I’m an innovator who challenges the status quo and sees technology as a catalyst for growth — not a risk.

With 20+ years in technology and a focus on cloud and AI & GPU infrastructure, I’ve helped leading organizations across banking, insurance, manufacturing, life sciences, and the public sector deliver meaningful transformation.

Educated at MIT and Oxford. Proud alumnus of Oracle, Deloitte and Accenture

—-
Opinions are my own

🚀 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗔𝗜 𝗘𝗗𝗚𝗘 — 𝗜𝘀𝘀𝘂𝗲 #𝟭
I’m excited to launch AI EDGE, a monthly AI briefing for people building, buying, and betting on AI.

The goal is simple:
Cut through the noise and highlight the most important AI news of the month, with clear context for tech and business leaders.

Why this newsletter exists👇
Too many AI newsletters are long and overwhelming.
AI EDGE is different:

Short headlines
Why it matters
Links to go deeper

If you care about where AI is going—and what actually matters—this one’s for you.

Let’s get started.

Jiri Kram

I am a seasoned cloud computing professional with 19 years of experience, I have worked with leading companies like Accenture, Deloitte, Deutsche Bank and Fujitsu. My advanced studies in Artificial Intelligence at MIT and the University of Oxford have equipped me to deliver innovative, scalable, and secure cloud solutions.

Passionate about technology, I thrive on collaborating with clients who challenge the status quo. I specialize in leveraging cloud and AI to drive competitive advantage and business growth. Let's connect to explore how we can use technology to achieve extraordinary results together.

-----
The views expressed on Linkedin are mine alone and do not necessarily reflect the views of my current, former, or future employers.

Accenture

Accenture Certified Technology Architect

Amazon Web Services (AWS)

Amazon Web Services Solutions Architect - Associate

Indico Data

Indico Data Advanced Workflow

Indico Data Workflow Fundamentals

MIT Sloan School of Management

Unsupervised Machine Learning: Unlocking the Potential of Data

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Oxford Leading Professional Service Firms Programme

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Building Containerized Applications on AWS

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How Google does Machine Learning

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Certified Salesforce Developer (DEV401)

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Cloud Architect @ ADF | Cloud & AI I Certified Technology Architect | ex-Oracle, Accenture, Deloitte

✅ 𝗝𝗲𝗻𝘀𝗲𝗻 𝗛𝘂𝗮𝗻𝗴 𝗷𝘂𝘀𝘁 𝘂𝗻𝘃𝗲𝗶𝗹𝗲𝗱 𝗮 𝗻𝗲𝘄 𝗡𝗩𝗜𝗗𝗜𝗔 ‘𝘁𝗼𝘆’ — 𝗥𝘂𝗯𝗶𝗻

It may look like a shiny box, but Rubin is NVIDIA’s next AI supercomputing platform — built for the age of AI factories.

Why it matters:
• Designed for trillion-parameter models
• Major leap in performance per watt
• Treats the data center as one giant computer

If Blackwell powered today’s AI boom, Rubin is about scaling what comes next.

Not a toy — just built by someone who plays at a different level 😉

More 

https://lnkd.in/dyRmG4Gf 

#CES2026 #NVIDIARubin

✅ 𝗚𝗼𝗼𝗴𝗹𝗲 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲𝗱 𝗨𝗖𝗣 (𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) 𝘁𝗼 𝘂𝗻𝗹𝗼𝗰𝗸 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗲

UCP is a new open standard that allows AI agents to safely and consistently handle commerce flows—product discovery, cart creation, checkout, and payments—across platforms.

Why it matters:

🔗 Eliminates fragmented, one-off commerce integrations with a single universal protocol
🤖 Enables AI assistants to move from recommendations to taking action
🏪 Merchants integrate once while remaining merchant of record
🔐 Designed with security, consent, and tokenized payments at its core

As AI becomes more agentic, UCP could be a foundational layer for how commerce works across search, chat, and future AI-driven experiences.

👀 Definitely one to watch.
https://lnkd.in/d_47JAkh

😂 𝗪𝗵𝗲𝗻 𝗔𝗜 𝗳𝗮𝗶𝗹𝘀, 𝗶𝘁 𝗳𝗮𝗶𝗹𝘀 𝘀𝗽𝗲𝗰𝘁𝗮𝗰𝘂𝗹𝗮𝗿𝗹𝘆 👌

🇨🇳 Thousands of drones, coordinated by AI, lit up the sky over China.

Top 100 Most Influential Men 2025 | Creator of The Signal | Guiding leaders and brands to grow influence and attract premium opportunities

The enemy isn't fear.
It's nostalgia.
Dressed as caution.

We keep reacting to AI like it's a stunt.
A demo.
Something to admire from a distance.

What's actually happening is quieter.

Compute converged.
Sensors matured.
Coordination stopped being fragile.

When thousands of machines move as one,
that isn't spectacle.
That's systems learning to operate at scale.

I waited for consensus once.
Confused hesitation for wisdom.
Missed the window.

That cost was real.

The real threat isn't job loss.
It's mistaking friction for value.

AI isn't arriving.
It's already embedded.
In logistics.
In medicine.
In energy.
In how cities organize themselves.
Systems that align before we finish debating whether they should.

Awe is a luxury.
Adaptation is a requirement.

Stand in awe if you want.
I'm building for what comes next.

♻️ Repost if you've stopped waiting for permission
🧭 Follow Leonardo Freixas for leadership that cuts through hesitation

Video credit: @longliveai (Instagram)

🚨 𝗠𝗖𝗣 𝗕𝗲𝗰𝗼𝗺𝗲𝘀 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 𝗔𝘁𝘁𝗮𝗰𝗸 𝗦𝘂𝗿𝗳𝗮𝗰𝗲

Major findings from the first CISO Weekly Brief of 2026:
	•	Model Context Protocol (MCP) has emerged as a new, high-risk attack layer as AI agents inherit identity and access weaknesses
	•	Machine identities outnumber humans 82:1, dramatically increasing unmanaged credentials and trust relationships
	•	A critical MCP OAuth flaw enabled remote command execution, showing how fast AI tooling can become weaponized
	•	Exfiltration-only ransomware is accelerating, making breaches harder to detect and respond to
	•	Ongoing leadership disruption at Cybersecurity and Infrastructure Security Agency raises concerns about early-warning resilience

Bottom line:
2026 is shaping up to be the year where MCP security, AI governance, and machine-to-machine identity move from experimental discussions to core enterprise risk.

🔗 Source:
https://lnkd.in/dnwvA8Nq

☕️ 𝗟𝗲𝘁’𝘀 𝘁𝗮𝗹𝗸: 𝗛𝗼𝘄 𝗺𝗮𝗻𝘆 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗮𝗿𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻?

Most teams can build an AI prototype.
Far fewer turn it into something that runs reliably, scales, and delivers real business value.

🚧 Common challenges I see:
	•	🧪 Works in experiments, breaks in real workflows
	•	🔄 Unstable or poorly owned data pipelines
	•	🤝 Gaps between data science, engineering, and product
	•	📉 No clear monitoring, drift detection, or retraining plan
	•	📊 Hard to measure success once the model is live

So I’m curious 👇
💬 What’s been the hardest part of getting AI into production for you?

🇨🇳 Ouch! Chinese Kung-Fu Robots. This looks dangerous.

Unitree Humanoid Robot Daily Training 🥳
Have you exercised today? How about training together with a robot?
Please use robots in a friendly and safe manner, and keep a safe distance.
#Unitree #HumanoidRobot #Training #WorkoutCompanion #AGI #AI #EmbodiedAI

🤔 𝗛𝗮𝘀 𝗟𝗼𝗻𝗱𝗼𝗻 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝗯𝗲𝗰𝗼𝗺𝗲 𝗘𝘂𝗿𝗼𝗽𝗲’𝘀 𝗔𝗜 𝗰𝗮𝗽𝗶𝘁𝗮𝗹?

Not because of buzz — but because of momentum. 🇬🇧

📈 The largest AI startup ecosystem in Europe
💸 More AI investment than any other European city
🎓 World-class research (UCL, Imperial, Oxford & Cambridge nearby)
🏢 Major AI labs + real enterprise adoption
🌍 Global talent, scaling companies, real impact

Ideas start here.
Companies scale here.
AI gets deployed here.
Funding companies get funded.

Official title or not, the centre of gravity for European AI is hard to ignore.

What’s your take — London, Paris, or somewhere else? 👇

🤔 𝗪𝗵𝘆 𝗱𝗼 𝟴𝟬% 𝗼𝗳 𝗔𝗜 𝗣𝗼𝗖𝘀 𝗻𝗲𝘃𝗲𝗿 𝗺𝗮𝗸𝗲 𝗶𝘁 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻?

Because building a model is the easy part.

Here’s what it actually takes to move AI from PoC to Production 👇

1️⃣ Clear business goal
If success isn’t measurable, production will fail.

2️⃣ PoC to prove feasibility
Fast experiments, small data, quick learning.

3️⃣ Reliable data pipelines
Automated, versioned, and monitored — no manual CSVs.

4️⃣ Production-ready models
Optimized for latency, cost, robustness, and explainability.

5️⃣ MLOps & automation
Model versioning, CI/CD, reproducible training and deployment.

6️⃣ Deployment that scales
APIs, batch, or edge — tested under real-world load.

7️⃣ Monitoring & governance
Detect data drift, performance decay, and stay compliant.

🔑 Key takeaway:
PoC proves the idea. Production proves reliability, scalability, and trust.

✅ 𝗕𝗼𝗮𝗿𝗱𝘀 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗮𝘀𝗸 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗮𝘀 𝗔𝗜 𝘀𝘁𝗮𝗿𝘁𝘀 𝗺𝗮𝗸𝗶𝗻𝗴 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀.

As Joaquim Alvarez says, once systems act autonomously, the conversation shifts from technology to governance:
• Who is accountable?
• How is risk managed?
• How do we explain outcomes?

Head of Cognitive & Smart Cities at NEOM | 🏛 MIT Chief Digital Officer | AI, Digital Twins & Urban Innovation | 🏆 Top LinkedIn Voice 2024 in Digital Strategy

⚡ 𝐀𝐆𝐄𝐍𝐓𝐈𝐂 𝐀𝐈: 𝐓𝐡𝐞 𝐬𝐡𝐢𝐟𝐭 𝐭𝐡𝐚𝐭 𝐟𝐞𝐰 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞𝐬 𝐚𝐫𝐞 𝐬𝐞𝐞𝐢𝐧𝐠⁣
Today, at best, organizations are using Generative AI but still don't leverage—because they don't understand—the power of Agentic AI.⁣
⁣
𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈:⁣
→ Generates text, images, code⁣
→ Waits for your next prompt⁣
→ Needs constant human direction⁣
→ Example: ChatGPT writing an email⁣
⁣
𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈:⁣
→ Sets goals and executes independently⁣
→ Makes decisions without human input⁣
→ Adapts strategy based on outcomes⁣
→ Example: AI autonomously managing your entire supply chain⁣
⁣
𝐓𝐡𝐞 𝐬𝐡𝐢𝐟𝐭 𝐢𝐬 𝐩𝐫𝐨𝐟𝐨𝐮𝐧𝐝:⁣
❌ "What should I tell the AI to do?"⁣
✅ "What outcomes do I want, and how do I audit the AI's decisions?"⁣
⁣
𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐢𝐦𝐩𝐚𝐜𝐭: The most advanced agentic systems already:⁣
→ Monitor global operations 24/7⁣
→ Predict disruptions hours before they occur⁣
→ Adjust logistics strategies automatically⁣
→ Reason through uncertainty and edge cases⁣
⁣
𝐓𝐡𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐛𝐨𝐚𝐫𝐝𝐬:⁣
𝘈𝘳𝘦 𝘸𝘦 𝘪𝘯𝘷𝘦𝘴𝘵𝘪𝘯𝘨 𝘵𝘰 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘦 𝘺𝘦𝘴𝘵𝘦𝘳𝘥𝘢𝘺'𝘴 𝘮𝘰𝘥𝘦𝘭 𝘸𝘪𝘵𝘩 𝘎𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘷𝘦 𝘈𝘐 𝘵𝘰𝘰𝘭𝘴...⁣
𝘖𝘳 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘤𝘰𝘮𝘱𝘦𝘵𝘪𝘵𝘪𝘷𝘦 𝘢𝘥𝘷𝘢𝘯𝘵𝘢𝘨𝘦𝘴 𝘸𝘪𝘵𝘩 𝘈𝘨𝘦𝘯𝘵𝘪𝘤 𝘈𝘐 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘵𝘩𝘢𝘵 𝘰𝘱𝘦𝘳𝘢𝘵𝘦 𝘢𝘵 𝘴𝘱𝘦𝘦𝘥𝘴 𝘢𝘯𝘥 𝘴𝘤𝘢𝘭𝘦𝘴 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘢𝘭𝘭𝘺 𝘪𝘮𝘱𝘰𝘴𝘴𝘪𝘣𝘭𝘦 𝘣𝘦𝘧𝘰𝘳𝘦?⁣
⁣
💡 The difference isn't technical. It's architectural. It changes how we design organizations, define roles, and measure leadership effectiveness.⁣
⁣
#AgenticAI #GenerativeAI #DigitalTransformation #ExecutiveLeadership #Innovation #AIStrategy

Jensen Huang highlights how open models are transforming AI.

“When AI is open, it proliferates everywhere.”

Jensen Huang explains why open models are fueling the AI revolution, activating innovation across industries, startups, researchers, students, and countries worldwide.

Learn more about our open models → https://nvda.ws/49D3DVE

🇨🇳 𝗪𝗵𝗶𝗹𝗲 𝘁𝗵𝗲 𝗪𝗲𝘀𝘁 𝗱𝗲𝗯𝗮𝘁𝗲𝘀 𝗔𝗜 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻, 𝗖𝗵𝗶𝗻𝗮 𝗶𝘀 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗔𝗜 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲.
This article is not about models, benchmarks, or hype cycles.

It looks at how AI is being embedded into the physical world — through robots, drones, autonomous mobility, energy systems, buildings, and cities operating at scale.

China’s advantage is not one breakthrough. It is the convergence of manufacturing, infrastructure, energy, and AI into a single system.

If you want to understand where AI is heading beyond screens and software — and why China’s approach matters — this article is for you.

👇 Full article below

My friend Bob Evans from #cloudwars explains what’s new in AI Economy this episode features NVIDIA Google Mercedes-Benz AG

#CloudWars founder · Keynote speaker · Helping CXOs drive growth via deep expertise in biz, innovation & tech

In a sneak peek at what will be a defining thread of 2026, Google has purchased an energy firm and NVIDIA is helping create an "#AI-driven" car for Mercedes-Benz AG. In the AI Economy, tech innovation & disruption is bleeding into all sectors of the global economy in unprecedented ways and at unprecedented speeds. Hear more of my predictions in today's Cloud Wars Minute → https://lnkd.in/exXrUHAC

Jensen Huang explains Nvidia chip strategy for AI. It’s worth to listen.

Top Semiconductor Voice | Co-Founder & CEO @ TestFlow | AI-Driven Chip Validation

Jensen Huang just BROKE the most important rule in the industry.

And it explains why Nvidia controls ~95% of the AI chip market.

Last night at CES, Jensen Huang unveiled Vera Rubin — a new AI supercomputer that is already shipping.

Not announced.
Not taped out.
In full production.

And here’s the moment every semiconductor engineer in the room froze:

→ Rubin GPU is 5× faster than Blackwell  
→ Only 1.6× more transistors  

That shouldn’t be possible.

By Moore’s Law, you’re lucky to get ~25% more performance per generation.
Jensen just delivered ~300%.

So how did Nvidia do it?

→ They broke the industry’s most sacred rule  
→ “Never redesign more than 1–2 chips per generation”

Instead, Nvidia redesigned everything at once.

→ Vera CPU  
→ Rubin GPU  
→ ConnectX-9 networking  
→ BlueField-4 DPU  
→ NVLink switches  
→ Spectrum-X Ethernet  

Every single component. From scratch.

Jensen calls it “extreme co-design.”
The industry calls it insane.

The result?

→ One rack moves 240 TB/s  
→ That’s 2× the entire global internet bandwidth  
→ In a single rack  
→ Running on 45°C water cooling (no chillers)  
→ Saving ~6% of global data-center power  

But the real story isn’t the hardware.

It’s what Nvidia is doing with it.

Nvidia just open-sourced Alpha Mayo — the world’s first reasoning autonomous-vehicle AI.

→ Mercedes-Benz CLA launches with it in Q1  
→ Europe in Q2  
→ Asia by year-end  

Not a concept.
Not a pilot.
Full production vehicles.

And the AI explains its decisions out loud:

“I’m slowing down because the truck ahead is braking and a cyclist is merging.”

It thinks.
It explains.
Then it acts.

Jensen personally drove it through San Francisco traffic for an hour.

And then Nvidia did something even more shocking:

→ They open-sourced everything  
→ Training code  
→ Data sources  
→ The entire stack  

Why give this away?

Because Nvidia learned something critical last year:

→ Open models activate the world  
→ DeepSeek-R1 proved open source can hit the frontier  
→ Every startup, lab, and country starts building  

And all of them need Nvidia hardware to train it.

That’s the strategy:

Give away the recipes.
Sell the kitchen.

The partnerships show where this is going:

→ Siemens integrating Nvidia into industrial design tools  
→ Cadence & Synopsys rebuilding chip design around Nvidia  
→ Palantir, ServiceNow, Snowflake running on Nvidia’s agentic AI stack  

This isn’t chip sales anymore.

Nvidia is rebuilding the entire computing stack.

→ Design  
→ Manufacturing  
→ Deployment  

Every layer of the trillion-dollar AI infrastructure build-out runs through them.

And once again:

→ They’re 18 months ahead  
→ While competitors chase Blackwell  
→ Nvidia is already shipping what makes Blackwell look slow  

Key takeaway:
This isn’t about faster GPUs.
It’s about owning the entire AI ecosystem end-to-end.

So the real question is:

Is anyone catching Nvidia?

The only company even close might be Google.
What do you think?

🤔 𝗖𝗮𝗻 𝘄𝗲 𝘀𝘁𝗼𝗽 𝗔𝗜 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗻𝗴?


Yes you see it correctly buses are on pavement 😎 

It’s not by design it’s AI hallucinating. 

🤖 AI hallucination isn’t a bug in the traditional sense. It’s a byproduct of how large language models work: predicting the most likely next word, not “knowing” facts.

What does help:
	•	Better data grounding (retrieval, citations, source linking)
	•	Clear prompts and constraints
	•	Human-in-the-loop review
	•	Domain-specific fine-tuning
	•	Knowing when not to use generative AI

What doesn’t:
	•	Blind trust
	•	Treating AI outputs as truth instead of drafts
	•	Assuming bigger models = zero errors

The real question isn’t “Can we stop hallucinations?”
It’s “How do we design systems, workflows, and expectations that account for them?”

AI is powerful — but responsibility still sits with us.

Curious how others are handling hallucinations in real-world use cases 👇

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Jiri Kram's Best Posts (last 30 days)