Artificial intelligence is transforming space missions from reactive operations to autonomous decision-making systems. But innovation without governance creates risk. The STL/IDAI framework shows why compliance, validation, and trust are now mission-critical components of every AI-powered space system.
In this article, David Beck, Director of the Center for Industrial Innovation Systems at the Institute for Digital Asset Innovation (@IDAI_Global), explores that from navigation and predictive maintenance to anomaly detection and Earth observation, AI is enabling spacecraft to act independently in deep space.
Read more 👉 https://t.co/14XRuhiExQ
#IDAI
Using AI is easy. Mastering it happens in levels.
Most people start by asking ChatGPT a few questions.
But real AI growth begins when you move from simply using tools to building workflows, agents, systems, and eventually new models.
Here is how the journey progresses:
Level 0: AI Awareness
Understand AI, machine learning, generative AI, hallucinations, and practical use cases.
Level 1: AI User
Write better prompts, research faster, summarize documents, and improve everyday productivity.
Level 2: AI Power User
Use prompt chaining, custom instructions, projects, context, and structured outputs to build repeatable workflows.
Level 3: AI Creator
Generate professional text, images, videos, presentations, voice, and social media content.
Level 4: AI Automation Builder
Connect apps, APIs, triggers, webhooks, and approval steps to automate business processes.
Level 5: AI Agent Builder
Build agents that plan, use tools, retrieve information, collaborate, remember context, and complete tasks.
Level 6: AI Engineer
Develop production-ready AI applications using Python, APIs, databases, RAG, deployment, monitoring, and security.
Level 7: AI Architect
Design secure, scalable, observable, cost-efficient, and governed enterprise AI platforms.
Level 8: AI Researcher
Train, evaluate, align, and improve advanced AI models.
You do not need to reach Level 8 to create value.
The right target depends on what you want to build, automate, or lead.
Which level best describes where you are today?
If you’re building your brand as a founder,
Remember: people trust people - not tools.
And LinkedIn is where that trust starts.
Let’s build yours.
Book your free call here : https://t.co/LkIlyNLj9M
Great Post from Denis Panjuta👏🏼
One call isn't a breakthrough: until it starts a flood. 🌊 Growth lives in the small, intentional ripples that build momentum. How are you starting yours today? #LeadGen#Growth#Callbox#B2B
Smart meters have moved utilities from monthly truck rolls to real-time grid visibility. But as the network expands, so does the attack surface. Standardized security is the only way to protect these distributed endpoints. #SmartGrid#AMI https://t.co/VCM6CQX84p
🏗️ What if the future of #AI isn't being built in billion-dollar #DataCenters... but in tents?
#Meta is reportedly deploying rapidly assembled, modular "tent-like" structures to expand AI compute capacity in months instead of years.
Watch episode: https://t.co/UFDrARiRe6
What happens when AI agents move from answering questions to taking action across enterprise systems?
I explored this in my new @LinkedIn post with @GraviteeIO : why the shift from API-first to agent-first will require stronger visibility, governance, security, and cost control.
Read it here: https://t.co/xqla3oWB37
#AI #GraviteePartner #AgenticAI
Can AI and Energy Scale Together?
AI is no longer only a software story.
It is becoming an infrastructure story, one shaped by electricity, grids, data centers, cooling, connectivity, capital, and long-term energy planning.
That is why the convergence of artificial intelligence, computing infrastructure, and energy systems is becoming one of the defining strategic questions of this decade.
Here are five reasons this matters:
1. AI growth is becoming an energy demand story
Every AI model, autonomous system, cloud platform, and intelligent application depends on physical infrastructure. As adoption accelerates, so does demand for reliable electricity, high-performance computing, cooling, and resilient power systems.
2. Data centers are becoming critical energy infrastructure
The next generation of data centers will not be planned in isolation. Their growth will depend on grid capacity, energy availability, permitting, location, connectivity, and long-term power strategies.
3. AI can also make energy systems smarter
AI is not only consuming energy. It is helping optimize it.
Across the energy value chain, AI can improve forecasting, predictive maintenance, asset performance, grid management, energy trading, infrastructure planning, and operational decision-making.
4. Energy security will shape the pace of AI adoption
The AI economy cannot scale on unreliable infrastructure. Countries and companies with resilient grids, diversified supply, flexible capacity, and strong digital infrastructure will be better positioned to capture the next wave of AI-driven growth.
5. Asia will be central to this convergence
Asia is already at the center of rising electricity demand, industrial expansion, data center investment, and digital infrastructure growth. The region’s choices around energy, computing, policy, and investment will influence the future of the global AI economy.
This is why Gastech 2026 and AIxEnergy arrive at such an important moment.
AI needs energy.
Energy increasingly needs AI.
The real opportunity is not simply to scale both independently, but to design them as one interconnected system.
Learn more about Gastech 2026 and AIxEnergy, and register as a delegate:
https://t.co/PhyuhvDyaD
#Gastech2026 #AIxEnergy #ArtificialIntelligence #AIforEnergy #EnergyInfrastructure #DataCenters #EnergySecurity #DigitalInfrastructure #PowerGrids #EnergySystems #Bangkok
A spacecraft near Mars can’t wait 40 minutes for Earth to make every decision. That’s why AI has become essential for modern space operations.
In this article, David Beck, Director of the Center for Industrial Innovation Systems at the Institute for Digital Asset Innovation (@IDAI_Global), explains that the future of space exploration will depend not just on smarter AI, but on responsible governance.
Read more 👉 https://t.co/CVYV9pduFf
#IDAI
Sat down with Jason Schroedl, who runs enterprise platforms at NVIDIA, at HPE Discover.
The term "AI factory" gets thrown around a lot. A year ago it was a slide title. Now it's a product you can actually buy. HPE and NVIDIA built the full stack together, from Vera Rubin and the new Vera CPU to Spectrum-X networking and RTX Pro GPUs, and packaged it into turnkey systems like HPE Private Cloud AI. The real story isn't the hardware. It's that a company can be running in weeks instead of spending a year building an AI platform from nothing.
The proof is who's already using it. St. Jude, Siemens Energy, Roche, Vultr, even the Dallas Cowboys and the Ryder Cup. Different industries, same shift from demo to production.
Jason's take: we're still in the early innings for enterprise AI. But with confidential computing keeping data and models protected on prem, and agent tools like NeMo built in, the gap between a good demo and something in production is closing fast.
@nvidia@HPE #HPEpartner #HPEdiscover
Full conversation below.
Largest Data Center Project Ever Proposed Is Officially Dead
“A Gallup survey released in May found 71% of Americans oppose data center construction in their area, with 48% strongly opposed, running higher than opposition to a local nuclear plant.”
https://t.co/PG7J9iJM0Z
Leadership news: Nathan McGregor has been appointed Head of Sales & Go-to-Market. Nathan will lead our global sales organization, helping customers and partners unlock greater value from enterprise wireless, private cellular, and 5G.