"Digital Ray: 80% of Ray Dalio's accuracy for the masses?"

💭 Beyond the AI Hype: The Reality We Don’t Talk About It’s time for me to start sharing my unfiltered thoughts about AI — what I’ve seen, what I’ve learned, and what’s really happening behind all the noise. Over the past year, I’ve been deeply involved in implementing AI assistants — both public and private — and building automation for business operations. Working across the Baltic and Scandinavian markets gives me a clear view of where AI is actually going… and where it’s not. And honestly? Most people and companies still hesitate to adopt AI. Why? Because they: – Don’t really understand what it can or can’t do. – Don’t know where it fits into their processes. – Are afraid of what the media and hype keep repeating — that AI will take our jobs, outsmart us, and maybe even replace us. But here’s what I see: AI is not the enemy. It’s just deeply misunderstood. The “hype” around Artificial General Intelligence makes it sound like it’s just around the corner, but in reality, it’s not even close. Every day, I spend 2–3 hours tracking what’s happening in this AI “bubble.” Big tech companies are buying, investing, merging — throwing billions into the game. From the outside, it can look chaotic, even suspicious. But inside, it’s all about risk mitigation — no one wants to miss out if AI truly takes off. Public data shows — these corporations have been sitting on huge piles of cash for years, much of it offshore, with nowhere to invest. Now, with the AI era exploding, they’re throwing that money into anything with “AI” in its name, hoping something groundbreaking will come from it. Data says that 96% of AI startups sink; the 4% that survive could redefine entire industries, and that's in a good scenario. That's good enough for corporations. Imagine investing your money with a 4% chance of success. Next time, I’ll go deeper — into what this AI wave really looks like from the inside, and why it reminds me so much of the crypto era.

Let me tell you about a time when I realized the game-changing potential of AI in trading. It was 2025, and I watched as XYZ Capital's AI trading coach boosted their portfolio returns by 35%. That's when it hit me: AI isn't just a tool; it's a REVOLUTIONARY force in finance. True Labs is taking this revolution further! They just raised $10M to democratize advanced trading tools. Their AI-driven platform is like having a top-tier mentor—constantly learning, adapting, and helping traders avoid common pitfalls. Imagine AI that evolves with market dynamics... Sounds mind-blowing, right? Here's why this matters: - AI trading tools are projected to grow by 25% CAGR, reaching $16B by 2025. - 93% of hedge funds have already embraced AI, setting new standards. - But, 60% of traders struggle due to lack of expertise. So, how do we stay ahead? - Embrace ongoing education in AI. - Align with platforms like True Labs for personalized strategies. - Advocate for ethical frameworks in AI trading. We're on the brink of a paradigm shift—let's innovate and grow together! How are you preparing for this AI-driven future? https://lnkd.in/g7Q-8Hsn

🔥 Hot take: We're not close to AGI, and current AI won't ever get us there. Everyone's talking about AI "thinking" and becoming superintelligent. But here's what's actually happening: AI today is basically really good autocomplete. It looks at patterns in massive amounts of text and predicts what words should come next. It's not thinking, reasoning, or understanding anything. It's doing extremely sophisticated pattern matching. Four problems we can't solve by just making AI bigger: Can't do basic logic - Train it on small math problems, it fails on bigger ones. It memorizes patterns instead of actually calculating. Same input, different output - Ask the exact same question twice, get two different answers. There's nothing reliable about it. Tiny changes break everything - Reword your question slightly and the whole answer changes. That's not understanding. Confidently wrong - It generates whatever sounds statistically likely, not what's true. It'll assert complete nonsense with total confidence. Why "just add more data" won't work: We've already scraped most of the internet and all known content. Now AI is training on AI-generated content, which is like making a photocopy of a photocopy—quality degrades fast. More computing power makes it better at patterns, not capable of actual thought. The bottom line: Current AI is incredibly useful (I use it daily). But it's not on a path to consciousness, emotion, or true reasoning. AGI will require completely different technology—not just bigger versions of what we have now. About that "AGI in 2-3 years" hype: Elon Musk, Sam Altman, Jensen Huang, etc—the people making these predictions are the same ones making billions from AI investments. Sound familiar? It's the same playbook as the crypto boom or the VR revolution that never came. Crypto and VR are still around, and AI will stay around too. But soon the investment bubble of AI will pop, as all bubbles do. Mark my words! This is hype driving investment, not science driving progress. AI will change how we work. But it won't replace human thinking anytime soon in my opinion.

𝘑𝘶𝘴𝘵 𝘢𝘴𝘬 𝘊𝘩𝘢𝘵𝘎𝘗𝘛 🤖𝙄 𝙙𝙤𝙣'𝙩 𝙣𝙚𝙚𝙙 𝙮𝙤𝙪. 𝙄'𝙡𝙡 𝙟𝙪𝙨𝙩 𝙖𝙨𝙠 𝘾𝙝𝙖𝙩𝙂𝙋𝙏 𝙝𝙤𝙬 𝙩𝙤 𝙢𝙖𝙠𝙚 𝙢𝙤𝙣𝙚𝙮. Heard this twice this week. I get this comment A LOT lately: 𝙒𝙝𝙮 𝙥𝙖𝙮 𝙛𝙤𝙧 𝙚𝙙𝙪𝙘𝙖𝙩𝙞𝙤𝙣 𝙬𝙝𝙚𝙣 𝘼𝙄 𝙘𝙖𝙣 𝙩𝙚𝙡𝙡 𝙢𝙚 𝙬𝙝𝙖𝙩 𝙩𝙤 𝙗𝙪𝙮/𝙨𝙚𝙡𝙡 𝙞𝙣 𝙧𝙚𝙖𝙡-𝙩𝙞𝙢𝙚? Fair question. Let me show you something. These are actual AI trading models tested on real markets. Here's what happened: DeepSeek: +22% ✅ Qven3 Max: +18% ✅ Claude Sonnet: -31% ❌ Grok 4: -42% ❌ Gemini Pro: -63% ❌ GPT 5: -67% ❌ So 4 out of 6 lost money. One AI lost 67% of capital. Here's the thing about 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗔𝗜 𝗮𝗱𝘃𝗶𝗰𝗲: 1️⃣ It's trained on historical data (not future data) 2️⃣ It has no skin in the game (doesn't care about YOUR loss) 3️⃣ It hallucinates. A LOT. (Sounds confident while being dead wrong) 4️⃣ It doesn't understand risk management 5️⃣ Every crypto bro asking GPT "should I buy this?" already lost money What you actually need: ✅ A SYSTEM (not a prediction) ✅ RULES (not vibes) ✅ RISK MANAGEMENT (not hope) ✅ EMOTIONAL DISCIPLINE (AI can't teach this) AI can give you information. But it can't give you wisdom. And wisdom is expensive. Because it's rare. The question isn't: 𝗪𝗵𝘆 𝘀𝘁𝘂𝗱𝘆 𝘄𝗵𝗲𝗻 𝗔𝗜 𝗲𝘅𝗶𝘀𝘁𝘀? The question is: 𝗪𝗵𝘆 𝗶𝘀 𝟲𝟳% 𝗼𝗳 𝗔𝗜 𝗹𝗼𝘀𝗶𝗻𝗴 𝗺𝗼𝗻𝗲𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗮𝗿𝗸𝗲𝘁? Because it doesn't understand what makes a REAL investment system work. That's why people book strategy calls with me 👇

𝘑𝘶𝘴𝘵 𝘢𝘴𝘬 𝘊𝘩𝘢𝘵𝘎𝘗𝘛 🤖𝙄 𝙙𝙤𝙣'𝙩 𝙣𝙚𝙚𝙙 𝙮𝙤𝙪. 𝙄'𝙡𝙡 𝙟𝙪𝙨𝙩 𝙖𝙨𝙠 𝘾𝙝𝙖𝙩𝙂𝙋𝙏 𝙝𝙤𝙬 𝙩𝙤 𝙢𝙖𝙠𝙚 𝙢𝙤𝙣𝙚𝙮. Heard this twice this week. I get this comment A LOT lately: 𝙒𝙝𝙮 𝙥𝙖𝙮 𝙛𝙤𝙧 𝙚𝙙𝙪𝙘𝙖𝙩𝙞𝙤𝙣 𝙬𝙝𝙚𝙣 𝘼𝙄 𝙘𝙖𝙣 𝙩𝙚𝙡𝙡 𝙢𝙚 𝙬𝙝𝙖𝙩 𝙩𝙤 𝙗𝙪𝙮/𝙨𝙚𝙡𝙡 𝙞𝙣 𝙧𝙚𝙖𝙡-𝙩𝙞𝙢𝙚? Fair question. Let me show you something. These are actual AI trading models tested on real markets. Here's what happened: DeepSeek: +22% ✅ Qven3 Max: +18% ✅ Claude Sonnet: -31% ❌ Grok 4: -42% ❌ Gemini Pro: -63% ❌ GPT 5: -67% ❌ So 4 out of 6 lost money. One AI lost 67% of capital. Here's the thing about 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗔𝗜 𝗮𝗱𝘃𝗶𝗰𝗲: 1️⃣ It's trained on historical data (not future data) 2️⃣ It has no skin in the game (doesn't care about YOUR loss) 3️⃣ It hallucinates. A LOT. (Sounds confident while being dead wrong) 4️⃣ It doesn't understand risk management 5️⃣ Every crypto bro asking GPT "should I buy this?" already lost money What you actually need: ✅ A SYSTEM (not a prediction) ✅ RULES (not vibes) ✅ RISK MANAGEMENT (not hope) ✅ EMOTIONAL DISCIPLINE (AI can't teach this) AI can give you information. But it can't give you wisdom. And wisdom is expensive. Because it's rare. The question isn't: 𝗪𝗵𝘆 𝘀𝘁𝘂𝗱𝘆 𝘄𝗵𝗲𝗻 𝗔𝗜 𝗲𝘅𝗶𝘀𝘁𝘀? The question is: 𝗪𝗵𝘆 𝗶𝘀 𝟲𝟳% 𝗼𝗳 𝗔𝗜 𝗹𝗼𝘀𝗶𝗻𝗴 𝗺𝗼𝗻𝗲𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗮𝗿𝗸𝗲𝘁? Because it doesn't understand what makes a REAL investment system work. That's why people book strategy calls with me 👇

Is the AI Boom Running Ahead of Itself? The latest coverage highlights a growing unease across the investment community: while the promise of artificial intelligence is enormous, valuations in the tech sector are beginning to raise red flags. Goldman Sachs CEO recently noted that a 10–20 % market draw-down may be on the cards an echo of the dot-com era. What’s happening: Stocks tied to AI have experienced a pull-back as investors reassess how much of the future value is already priced in. Regulators and major institutions warn about “stretched” valuations in tech and AI sectors. The risk: If the expected productivity gains and revenue streams from AI aren’t realised as quickly as the hype suggests, a sharp correction could follow. What this means for professionals and decision-makers: Revisit assumptions: Are your business cases and investment models factoring in a more conservative timeline for AI impact? Diversification matters: In a sector potentially poised for correction, exposure to non-tech or value-oriented segments may offer ballast. Focus on fundamentals: Prioritize entities with clear revenue models from AI/machine learning-driven products or services, not just hype. Stay alert: Institutional warnings are no longer fringe commentary, they’re showing up in policy and risk frameworks.

I am recently conducting a research on the AI adoption in the Finance Industry and here's what I've noticed True industry leaders aren't debating AI adoption. They've been deploying it for years and are now fine-tuning advanced versions. Let's take JPMorganChase for example: →They've used AI since 2012, → Hired their first head of Machine Learning in 2014 And today? It's fueling their edge over competitors by saving thousands of legal work hours annually. How their Contract Intelligence (COiN) platform is revolutionizing contracts: → Analyze 12,000+ loan agreements in seconds → Extract key clauses automatically → Assess risk with machine learning → Ensure regulatory compliance → Flag anomalies humans would miss The Impact: ✅️ Millions saved in legal costs ✅️ 99.9% faster processing ✅️ Significantly improved accuracy ✅️ Better risk assessment But here's what's really interesting: This isn't just about cost savings. It's about competitive advantage. While competitors spend weeks reviewing contracts, JPMorgan closes deals in days. While others hire more lawyers, JPMorgan reallocates talent to strategic work. While the industry debates AI adoption, JPMorgan already has years of deployment data. The lesson? Embrace AI now. It doesn't just save time—it redefines your edge in finance. Start small, scale smart, and watch your operations transform. What's one AI tool you're experimenting with in your work? Or where do you see the biggest untapped opportunity in finance? Drop your thoughts below—let's swap ideas and level up together! 👇 P.S: My research was assisted by AI tools—they helped me pull verifiable data from across the web in record time. Proof that embracing AI starts with the basics!

Mastering AI Isn’t About the Tools — It’s About the Prompts. That's according the Perplexity's internal guide on how to get more done with AI attached. To save you from reading all 43 pages, here is the summary: Key points 1) The biggest difference between average and exceptional AI output isn’t which platform you use — it’s how you ask. 2) Prompting is fast becoming a core skill. Think of it as talking to an analyst, not typing into a machine. The clearer and more structured your thinking, the better the model performs. 3) Here’s how to get more out of any AI — ChatGPT, Claude, Gemini, or Perplexity — today 👇 How to best write your prompts: 1️⃣ Be Clear and Specific Don’t say: “Tell me about crypto.” Do say: “Summarize the top 3 DeFi narratives likely to drive liquidity in Q4 2025, in bullet form.” Precision creates better direction. 2️⃣ Give Context and Assign Roles AI performs best when it knows who it is and who you are. Try: “You’re a macro strategist advising a digital-asset fund. I’m the CIO — outline a one-page weekly market brief.” 3️⃣ Control the Tone and Format Want a table, bullet points, or executive summary? Tell it. Structure your request like you would brief a team member. 4️⃣ Break It Down Complex questions yield vague results. Instead, build step by step: 👉 First, “Summarize the report.” 👉 Then, “Expand point 2 into actionable insights.” 👉 Finally, “Write this for LinkedIn.” 5️⃣ Iterate and Refine Don’t settle for the first draft. Tweak the tone, tighten focus, or ask “make this more data-driven.” AI improves when you guide it. Good luck !

𝗠𝗼𝘀𝘁 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜 𝗶𝗻 𝗶𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. That's backwards. While I am busy re-defining how AI changes institutional investing - I've learned that successful AI adoption starts with something far more fundamental: Trust. Not trust in the technology. Trust in the people implementing it. Here's what institutional investors actually care about when evaluating AI: ❌ "Machine learning algorithms can process millions of data points" ✅ "How do I explain this decision to my board?" ❌ "Our AI reduces processing time by 80%" ✅ "What happens when it's wrong?" ❌ "Cutting-edge neural networks" ✅ "Can my team actually use this?" The firms winning with AI aren't the ones with the most sophisticated tech. They're the ones who understand that institutional finance is built on accountability, transparency, and regulatory compliance. AI doesn't change that. It needs to work within that. Three lessons from decades in this space: 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 What decision needs to be made? Who's accountable? Then figure out how AI supports that—not replaces it. 𝟮. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 > 𝘀𝗼𝗽𝗵𝗶𝘀𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 A slightly less accurate model that you can explain beats a black box every time in regulated environments. 𝟯. 𝗔𝘂𝗴𝗺𝗲𝗻𝘁, 𝗱𝗼𝗻'𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 Institutional investors don't want AI making decisions. They want AI making them better at making decisions. The competitive shooting parallel (Yes I do that too)? The best equipment doesn't make you a better shooter. Control, precision, and discipline do. That's what AI should be doing for institutional investors. Read in detail on my substack: https://lnkd.in/g4NJBCFh What's your take? Are we over-complicating AI in finance? #AI #InstitutionalInvesting #Fintech #AssetManagement #Leadership

💡 AI and the Future of Equity Management: Beyond Efficiency Artificial Intelligence (AI) is no longer a futuristic concept—it's actively reshaping equity management right now, driving unprecedented speed and sophistication. Key ways AI is transforming investment decisions: • Alpha Generation: AI algorithms sift through vast datasets (fundamentals, news, technicals, and alternative data) far faster than humans, leading to sharper stock analysis and selection and more dynamic portfolio adjustments. • Risk & Sentiment: Advanced NLP models provide nuanced sentiment analysis of market communications, acting as an early warning system to spot subtle risks and opportunities. • Hyper-Personalization: Portfolios are optimized in real-time based on individual investor goals, risk profiles, and market shifts, leading to more adaptive asset allocation. The Crucial Equity & Fairness Conversation While the efficiency gains are undeniable, the conversation must also address equity in its ethical sense: • Bias Risk: AI models are only as good as the data they're trained on. We must actively guard against historical biases in data sets that could perpetuate or amplify unfair outcomes in investment recommendations or access. • The Black Box: A lack of transparency in complex models (the "black box" problem) poses a governance challenge. We need stronger frameworks for interpretability and accountability to ensure fair and ethical decision-making. The true value of AI in finance lies in combining its superior analytical power with a robust, human-led ethical framework. The goal isn't just better returns, but smarter, fairer, and more accountable wealth management. #AIinFinance #EquityManagement #FinTech #MachineLearning #ResponsibleAI #InvestmentStrategy #entrepreneurs #AI #SaudiArabia #biban25 🌿