Artificial intelligence

Artificial intelligence

Whenever the conversation turns to AI's role in cybersecurity, one question inevitably surfaces - sometimes bluntly, sometimes between the lines: "If AI can spot patterns faster than I can, will it still need me?" It's a fair question - and one that reflects a deeper anxiety about the future of security careers. AI is everywhere now: embedded in email gateways, SOC workflows, identity systems, and cloud defenses. But here's the truth: AI isn't erasing security roles. It's reshaping them.

"I was surrounded by people much older than me, with more experience in technology," he told Business Insider. "But I felt age is just a number."

MCP is a standardized protocol that lets AI models securely connect to external data sources and tools. Think of it as a translator between AI models and your infrastructure. Without MCP, you'd need to: Manually format data for AI models Handle authentication and security yourself Build custom integrations for each service Manage complex API interactions With MCP, you get: A standardized way for AI to access your data Built-in security and authentication Real-time data access without exposing credentials Contextual responses based on live infrastructure data

Marvell ( NASDAQ:MRVL) is one of the hidden gems that is about to get more spotlight. The custom AI chipmaker's stock is down by more than 20% year-to-date, but its revenue and earnings have soared during this time. This mismatch, combined with insatiable demand for AI chips, suggests Marvell is an underrated AI stock. Marvell is one of the companies that produces custom AI chips for big tech. These chips can't handle every task under the sun like Nvidia's GPUs, but they address specific workloads at a much lower price.

While only a few words in those sentences turned out to be true, they were closer to reality than Musk's other promises about Tesla's self-driving ambitions in 2025. There was also the time he swore "half of the population of the U.S." would have Robotaxi access by the year's end. Or the many times he said that the safety driver, the human in the passenger's seat tasked with monitoring the car as it drives along, would no longer be needed.

A decade ago, becoming an engineer who specialized in artificial intelligence meant having a Ph.D. and working in a research lab. Not anymore. The landscape has fundamentally shifted, and your goal to how to become a machine learning engineer is more achievable - and strategically vital - than ever. The massive, rapid adoption of AI across industries - from personalized retail recommendations to automated factory floors - has created an insatiable demand for people who don't just build models, but who can integrate them into real products.

This rapid change is largely due to the efficacy of AI tools and how they've tripled productivity. Just a few years ago, we were debating whether tools like GitHub Copilot were even reliable enough for basic autocomplete. Fast forward to today, and AI isn't just generating components; it's scaffolding entire full-stack applications, leading many to wonder if it might truly "take our jobs" in the future.

Programming with AI is still in its infancy, and yet it has already redefined what programming will be about in the immediate future. As a programmer, you are going to interact with a frigging robot that writes code on your behalf. What was science fiction yesterday is now a new reality, and it raises many questions. I won't pretend to have any answers here, just assorted thoughts.

Yet across sectors, many organizations are reaching the same conclusion. Adoption is proving harder than invention, especially for general-purpose technologies, as it once was for electricity. As a result, AI adoption remains on the sidelines. Systems exist, but changing how work actually gets done is far more difficult. The constraint is no longer technological capability. It is whether institutions and organizations are prepared to absorb AI.

Retrieval-augmented generation (RAG) has quickly become the enterprise default for grounding generative AI in internal knowledge. It promises less hallucination, more accuracy, and a way to unlock value from decades of documents, policies, tickets, and institutional memory. Yet while nearly every enterprise can build a proof of concept, very few can run RAG reliably in production. This gap has nothing to do with model quality.

This post is Part 1 of a series. In my previous post, I discussed the conundrum we face regarding artificial intelligence (AI) today: On one hand, we're told to use it or get left behind; on the other, we're warned about the "cognitive diminishment" that can result from that very use. I suggested the solution wasn't an uncritical embrace, nor an outright rejection. Yes, we need to learn to use AI. But the dilemma of cognitive decay remains.

With its new Pax Silica Declaration, Washington has picked its most trusted partners in the AI sector: An array of close U.S. allies, including Australia, the U.K., and Israel. Yet despite deepening trade relations between the U.S. and ASEAN nations like Thailand, Malaysia and Vietnam, Singapore remains the agreement's only Southeast Asian signatory. That decision comes even as ASEAN nations like Malaysia are investing in their own AI industries, like semiconductors and data centers.

All the way back in 2019, an article in The Wall Street Journal warned readers that AI has learned to write fake news stories. One of the tools highlighted in the article was GPT-2, a precursor to what we now know as the game-changing tech that is ChatGPT. Fast forward to 2025, and we now have AI integrated directly within the Google Search experience. Now, when you look up something on Google Search, you will see an AI Overview at the top of the page.

A typical AI-powered tool leverages the "knowledge" gained from previously processed image libraries. The more vivid these databases and the more additional information they include - like detailed descriptions of objects in pictures or other tags that may simplify visual object recognition- the better the final result a tool delivers. The origin of processed databases and the legal safety of generated images are other critical issues regarding AI-powered tool usage.

Sam Altman reportedly issued an internal "code red" at OpenAI in December 2025 - an emergency directive to focus resources on improving ChatGPT whilst delaying initiatives like advertising or Pulse triggered by Google's launch of Gemini 3. The flagship product that triggered the current AI frenzy, deployed to hundreds of millions of users, needed an internal alarm bell. Not because it had fundamentally failed.

The deal is sure to turn heads too. Manus and its parent company Butterfly Effect are now based in Singapore but were founded in China - a country with a fraught relationship to the U.S tech industry - and maintain operations there. Facebook's parent company will reportedly pay more than $2 billion to acquire the startup, which it hopes will bolster its own lagging AI capabilities.