploading. A full human consciousness, scanned and running in silicon.

Storage-wise? We’re already good. The human brain has roughly 86 billion neurons and ~100 trillion synapses. High-resolution connectome mapping (the wiring diagram) is estimated at a few petabytes for the static structure. Totally manageable on today’s data-center scale. Even with dynamic state (all the electrochemical chatter), we’re talking exabytes at worst. Disk space isn’t the bottleneck.

The real question is compute.

What does it actually take to run a brain in real time? The biological version sips just ~20 watts while delivering insane parallelism. Replicating that neural network density in hardware, not just abstract deep-learning layers, but something that faithfully mirrors the brain’s messy, recurrent, spiking architecture, is a whole different game.

If we could build a system with comparable synaptic density and fidelity, could we then upload the actual synaptic firing patterns? The dynamic “software” state that is you at any given moment?

And here’s the million-dollar (or trillion-dollar) question: How the hell do we perform the upload?

In Pantheon they go the dramatic route: a destructive “Deep Scan” that lasers the brain layer by layer while building the digital twin. Real-world connectomics is already doing this at small scale (they’ve fully mapped the fruit fly brain), but scaling to humans is still sci-fi territory. Non-destructive options (nanobots swimming through living tissue, ultra-high-res MRI on steroids, etc.) feel even further out.

So… how far are we? Decades? Centuries? Or is whole-brain emulation quietly creeping up on us while we argue about chatbot ethics?

Would you want to upload? Would you trust the copy to still be “you”? And if UI becomes possible, does it make today’s AI look like finger-painting by comparison?

I’m genuinely curious where this goes. 🔥

I attended a really interesting panel discussion last week.

Honestly, I was slightly terrified at first - the ratio of lawyers in the room was high enough to warrant a "without prejudice" disclaimer 😂

Jokes aside, I thoroughly enjoyed it. We dove into accountability, impact, policies, and good governance - all wrapped around the big question: how do we actually get sh!t done?

One line that really stuck with me was:

"What if your company is protecting the very rules that are holding it back?"

That question sent me down a rabbit hole… Are we unconsciously forcing non-determinism to behave like determinism?

The Sacred Machine: Non-Deterministic AI in a Deterministic World

🔥 The Making of a CDO and CAIO

Over the past few years, many professionals have asked me: What does it really take to become a Chief Data Officer (CDO) or Chief AI Officer (CAIO)?

There’s no simple linear path. Both roles are shaped by context, delivery, and credibility. Here’s my perspective:

The CDO Role
The CDO has evolved from a visionary data leader into a regulator-mandated guardian of data quality, governance, privacy, and compliance.

It demands deep expertise in data architecture, master data management, regulatory frameworks, and enterprise platforms. Regulators hold CDOs personally accountable. This is not a role for generalists.

Typical path: Progressive leadership in data engineering/analytics, turnaround experience, cross-functional exposure (IT, risk, finance), and proven outcomes like cleaner data and reduced risk.

Market trends show CDOs shifting toward decision enablement and AI readiness, becoming true strategic partners.

The CAIO Role
The CAIO is newer and still evolving. Historically reporting to various leaders, the role is now increasingly reporting directly to the CEO to drive real change.

The mandate sounds simple: identify AI opportunities and manage risks. But it’s actually a demanding transformation and change management role. It requires scaling adoption, building capabilities, and delivering measurable business impact.

Diverse backgrounds succeed here: from domain experts with little prior AI experience to technical specialists. The key? Bridging strategy, technology, and execution.

Market reality (IBM 2026 study): 76% of organisations now have a CAIO, up from just 26% in 2025. Companies with dedicated CAIOs see around 5% higher returns on AI investments.

What It Really Takes
Neither role is for everyone. You need:
- Credibility through real delivery
- Comfort with accountability
- Systems thinking + business acumen
- Relentless learning

Focus on leading major transformations, owning business outcomes, and building executive presence. The path is rarely linear. It’s forged in messy data realities and organisational change. 🚀

Don't use AI just for the sake of using AI

Happy birthday to all May babies 🎂 and some unsolicited reflections 😁

https://www.linkedin.com/posts/davidrh_tomorrow-is-my-birthday-and-im-spending-activity-7466412191164911616-5jqY?utm_source=share&utm_medium=member_android&rcm=ACoAAAC11PUB6bXVB2Khjqo0ABlxtMcjJuzhmtw