Best of LinkedIn: Cloud Insights CW 19/ 20

00:00:00: This episode is provided by Thomas Allgeier and Frennus based on the most relevant LinkedIn posts about cloud in calendar weeks, nineteen and twenty.

00:00:07: Frenness has built a sovereign Cloud Market Radar for ICT providers with weekly hot news monthly reports quarterly leadership presentations an AI podcast for field teams.

00:00:18: you can find more info in description

00:00:20: right.

00:00:20: so jumping straight into it

00:00:22: yeah today we are doing deep dive to top-cloud trends across linkedin over last two week

00:00:28: And our mission here is really just to, you know filter out all the noise.

00:00:32: Exactly because if you're an ICT or tech professional listening to this You don't have time for The Fluff!

00:00:38: You want the actual practical insights To stay ahead of the curve

00:00:42: Absolutely.

00:00:42: So we've clustered All these Insights into three main themes For today.

00:00:45: We are looking at Sovereignty and Compliance cloud costs and fine ops, finally AI Cloud in architecture.

00:00:53: Yeah And that first one sovereignty and compliance is huge.

00:00:56: right now.

00:00:56: I mean imagine moving your company's entire proprietary data infrastructure to what you think.

00:01:02: Is this highly secure local European cloud?

00:01:05: Right You've checked all the compliance boxes.

00:01:07: The physical servers are literally down the street.

00:01:11: You wake up to find out a foreign government can legally demand access

00:01:15: to all of it.

00:01:16: Oh, man

00:01:17: And there is absolutely nothing your cloud provider can do to stop them like that Is not a hypothetical nightmare anymore?

00:01:22: No

00:01:22: It's really not.

00:01:23: its

00:01:23: the operational reality for so many organizations That are frankly operating under this illusion of data residency.

00:01:31: It

00:01:32: is a massive blind spot in the industry like we are seeing a very sharp pivot and how people talk about this, The days have talking about cloud purely as you know if vehicle for abstract digital transformation.

00:01:44: those were over.

00:01:45: Yeah, completely over.

00:01:46: The focus across the industry over the past couple of weeks has been laser targeted on one thing and that is control legal control over where your data lives financial control over what it costs And architectural control over these AI systems that are changing everything.

00:02:00: So let's start right there with the legal control because this friction between data residency and true data sovereignty Is causing I mean just a lot of heartburn out There.

00:02:08: Oh definitely

00:02:09: two voices on LinkedIn really dissected This recently Andreas Hamburger and Nor Amy Ismail.

00:02:16: They both point out that organizations are basically treating data residency as if it's a magic shield,

00:02:21: right?

00:02:22: I think my date is in Frankfurt so European privacy laws protect me

00:02:25: exactly.

00:02:27: But hamburger and Ismail argue that residency absolutely does not equal sovereignty.

00:02:32: No they are highlighting a really critical misunderstanding of international law here specifically mechanisms like the US Cloud

00:02:40: Act.

00:02:41: because the physical location of a server, you know?

00:02:44: The residency.

00:02:45: It doesn't extinguish the legal reach of the jurisdiction where the parent company is headquartered.

00:02:50: wait so even if the hardware Is local

00:02:52: exactly yeah If the corporate entity operating your cloud as incorporated in United States that Company is subject to US Jurisdiction.

00:03:00: A federal judge can compel That parent company To produce data held anywhere In their global infrastructure.

00:03:07: The jurisdiction follows the corporate charter.

00:03:08: Yeah, it doesn't matter if the hardware is sitting in a data center and Kuala Lumpur or Auckland.

00:03:13: So relying purely on geographic location Is essentially like putting all your company's most sensitive IP Into a safety deposit box at a local bank branch.

00:03:24: That's a good way to look at it.

00:03:25: Like you can go look at the vault.

00:03:27: It's In Your City But you're ignoring the fact that the bank is headquartered in a foreign country and that Foreign government holds a master key.

00:03:34: That forces The Bank manager to open your box whenever they issue a warrant.

00:03:38: Yeah, that captures the vulnerability perfectly.

00:03:41: the physical vault means nothing if the legal authority just overrides it.

00:03:45: Andy Jenkins had actually offered up fairly

00:03:48: brutal

00:03:49: assessment of this dynamic.

00:03:51: Oh

00:03:51: yeah, what did he say?

00:03:53: He pointed out that running a European cloud strategy on US controlled hypervisors or you know relying heavily on services like Microsoft Entra ID for your core authentication it fundamentally undermined any claim to sovereignty.

00:04:06: I mean i make sense

00:04:07: his phrasing was super sharp...he called it renting a room in someone else's prison.

00:04:11: oh wow!

00:04:12: That is harsh visual

00:04:13: right.

00:04:14: You have the illusion of your own space but somebody ultimately controls.

00:04:18: Yeah, that really gets to the core of the dependency issue.

00:04:21: And Deon Wiggins takes that dependency argument even further.

00:04:24: he completely dissects the hardware layer okay?

00:04:27: He argues that the entire concept of a truly sovereign cloud Even one built on American or Chinese soil is largely a myth because of the global supply chain.

00:04:36: Because

00:04:37: are the components

00:04:37: exactly?

00:04:38: if you want to run AI workloads You need GPU's.

00:04:42: do look at it dominant US company like Nvidia right But their chips are fabricated by TSMC in Taiwan.

00:04:49: Using lithography machines from ASML and the Netherlands, which rely on specialized Zeiss optics from Germany.

00:04:57: Wiggins' point is that no single nation controls modern technology stack end-to-end.

00:05:02: True sovereignty isn't about isolationist ownership of a stack — it's operational resilience.

00:05:09: Can your organization keep functioning if foreign jurisdiction suddenly cuts off access or demands data?

00:05:15: Okay, I hear that tough love assessment.

00:05:17: But looking at the market activity i kind of have to push back a little.

00:05:19: Fair enough

00:05:20: because The major hyperscalers are very aware Of this narrative and they Are deploying serious capital To fight it.

00:05:27: we're seeing massive investments in localized boundaries.

00:05:30: That's true.

00:05:31: They are spending A

00:05:31: lot yeah.

00:05:33: For instance, Stefan Israel and Jens Dommel recently highlighted AWS's new strategic partnership with Vodafone Germany.

00:05:39: Right I saw that

00:05:41: they aren't just selling standard AWS.

00:05:43: They are actively building out an aws European sovereign cloud to deliver full capabilities while really trying To meet these strict residency in operational requirements.

00:05:53: Mm-hmm

00:05:54: And on the Microsoft side Jonathan Palmer and Frank Calvert Just championed a one year progress report on Microsoft's European digital commitments.

00:06:02: They are rolling out incredibly complex new data boundaries and sovereign private cloud options.

00:06:08: they're clearly trying to engineer a solution to this legal friction.

00:06:11: Well, the investments are undeniable And technologically those localized boundaries Are frankly impressive feats of engineering.

00:06:19: But from a strict illegal in governance standpoint deep skepticism definitely remains.

00:06:24: Anthony month reacted to these developments by publicly questioning if the AWS European sovereign cloud for example is ultimately just a very sophisticated marketing

00:06:34: rapper and marketing rapper ouch

00:06:36: because at the end of The day no matter how many local data centers you build or local employees You hire it remains.

00:06:42: A wholly owned subsidiary of an American corporation?

00:06:45: Yeah, the core legal question is still there exactly

00:06:48: Can a subsidiary ever truly shield its customers from the binding legal obligations of it's parent company?

00:06:54: Right.

00:06:55: If the parent company gets a subpoena, the subsidiary has to comply.

00:06:59: The master key still works

00:07:00: Which brings us to Bruno Dzogovich definition Of what actually constitutes sovereignty.

00:07:05: He argues that it boils down To absolute operational control.

00:07:09: Operational

00:07:10: control

00:07:10: Yeah It's not just having your data sit in a specific region.

00:07:13: Its' having verifiable independent control over the physical infrastructure, platform architecture and strategic direction of services.

00:07:21: So who is actually delivering that right now?

00:07:24: Well if you want to reality check on it Look at the analysis Dimitri Mislenikov just ran.

00:07:29: Okay, he evaluated thirty different European cloud providers against six rigorous criteria things like strict EU ownership reliance on open source core technologies to prevent vendor lock-in and carbon neutrality.

00:07:45: I'm

00:07:45: guessing it wasn't a high pass rate.

00:07:47: out of thirty providers claiming to offer european cloud solutions only three met all six criteria.

00:07:54: wow

00:07:55: Just three.

00:07:56: yeah key core leaf cloud and infomaniac only

00:07:59: three out of

00:07:59: thirty.

00:08:00: that is a massive signal-to-noise ratio And it really highlights just how much Sovereignty washing is happening in the market right now.

00:08:07: Oh, absolutely.

00:08:08: But you know as your Outlining jogovitch's point about operational control It triggered another thought.

00:08:14: You cannot achieve true operational control if you don't have absolute financial control over your infrastructure.

00:08:20: That is a very good point!

00:08:21: If you do not know what it costs to process your data, or those costs can just spiral without any warning... ...you aren't really in control of the operating model at all!

00:08:29: No- you're completely at the mercy of consumption rate

00:08:32: Exactly.

00:08:33: And this leads right into the second theme The massive shifts we're seeing around cloud cost and phenops.

00:08:39: Yeah, this is a hot topic.

00:08:40: Nancy Maggia shared an insight recently that completely reframes the whole conversation.

00:08:45: She argues that the biggest risk in cloud environments right now isn't a technical failure

00:08:50: It's a financial failure.

00:08:51: Yes

00:08:52: it's that phenomenon where the budget just silently bleeds out microservice by microservice and her key takeaway is cultural.

00:09:00: She says this shift toward financial governance has to be led by IT, not

00:09:04: finance.".

00:09:05: That makes a lot of sense

00:09:06: Right.

00:09:07: And Francisco C backed this up with his experience working in the Elitam mid-market, he noted that common cloud wastes like having massive expensive development environments completely abandoned but still running and production.

00:09:19: Oh

00:09:19: I've seen so many times!

00:09:21: He says isn't a flaw of technical architecture.

00:09:24: it is direct symptom missing fenobs governance at engineering level Because

00:09:29: how we track costs has to change.

00:09:32: here The traditional model is totally reactive.

00:09:35: Finance gets a massive cloud bill at the end of month.

00:09:38: They panic.

00:09:38: they walk over to IT and ask What happened thirty days ago?

00:09:42: Yeah, and by then it's too late.

00:09:44: exactly that post deployment analysis is entirely dead.

00:09:49: Nicholas fondrini argues That fine ops and devops must converge At the pipeline level.

00:09:55: cost visibility has become a real-time signal embedded directly in the CI CD pipeline.

00:10:01: So, practically speaking what does that actually look like for an engineer day-to-day?

00:10:05: Okay

00:10:05: imagine a developer writes new piece of infrastructure as code and submits pull request.

00:10:10: In Fundrini's Converge model before the code is even allowed to merge An automated FinOps tool analyzes it.

00:10:17: Oh I see.

00:10:17: And posts comments saying hey this architectural change will increase our monthly AWS bill by four thousand dollars.

00:10:23: Wow

00:10:23: right there in pipeline.

00:10:24: Yep

00:10:25: The engineers sees financial impact while the architectural decision still completely reversible.

00:10:30: It shifts costs from a lagging financial metric to a leading engineering constraint.

00:10:35: Okay, embedding cost checks into standard infrastructure pipelines makes total sense but how does the supply when we introduce The Wild West of AI and large language models?

00:10:46: Uh yeah that's the tricky part

00:10:47: because Standard Compute is somewhat predictable.

00:10:50: But with LLMs, a slight change in how an application prompts a model or a sudden spike in user queries and your token consumption can blow through your monthly budget.

00:11:00: In like an afternoon

00:11:01: standard phenops cannot handle LLM economics.

00:11:05: it just can't.

00:11:05: It requires a completely new playbook And Venkat Reddy Chintalapudi laid out what an LLm phenop strategy actually looks Like.

00:11:12: okay What's his take?

00:11:13: They

00:11:14: require shifting the engineering mindset away from traditional cloud cost levers buying reserve capacity, and moving toward dynamic routing decisions.

00:11:23: Walk me through a routing decision.

00:11:24: how does that practically save money without degrading the AI's performance?

00:11:28: Think of it like a highly intelligent traffic cop sitting at your API gateway.

00:11:32: if a user asks you application very simple question like summarize this short email The Gateway recognizes low complexity.

00:11:41: It routes that prompt to smaller open source eight billion parameter model That costs fractions of ascent to run.

00:11:49: Right,

00:11:49: you don't need a massive model for that?

00:11:50: Exactly!

00:11:51: Yeah.

00:11:51: But if user asks the application to debug a complex block of Python code The gateway recognizes high complexity and routes it into a massive expensive frontier model like GPT-IV.

00:12:02: Oh thats brilliant.

00:12:03: Chantelapudi notes that implementing this dynamic routing layer alone can yield thirty to fifty percent savings because you aren't using a supercomputer to do basic arithmetic.

00:12:13: That is such a smart way of handling it!

00:12:15: It treats AI models like tiered storage, where only use the expensive high-performance tier when workload actually demands them.

00:12:22: But even with routing how do measure if that spend was worth?

00:12:26: Well...that's next evolution.

00:12:28: and George Pilatus insists that AI Finoms has fundamentally changed its metrics.

00:12:32: Historically we measure raw consumption.

00:12:35: how many gigabytes of storage or hours of compute.

00:12:37: With AI, you can't just measure raw token consumption Or How Many Lines Of Code And AI Coding Assistant Generated.

00:12:44: Politis argues You have to Measure Tokens Per Verified Outcome

00:12:48: Because bad code generated quickly is still bad code.

00:12:52: Exactly If your AI generates thousands of lines of code consuming massive amounts of tokens But that code introduces security flaws and requires Massive Human Rework To Actually Function In Production

00:13:04: then your ROI is profoundly negative.

00:13:07: You burn cash to create technical debt, you have to measure the cost against this successfully verified business outcome not just the raw output of a machine.

00:13:15: Token's per-verified out come.

00:13:17: I love that!

00:13:18: It has fantastic metric.

00:13:19: it also perfectly highlights human element and all automation which ties into a very memorable quote shared by JR Stormant.

00:13:26: Oh, I know the one!

00:13:27: Yeah he noted that despite all of the automated pipelines and AI routing get-ways, Phenop's practitioners remain the essential squishy human meatballs required in every emerging

00:13:37: workflow.

00:13:37: It is an evocative phrase for sure Very

00:13:39: squishy

00:13:40: But Stormant is absolutely right.

00:13:42: Algorithms optimize but humans strategize.

00:13:46: GERPRIT SING explains exactly how these Phenops teams need to operate strategically, particularly when managing up the C-suite.

00:13:54: He advises using the five Ys technique but applying it to corporate strategy rather than just root cause analysis.

00:14:00: So instead of just using it to figure out why a server crashed you use it to dissect leadership's intentions?

00:14:08: Precisely Yeah.

00:14:09: When a CEO walks into the room and asks, what are we spending on AI?

00:14:13: The absolute worst thing if you an ops team can do is just hand them a dashboard with a dollar

00:14:17: amount.

00:14:18: they need to dig deeper.

00:14:19: They need ask the first why?

00:14:21: maybe the ceo says that one know how different product teams or utilizing AI?

00:14:24: Why?

00:14:26: because I needed decide which infrastructure to double down on.

00:14:28: why?

00:14:29: Because they want to accelerate the release of specific features said to drive innovation.

00:14:34: Y To capture market share from a competitor in a new vertical.

00:14:38: By the time you get to the core reason, you realize that CEO doesn't actually want us spend report at all.

00:14:43: They are trying make a highly complex strategic investment decision

00:14:48: And human element that Phenops professional has to uncover that real context.

00:14:54: If they don't...they're just delivering clean spreadsheets That have zero influence on actual business outcomes.

00:15:00: Reframing elevates the Phenop's role from glorified bookkeeping to strategic architectural advisory.

00:15:08: And that links directly to our final focus area today, AI cloud and architecture.

00:15:13: Yeah this is where it all comes together because

00:15:15: you can't achieve those strategic phenops outcomes or the sovereign data boundaries unless.

00:15:28: Aiman Hussein hammered this point home recently.

00:15:31: He noted that cloud buying is no longer a technology procurement decision where you just compare feature sets between vendors, right?

00:15:38: It has entirely evolved into an operating model decision.

00:15:42: it's about value realization managing decentralized teams and navigating complex ecosystems

00:15:48: And Karyapitay echoed this exact sentiment.

00:15:52: He stated that the real AI-raised enterprises are running right now isn't about raw AI capability.

00:15:58: It's a battle over operating models, governance and architectural control inside the enterprise

00:16:04: And we shouldn't view this shift in thinking as something reserved only for bleeding edge AI startups Right?

00:16:10: No not at all.

00:16:11: it applies to core legacy Enterprise systems too.

00:16:14: Henrik Hausen pointed out that when massive companies are debating whether to move to SAP Sforonic Cloud versus keeping their ERP on-premise, they usually treat it as a technical feature comparison.

00:16:25: But fundamentally, Hausin argues is leadership and operating model decision.

00:16:29: It's a choice about how much strict standardization the company is willing to enforce across its business units versus How much bespoke customization they believe?

00:16:36: They truly need to survive.

00:16:38: so The technology choices entirely secondary to the operating model choice.

00:16:42: Yeah, but here Is where this operating model shift gets really interesting and honestly A bit daunting for the workforce.

00:16:48: Oh the talent question.

00:16:49: yeah If operating models are shifting heavily toward AI automation and we're building these dynamic routing gateways to handle the complexity, do we still need traditional cloud infrastructure engineers?

00:17:04: Or our AI agents just gonna write all of their deployment scripts for

00:17:07: us.

00:17:07: It's probably most common anxiety in industry right now especially when you see real world examples like one Geert Audenard shared.

00:17:14: What

00:17:14: happened there?

00:17:15: Well, he had a customer who needed a very specific command executed across all virtual machines within a whiteski.cloud environment.

00:17:23: The platform's API supported the action but There was no native bulk function in the dashboard.

00:17:29: Okay so manual slog

00:17:30: Right.

00:17:31: So instead of spending hours writing and testing a script manually He asked an AI coding agent to write a Python script for it.

00:17:38: Five minutes later the script was written tested And the problem was solved.

00:17:42: Stories like that make it sound human infrastructure engineering is on its way out.

00:17:46: I mean, five minutes to bypass what used to be a half-day ticket?

00:17:49: It definitely sounds that way but the reality is actually the exact opposite.

00:17:54: Human infrastructure skills are becoming significantly more critical.

00:17:57: But then nature of this skills as shifting shifting

00:18:00: how.

00:18:00: for

00:18:00: shock aside Wani points at a fascinating trend.

00:18:03: The next generation of highly paid DevOps engineers Are going to be ones operating and maintaining AI systems.

00:18:10: Oh interesting Why?

00:18:12: Because running AI at an enterprise scale is not a data science problem.

00:18:17: It's fundamentally massive distributed systems and networking problems,

00:18:20: meaning the data scientists build the model but getting them to actually run without crashing network.

00:18:26: that isn't engineering problems

00:18:28: exactly.

00:18:28: you're dealing with massive clusters of GPUs need communicate microsecond latency.

00:18:34: You need deep knowledge of Kubernetes, GPU scheduling complex ingress controllers and deep observability just to see where the bottlenecks are.

00:18:43: So the AI might write a Python script in five minutes, but humans have to architect and maintain the highly complex distributed network that keeps that AI agent running efficiently and securely in the first place.

00:18:55: Exactly!

00:18:55: But that brings up a really frustrating paradox.

00:18:58: David Lenthicom highlighted... If these highly skilled human engineers are so essential Why is the cloud job market currently so broken?

00:19:09: It's incredibly broken.

00:19:10: He pointed out this massive disconnect, we constantly hear executives complaining about a severe talent shortage yet highly capable infrastructure professionals can't land roles.

00:19:21: he blames a systemic failure and how companies hire.

00:19:24: The market has flooded with fake-job postings bloated requirements where companies try to roll three distinct engineering disciplines into one salary.

00:19:32: Oh the unicorn job description.

00:19:34: Yes It's a massive signaling problem.

00:19:45: Companies don't know how to hire for the operating models they are actually trying.

00:19:51: For engineers trying to cut through that noise and actually build value, Riyaz Syod offered some incredibly practical advice.

00:19:58: He says engineers shouldn't try to chase the latest fifty AWS micro tools just to pad their resume with keywords.

00:20:05: instead they need a master.

00:20:06: five two seven core foundational services deeply Things like EC-II, S-III, identity and access management.

00:20:14: And core networking fundamentals like VPC in Route fifty.

00:20:17: three because

00:20:18: the basics never change

00:20:19: exactly.

00:20:20: Because when a massive distributed AI system breaks it's usually not The shiny new AI tool that failed It's a foundational networking or IAM permissions issue.

00:20:30: deep foundational knowledge is what actually solves real-world architectural

00:20:34: problems.

00:20:35: So let's bring this full circle, connecting the architecture back to where we started with sovereignty and compliance.

00:20:39: Okay How do these operational architectures hold up when we introduce autonomous AI into the mix?

00:20:45: They often break completely And This Is Where Andreas Hamburger provided a very stark warning that ties all our themes together.

00:20:51: today

00:20:51: What's

00:20:51: The Warning?

00:20:52: As organizations move rapidly toward egenic AI where AI agents aren't just answering prompts, but are autonomously making API calls and completing multi-step tasks across the network.

00:21:03: We have to fundamentally rethink how compliance works at the architectural

00:21:08: level.

00:21:09: Break down that mechanism there for us – why does an AI agent threaten compliance if data is already secure?

00:21:15: Because of how data is handled during reasoning….

00:21:18: When an AI Agent accesses a database It is actively processing that data in memory, not just storing it.

00:21:27: Standard at rest encryption which are the bedrock of most compliance frameworks provides absolutely no protection during that active reasoning phase.

00:21:34: The data has to be decrypted to be analyzed by the model

00:21:37: Right.

00:21:37: so if your storage isn't a highly sovereign compliant environment and Frankfurt

00:21:41: But you're AI inference layer?

00:21:43: The compute knows actually doing the reasoning Is running through different less secure cloud region because was cheaper or had available GPUs.

00:21:50: Your sovereignty is instantly broken.

00:21:52: Yeah, you've leaked decrypted highly sensitive data outside your protected boundary architecture.

00:21:58: teams must ensure that the inference layer sits within the exact same compliance and geographic envelope as the data storage itself.

00:22:05: Otherwise, all of money you spend on fine-ups governance—and all legal battles you fought for data residency —are built upon a completely compromised architectural

00:22:13: foundation.".

00:22:14: Exactly!

00:22:15: That is phenomenal insight.

00:22:16: to end on….

00:22:17: The architecture has support your compliance and sovereignty at this moment not just when it's sitting idle in storage...

00:22:37: Thank you so much for joining us on this deep dive.

00:22:39: Don't forget to subscribe, bulletproof sovereign architectures, but as the genetic AI matures and these agents begin making their own autonomous cloud consumption API routing decisions in real time.

00:23:00: Who governs the AI when the AI effectively becomes your primary cloud operator?