The most valuable skills by 2030 won’t be technical.
They’ll be strategic:
Sharing key insights from Jeroen Kraaijenbrink (@JKraaijenbrink) and the World Economic Forum (@wef)
The future belongs to people who can:
Think clearly, adapt quickly, and lead effectively.
Top high-impact skills include:
A) Systems thinking
B) Creative thinking
C) Analytical thinking
D) Self-awareness and motivation
E) Leadership and influence
F) Resilience and adaptability
G) Curiosity and lifelong learning
These are no longer optional skills.
They’re becoming the foundation of long-term success in business and life.
AI and digital tools will keep evolving,
But tools alone don’t create results.
People do.
The real competitive edge in 2030 will come from:
Better decisions.
Faster execution.
Stronger leadership.
That’s what will separate top performers from everyone else.
P.S. Which of these skills are you currently working on?
♻️ Share this to help others prepare for the future.
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Technology doesn't scale itself. Distribution does.
Today we published additional detail on Krytheon's path from piloted FX activity to deployment through a telecom ecosystem spanning 23 countries.
https://t.co/c3RFduXQ3b
#Krytheon#Fintech#Payments#FX
Krytheon engages Hudson Capital Advisors as a strategic capital markets advisor to support the next phase of growth across cross-border payments, treasury coordination, digital wallets, and AI-enabled enterprise infrastructure.
@aaorial#FinTech#AI#Payments#CapitalMarkets
Krytheon announced the continued integration of treasury, FX and operational intelligence infrastructure into its platform for regulated cross-border coordination and workflow execution.
https://t.co/dyZwpgfx4z
#Treasury#FX#Fintech#EnterpriseInfrastructure@aaorial
Payment speed has improved. Execution has not.
Cross-border systems remain fragmented across identity, workflow, compliance, and payments.
https://t.co/hrHcZPvqwv
#Fintech#Payments
Krytheon appoints Aylin Orial as CEO and Chief Innovation Officer, supporting initial platform deployment https://t.co/uVqWOQz31T via @ein_news@aaorial#fintech
People think learning AI takes months.
It's really just a couple of hours.
And I wrote 17 free guides to start right away:
Claude 101: https://t.co/Jv1jsvFB7T
Claude Code: https://t.co/WYZd5ltnXo
Claude Skills: https://t.co/jT4uB5AFtY
Nano banana 2: https://t.co/qfHT594CCI
Claude in Excel: https://t.co/mfcXYSA57j
Best AI for Search: https://t.co/77BmjbJjP0
1M followers with AI: https://t.co/1TV9LYAptv
Claude for your team: https://t.co/U1JsBVC299
No prompt saves you: https://t.co/SDKJWykHE4
AI Slides (PPT in 2026): https://t.co/RfcyYRQ2Ad
Set up Claude Cowork: https://t.co/diDhiKjHtU
Claude to sound like you: https://t.co/99RzxXTvzs
Claude interactive charts: https://t.co/ebCHGZqgPt
Claude as your computer: https://t.co/TxYuHPiImn
Claude Cowork + Project: https://t.co/Q7AN9CZ2mg
You're an AI workaholic: https://t.co/mCIvB3ZPA5
Setup AI before prompting: https://t.co/pE3OF722aw
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BREAKING: AI can now analyze any stock like a Wall Street analyst (for free).
Here are 10 insane Claude prompts that replace $2,000/month Bloomberg terminals:(Save for later)
Google DeepMind just published the most important AI safety paper of 2026.
And almost nobody is talking about it.
"Intelligent AI Delegation" - a framework for how AI agents should hand off work to other agents and humans.
Why does this matter?
AI agents are getting more capable. But they can't actually delegate.
Not really.
They can break tasks into pieces. They can call other agents. But that's not delegation.
Real delegation requires:
➡️ Transfer of authority
➡️ Assignment of responsibility
➡️ Clear accountability
➡️ Trust calibration
➡️ Permission handling
➡️ Verification of completion
Current multi-agent systems have none of this.
They're just parallelization with extra steps.
As we move toward millions of specialized AI agents embedded in firms, supply chains, and public services - the delegation problem becomes critical.
Without it:
➡️ No clear accountability when things fail
➡️ No trust mechanisms between agents
➡️ No way to verify task completion
➡️ Cascading failures across agent networks
This paper is the foundation for how the agent economy will actually work.
🚨BREAKING: Microsoft Research + Salesforce just dropped a paper that should scare every AI builder.
They tested 15 top LLMs GPT-4.1, Gemini 2.5 Pro, Claude 3.7 Sonnet, o3, DeepSeek R1, Llama 4 across 200,000+ simulated conversations.
Single-turn prompt: 90% performance.
Multi-turn conversation: 65% performance.
Same model. Same task. Just... talking normally.
The culprit isn't intelligence. Aptitude only dropped 15%.
Unreliability EXPLODED by 112%.
→ LLMs answer before you finish explaining (wrong assumptions get baked in permanently)
→ They fall in love with their first wrong answer and build on it
→ They forget the middle of your conversation entirely
→ Longer responses introduce more assumptions = more errors
Even reasoning models failed. o3 and DeepSeek R1 performed just as badly.
Extra thinking tokens did nothing.
Setting temperature to 0? Still broken.
The fix right now: give your AI everything upfront in one message instead of back-and-forth.
Every benchmark you've seen was tested on single-turn prompts in perfect lab conditions.
Real conversations break every model on the market and nobody's talking about it.
Fintech is quietly back in the spotlight.
It now captures 11% of global venture funding, second only to AI. Rounds are getting larger, and investors are focusing less on hype and more on business models that actually scale.
The signal matters. As AI reshapes infrastructure, fintech sits right at the intersection of money, data, and trust. Is this the start of a sustained rebound, or just a selective reset favoring the strongest players?
https://t.co/R24tyIUXVa @FintechCH@CBinsights
🧠Beyond Transformers: The Rise of New AI with Infinite Context
Transformers changed everything, but let's be real: the "Context Window" is a bottleneck.
We are officially entering the era of Infinite Context with new AI models that will introduce "Neural Memory," allowing AI to "To memorize and train itself on your data as it reads it.
Read more: https://t.co/56lTISUVG5
Memory in AI agents seems like a logical next step after RAG evolved to agentic RAG.
RAG: one-shot read-only
Agentic RAG: read-only via tool calls
Memory in AI agents: read-and-write via tool calls
Obviously, it's a little more complex than this.
I make my case here: https://t.co/KyzloUFmw4
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