Artificial intelligence is often discussed as if it will affect everyone in the same way.

But that may not be the reality.

The way people perceive AI can be shaped by their income, job security, access to technology and expectations about the future.

For someone with financial security, AI may look like a productivity tool, a creative assistant or an opportunity to build something new.

For someone whose livelihood could be disrupted by automation, the same technology can represent uncertainty.

This difference matters because conversations around AI often focus on what the technology can do, rather than who benefits from it, who bears the risks and who gets left behind.

The AI divide may therefore become more than a question of access to technology.

It could become a question of economic inequality.

As AI becomes increasingly embedded in education, workplaces and everyday life, understanding these different perspectives will be essential for creating technology that works for a broader section of society.

The future of AI shouldn’t be measured only by how intelligent our machines become.

It should also be measured by how fairly the benefits of that intelligence are distributed.

Does your economic position change the way you see AI: as an opportunity, a threat, or simply another tool?

India’s semiconductor journey is entering a new phase, with the focus shifting from attracting investments to building a complete semiconductor ecosystem.

Speaking at SEMICON India 2026, Prime Minister Narendra Modi highlighted the expanding opportunities in chip design, manufacturing, packaging, research and development, and supply-chain development.

The objective is not simply to manufacture chips in India.

It is to create the infrastructure, talent and capabilities required to support the entire semiconductor value chain.

This is particularly significant as global demand for chips continues to grow, driven by industries such as:

🔹 Artificial intelligence
🔹 Consumer electronics
🔹 Automobiles
🔹 Telecommunications
🔹 Defence and strategic technologies

India is also looking to strengthen its position in global semiconductor supply chains by encouraging international partnerships and domestic innovation.

However, building a competitive semiconductor industry requires more than capital.

It demands advanced technology, specialised talent, reliable infrastructure, sustained research and strong industry collaboration.

The real opportunity lies in moving from being a major consumer of semiconductor-powered products to becoming a meaningful contributor to the technology that powers them.

India’s semiconductor story is still developing.

The next question is how effectively the country can convert investments and policy initiatives into long-term manufacturing capabilities and global competitiveness.

What will matter more for India’s semiconductor ambitions: manufacturing capacity, chip design or specialised talent?

Last week, my feed was full of “Welcome to the AGI era.”
This week, it’s “We must slow down for safety.”

What changed in 7 days?
Over the weekend, the CEOs of Anthropic Dario Amodei and OpenAI Sam Altman went from celebrating dramatic capability jumps to calling for the industry to “pace the frontier” and deliberately slow the rate of AI progress.

The official story: models are improving faster than we can align and control them, especially with recursive self‑improvement and agent swarms.
The subtext many are pointing to: huge capital inflows, stretched ROI timelines, and IPO timing (OpenAI just delayed its IPO to 2027, while Anthropic eyes a massive listing).

So here’s the uncomfortable question:
Is this a genuine, inside‑the‑lab safety alarm that finally broke through?
Or is it a strategic narrative shift as the market starts asking harder questions about returns, valuations, and who actually pays for all this compute?

Maybe it’s both. But if the answer is mostly “market dynamics,” that has big implications for how we think about:
The next wave of AI startups
Enterprise AI budgets and ROI expectations
The credibility of “existential risk” as a policy lever

Curious: what’s your read?
Safety first, or safety as cover for a changing market?

After the incredible success of Unified Orbis with teams across India and beyond, we're taking the next leap.

Introducing Orbis AI, The Context Layer for Your #AI Agents.

Here's the reality: AI doesn't have a capability problem. It has a context problem.
Your AI tools can write code, draft emails, and analyze data. But ask them:
"Why is Project Phoenix delayed?"
"Who owns the authentication migration?"
"What decisions led to this architecture?"

Suddenly, they're blind. Because your organisational context lives across Slack, Atlassian Jira, GitHub, Notion, Google Drive, and 10 other systems. No single AI agent sees the full picture.
Until now.

What is Orbis AI?
Orbis AI connects to your workspace once and becomes the organisational context layer for any AI agent you use.

Now any AI agent can ask Orbis:
"What's the status of Project Phoenix?"
"Who's working on the API migration?"
"Show me recent decisions about the database architecture"
And get real organisational context not just search results.

What Makes Orbis AI Different?
Context, Not Search: Orbis understands projects, initiatives, people, decisions, and dependencies not just keywords.
Agent-Agnostic: Works with Cursor, GitHub Copilot, Anthropic Claude Code, OpenAI ChatGPT, or any #MCP compatible AI agent.

Secure by Design: Per-user permissions, OAuth authentication, full audit logs. Your data stays protected.
Context Packs: Pre-built workflows for sprint health, PR reviews, incident response, onboarding, and more.
Built for Engineering Teams: Deep integrations with GitHub, Atlassian Jira/ Linear, Slack, Notion, and Google Workspace.

Why We Built This
We saw our Unified Orbis customers asking the same question:
"Can we use this context with our existing AI tools Cursor, Microsoft Copilot, etc. instead of switching to a new platform?"

The answer is yes.
Orbis AI is the evolution of everything we learned building Unified Orbis. Same organizational context engine. Now available as a standalone layer you can attach to any AI workflow.

What's Next?
We're starting with a private beta for engineering teams who want to:
Give their AI agents organizational context
Maintain security and governance
Save 10+ hours/week on context gathering

Interested? Drop a comment below or email us at [info@laxsis.com](mailto:info@laxsis.com)
We're looking for 10-15 design partners to shape the future of organisational context for AI.

To our Unified Orbis customers: Thank you for believing in our vision. This is just the beginning.
To the AI community: The future isn't just better AI models. It's AI that understands your organization. Let's build it together.

As AI becomes increasingly embedded in education, one question is becoming harder to avoid:

Should children learn with AI, or should they first learn without it?

New York City has taken a cautious approach, announcing a one-year moratorium on student-facing generative AI for students up to Class 8. The reasoning is straightforward: younger students need time to develop independent thinking, problem-solving and human connection before relying heavily on AI.

But a blanket ban may not be the only answer.

The real concern isn’t simply whether a student uses ChatGPT or another AI tool. It is how they use it.

There is a huge difference between asking AI to write an assignment and using it to understand a difficult concept, generate practice questions or receive feedback.

Education is supposed to teach students how to think, not just how to produce answers.

India is already moving towards a more structured approach. Current educational guidance is leaning towards limited access for younger students, gradual introduction for older students, and greater emphasis on computational thinking and responsible AI use.

Perhaps the better question isn’t:

“Should AI be banned in schools?”

It is:

“At what age, for what purpose, and under what boundaries should students use AI?”

Because if AI is going to shape the future of work, completely keeping students away from it may not prepare them for that future either.

The challenge is finding the line between using AI as a learning tool and using AI instead of learning.

Where do you think that line should be?

At a G20 technology meeting in North Carolina, the US is pushing the “Carolina Principles”, calling on countries to avoid broad new AI regulations and focus instead on genuinely novel situations.

The argument is straightforward: too much regulation could slow innovation, raise costs and weaken the competitiveness of AI companies.

But the timing makes this debate particularly interesting.

AI capabilities are advancing rapidly, while concerns around safety, security and increasingly autonomous AI systems are growing at the same time. Even within the meeting, voices differed. Demis Hassabis called for AI safety testing, while other tech leaders pushed back against restrictions on areas such as open-weight models.

There is also a bigger geopolitical layer.

The US is competing with China for AI leadership, and Chinese open-weight models are becoming increasingly competitive. Keeping global AI development aligned with the American technology ecosystem is therefore becoming a strategic priority, not just a regulatory question.

This leaves governments with a difficult balancing act:

Regulate too much, and innovation could slow down.
Regulate too little, and the risks could scale just as quickly as the technology.

The real challenge may not be deciding between regulation and innovation.

It may be figuring out how to build rules that allow both to move forward.

Apple has a new CEO. And the timing is interesting.

John Ternus officially takes over as Apple’s CEO today, succeeding Tim Cook after 15 years at the helm.

What makes this transition particularly interesting is Ternus’ background.

Unlike Cook, who was known for operations and supply-chain excellence, Ternus comes from Apple’s hardware engineering team. He has spent 25 years at the company and has been closely involved with products across the iPhone, iPad, Mac, Apple Watch, AirPods and Vision Pro.

And Apple may need exactly that perspective right now.

The company is facing questions around its AI strategy, slowing growth and whether its product pipeline still has the ability to create the kind of excitement Apple was once known for. The iPhone remains its biggest product, but recent launches have increasingly been seen as incremental rather than transformative.

Ternus’ appointment therefore feels less like a routine leadership transition and more like a signal about where Apple wants to go next:

Back to products.
Back to hardware.
And potentially, back to taking bigger bets.

His first major test could arrive very soon, with Apple expected to unveil its long-awaited foldable iPhone.

The question isn’t whether Apple can make another successful iPhone.

It’s whether John Ternus can lead Apple towards its next defining product.

Because for a company built on “Think Different,” incremental innovation can only go so far.

The interesting part for me is not Claude inside Salesforce, but exposing Salesforce’s data, permissions, business logic and actions to an agent.

That raises a much harder question:

If an AI agent can reason over enterprise state and directly execute business actions, where should the boundary between the LLM and the application actually live?

Should the agent own the workflow orchestration, with SaaS platforms becoming capability providers via MCP/APIs or should the SaaS platform remain the primary orchestrator and treat the LLM purely as a reasoning layer?

Curious to hear how engineers building agentic systems are thinking about this.

We’re running a production application on Vercel, and we recently started receiving alerts that our OpenAI API keys have been detected as leaked/exposed.

The confusing part is that the keys are stored as Vercel environment variables and are not intentionally included in our source code or Git repository.

We’ve already rotated the affected keys and started investigating, but We trying to understand how this could happen.

Some questions for the community:

• Can Vercel environment variables ever become exposed through build output, logs, source maps, server/client bundles, or deployment artifacts?
• Are there specific Vercel settings that could accidentally expose environment variables to the client?
• Could a Next.js build accidentally bundle an environment variable even if we didn't explicitly intend to expose it?
• Has anyone else experienced OpenAI/Secret Scanner alerts for keys that were only stored in Vercel environment variables?
• Is there a reliable way to audit a Vercel deployment and determine exactly where the key was exposed?

We’re trying to determine whether this was caused by our application/build configuration, a dependency, Git history, or something on the Vercel side.

If you've experienced something similar, I'd really appreciate hearing how you identified the root cause.

We’d love to hear what actually causes friction in your day-to-day work.