Cloud Dialogues

The Operational State of AI & Cloud
We’re kicking off 2026 with a reality check.

In this episode, Matt, Georgia, and special guest Allen Helton (Ecosystem Engineer at Memento, AWS Hero, and, yes - farmer) dig into what’s actually happening in AI and cloud right now. Less hype, more hard truths. From AI pilots that won’t scale to power grids that can’t keep up, this conversation explores what it really takes to move from experimentation to production.

🎙️ Hosts & Guest

Matt — Host (Texas)

Georgia — Host (London)

Allen Helton — Ecosystem Engineer at Memento, AWS Hero, and farmer

🗞️ Cloud & AI News: What’s Worth Paying Attention To
GPT Health: Innovation or Repackaging?
The team unpacks OpenAI’s GPT Health launch, questioning whether it’s a genuinely differentiated product or simply a safer wrapper around existing capabilities. Georgia shares how ChatGPT proved unexpectedly useful for post-surgery aftercare - sometimes outperforming traditional medical guidance.

AWS Is Back in Growth Mode
AWS reported ~20% year-on-year growth in Q3, its strongest in nearly three years. The consensus? AWS has finally caught up on AI - largely thanks to its Anthropic partnership and global access to Claude through Bedrock.

Quantum Computing: Is 2026 the Tipping Point?
IBM predicts quantum computers will outperform classical systems as early as 2026. The group discusses what that could mean for cryptography, banking, and security - while openly admitting that quantum still needs more expert decoding.

Power Is the Real Bottleneck
Google flags US transmission infrastructure as the biggest blocker for data-center expansion. That sparks a broader sustainability discussion: hyperscalers can’t depend on aging grids forever, and renewables aren’t optional - they’re inevitable.

🧠 The Operational Reality of AI & Cloud
Your Data Foundation Still Isn’t Ready
A recurring theme: organizations move “two steps forward, one step back” when AI exposes weak data governance and cloud foundations. As Georgia puts it: AI will not solve your data governance problems.

The Education Gap Is the Silent Killer
AI initiatives fail when business teams don’t understand the technology they’re adopting. Outsourcing isn’t enough - successful organizations immerse their entire teams so AI outputs are interpreted, validated, and trusted.

Are We Really Past Pilots?
Some say the pilot phase is over. Alan disagrees. Large parts of the industry are still early on the adoption curve - but the difference now is maturity: guardrails, retrieval systems, and meta-agents are production-ready.

👩‍💻 How AI Is Changing Software Careers
AI isn’t just changing how software is built - it’s changing who gets hired.

Key shifts discussed:

Programming language choice matters less than ever

Code review, comprehension, and reasoning now outweigh writing from scratch

Systems thinking is becoming table stakes - even for junior roles

“Tech-lead thinking” is creeping into every level

Alan’s advice to students and early-career engineers:

You still need to understand how it all works - everything you write is part of something bigger.

🧩 Developer Operating Models: What Actually Scales?
Ralph at Scale
Matt introduces Geoffrey Huntley's Ralph Wiggum development approach: giving an LLM an ordered backlog and letting it execute autonomously across fresh context windows. Powerful - but expensive and hard to sustain.

The “Gas town” Model
An alternative approach uses 30-40 agents working in parallel across a stack. Fast, impressive… and extremely token-hungry and even more expensive!

The Sensible Middle Ground
Our hosts argue for balance: AI-accelerated delivery with strong human oversight. Think weeks of work compressed into afternoons - without sacrificing quality, maintainability, or understanding.

🔮 Looking Ahead
Regional Model Availability Is a Deal-Breaker
Many regulated organizations simply can’t adopt AI due to regional model restrictions. Australia, for example, has access to just one local foundation model - highlighting a global compliance challenge.

Sustainability & Reliability Risks
If models became unavailable or prohibitively expensive, productivity would fall off a cliff. Competition should help manage costs - but reliability at scale may be the bigger risk.

The Adoption Curve Has Never Been Wider
AI adoption now spans:

Teams using autonomous coding agents daily

Enterprises still waiting for approval to touch an LLM

Most regulated industries haven’t even started formal approval processes.

✅ Key Takeaways

Data governance is still the biggest blocker to AI success

Developer roles are shifting toward systems thinking and code comprehension

Enterprise AI adoption is far lower than headlines suggest

Regional model availability is a serious global constraint

Power and sustainability will shape the future of cloud growth

There’s no single “right” AI operating model

Business teams must deeply understand the tech - not just fund it

📬 Closing Notes
Alan plugs his newsletter Ready Set Cloud of the Week (readysetcloud.io), where he curates and analyzes the most interesting tech stories each week.

As always, we’d love to hear from you.
Feedback, guest ideas, and topic suggestions → [email protected]

Cloud Dialogues is a podcast for technology leaders navigating cloud, AI, and enterprise transformation—grounded in reality, not hype.

Matt and Georgia recap AWS re:Invent 2025 with special guest Michael Walmsley, AWS Serverless Hero and Global Technology Architect at Accenture. Fresh from the Vegas event with 70,000 attendees, they discuss the major announcements, the shift toward AI agents, and Michael's wild experience coding on a bus for a $100K hackathon prize.

Highlights
Road to re:Invent Hackathon

50 developers coded on buses traveling LA to Vegas over 5 hours

Michael's team built "Lucky Loo.me" - an AI bathroom finder using facial recognition

Winning team created "Oric" - an IDE that turns 3 lines into 3,000 lines of AI slop

Prize: $100K split among the winning team

The Big Theme: AI Agents Everywhere

"Agents" was the dominant word at every booth

AWS pushing agent capabilities into every service team

Evolution from general AI (2024) to production agent platforms (2025)

Announcements we covered:

Agent Core Updates

New policy controls for blocking unauthorized actions

Evaluation tools for inspecting agent behavior

Progressive adoption - use pieces without adopting the whole platform

AWS Agent Marketplace

Vendors can now sell pre-built agents

Example: Cloud Zero cost management agent

Lambda Updates

Lambda managed instances

Durable functions for long-running workflows in code

Alternative for developers who don't want Step Functions

S3 Vectors (GA)

Store 20 trillion vectors in one bucket

90% cost savings vs traditional vector databases

Sub-100ms query times for frequent queries

"S3 is the cheapest database on the planet"

CloudWatch Unified Data Store

All logs and metrics exposed in S3 Tables

Cheap, structured SQL querying of observability data

AWS Interconnect ⭐ Biggest Surprise

High-speed encrypted links between AWS and Google Cloud

Azure support coming 2026

Free during preview (pricing TBA)

Major shift from AWS's anti-multi-cloud stance

Acknowledges multi-cloud reality in enterprises

Kiro

Rebranding away from confusing "Amazon Q" umbrella

Kiro Powers: AI-activated tool modules

Reduces context bloat in coding agents

Active hackathon scene with significant prize pools

Guest
Michael Walmsley - AWS Serverless Hero, Global Technology Architect at Accenture, specializing in serverless and SaaS architecture. Fourth year attending re:Invent.

Key Takeaway
AWS is maturing from general AI capabilities to production-ready agent platforms while finally embracing multi-cloud architectures. The focus has shifted to making agents secure, manageable, and practical for enterprise use.

In this episode, Matt and Georgia sit down with Brad Young (Capgemini Invent) and Alistair Adams (Solution Energy) for a fast-moving conversation about AI’s exploding energy appetite and what it means for the future of data centers, power grids, and sustainability. From geopolitical tension to geothermal innovation, this one covers the full energy spectrum.

What We Covered:

- AI’s Energy Crunch
AI growth is driving unprecedented demand for power. Hyperscalers like Meta, Google, and Microsoft are signing multi-billion-dollar infrastructure contracts at record pace, stretching grids and reshaping global infrastructure priorities.
- The Rise of “Power-First”
Google’s “power-first strategy” shows the new reality: build data centers where the power is, not where the people are. Nvidia’s Jensen Huang agrees—co-locating at generation sites may be the future. Reliable, renewable baseload power is now the real competitive edge.
- Water: The Silent Crisis
Energy gets the headlines, but water is just as critical. Google already uses ~70 billion litres annually for cooling—on track to rise tenfold. Innovations like geothermal heat rejection (e.g., the Pawsey supercomputer in WA) offer promising alternatives.
- Renewables: What Actually Works
Not all green energy is created equal. Wind and solar can’t deliver the 24/7 baseload those massive GPU clusters require. That leaves geothermal and nuclear as the only scalable clean options—though nuclear remains politically fraught in markets like Australia.

Regional Realities

- Australia: Victoria faces a looming 1.5 GW gap with coal retirement.
- UK: Grid constraints limit data center growth.
- US: Federal policy is leaning hard into nuclear and geothermal for AI.
- Europe: Regulation is reshaping the tech landscape—for better or worse.

Cloud’s Hidden ESG Problem

Most cloud usage sits in companies’ Scope 3 emissions. As ESG rules tighten, lack of transparency from hyperscalers becomes a real compliance exposure.
- Social License Becomes Strategy
Community pushback is halting billion-dollar projects. The new game: secure energy, protect water, and bring the community with you. “Permission-based infrastructure” is quickly becoming the norm.
- AI, Talent & the Enterprise Gap
We discuss the widening skills challenge—junior staff struggle to validate AI outputs, and enterprises claiming “we don’t have use cases” are already falling behind.
- Greener Compute Through Smart Pricing
Dynamic cloud pricing tied to renewable availability is on the horizon—think “off-peak compute,” automatically routing workloads to greener grids.

Standout Insights

- We’re in the “Nokia 3210 era” of AI—25+ years of disruption ahead.
- Robotics is still more marketing than reality.
- Enterprise AI adoption is early; the real environmental impact is still to come.

Key Takeaways

- Data center location will follow energy, not geography.
- Community permission is as critical as capital.
- Water use must be part of every sustainability conversation.
- Geothermal and nuclear are the only viable clean baseload options.
- The next decade will be messy as demand outpaces grid upgrades.
- Hyperscalers are accelerating renewable markets—out of necessity.
- ESG exposure from opaque cloud emissions is rising fast.

Conclusion

AI’s growth is forcing a complete rethink of how we power digital infrastructure. The winners will be those who can solve the combined puzzle of clean energy, water management, community trust, and transparent reporting—at a speed the grid has never been asked to move before.

After a whirlwind summer break (Georgia was in Australia, the US, Switzerland, France and back to the UK), your hosts return to talk fake spring in Melbourne, big AI news, and the latest progress in our Agentic AI Experiment.

🚀 AI News Highlights
Gemini Nano Banana (2.5 Flash): Google’s new multimodal model nails hands (finally) and shines at storyboarding with JSON prompts.

Kimi K2: A front-end coding powerhouse from China’s Moonshot AI — cheaper than Claude Sonnet 4, though backend isn’t its strong suit.

GPT-5: Quietly flexing its ability to augment answers with real-time web searches.

Regulation: Australia looks set to ditch bespoke AI laws — a move we (cautiously) support.

Cloud & Infra: AWS NZ finally opens after a 4-year wait, while Oracle’s $300B OpenAI deal catapults Larry Ellison to the #1 richest spot.

🤖 The Agent Experiment: Content Co-Creator

We update you on our experimental AI system designed to help creators generate social content ideas.
The Vision: AI that uses your interests, calendar, and activities to suggest posts, captions, and even storyboards.

The Hurdles:

Social APIs = pricey + restrictive
Scraping trending content = messy (lots of “weird” results)
TikTok ≠ Instagram: their algorithms play by very different rules
Creator Insights: TikTok’s algorithm makes it easier to go viral from scratch — and creators earn more there than on Insta.

❤️ The MLP (Minimum Lovable Product)
Instead of chasing APIs, we’re starting simpler:
Web app that asks about passions & activities

Optional calendar integration
AI-generated content ideas + Nano Banana-powered storyboards
Real-world testing on ourselves first
And with Instagram’s new “Edits” feature echoing this direction, the market clearly agrees.

🔮 What’s Next
Iterating the Content Co-Creator with real feedback
Upcoming episodes on renewables + data center power
Inviting listeners to weigh in ([email protected])

This episode blends AI news, social media realities, and product-building tradeoffs — with plenty of laughs along the way.

In the second instalment of our Agentic AI series, Georgia and Matt are joined by returning friend of the pod and AI product expert, Pete Sbarski, to put our AI-generated startup ideas under some very real scrutiny.

But first… the news:

⚖️ Legal & Regulatory Shakeups A US court ruled that training AI on legally purchased books = OK, pirated books? Not so much.

Germany's taking on DeepSeek, pressuring Apple and Google to block the app over privacy concerns.

⚡️ Infrastructure Watch

Amazon's going big in Australia, announcing a massive $13B investment in data centers and solar by 2029.

Cloudflare and Google Cloud had a rough week, with outages reminding us that even hyperscalers aren’t immune to single points of failure.

💡 Back to Business: Can AI Actually Build a Startup?

Last episode, Claude and Gemini pitched six startup ideas.

This week, we brought in Pete to roast them (constructively). Here’s what made the shortlist:

📝 AI RFP Assistant

✅ Compliance Monitor for SMEs

🔍 B2B Data Quality Tool

⚖️ AI Paralegal for Small Firms

💰 Grant Guru for Nonprofits

✍️ Content Copilot for Solo Creators

🧠 Key Takeaways from Pete

“Just because you can, doesn’t mean you should” — some use cases (like legal AI) come with huge liability baggage.

Data hygiene does not necessarily mean fancy tools — a lot of orgs want AI, but what they really need is cleaner, more structured data.

AI isn’t the bottleneck — people are — from resistance to change to unclear ownership, it’s often human systems holding us back.

✅ What We’re Testing Next

We’re moving ahead with two experiments:

The Content Copilot – using AI to repurpose and summarise our own podcast archive.

RFP / Grant Assistant – trialing real-world applications with a friend’s health tech startup.

We’ll be back soon with some early results — and hopefully a winner. 👀 📩

Want early access and exclusive insights from our experiments?

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