Artificial intelligence is transforming the entire pipeline from college to the workforce: from tests and grades to job applications and entry-level work.

This is a hard time to be a young person looking for a job. The unemployment rate for recent college graduates has spiked to recession levels, while the overall jobless rate remains quite low. By some measures, the labor market for recent grads hasn’t been this relatively weak in many decades. What I’ve called the “new grad gap”—that is, the difference in unemployment between recent grads and the overall economy—just set a modern record.

In a recent article, I offered several theories for why unemployment might be narrowly affecting the youngest workers. The most conventional explanation is that the labor market gradually weakened after the Federal Reserve raised interest rates. White-collar companies that expanded like crazy during the pandemic years have slowed down hiring, and this snapback has made it harder for many young people to grab that first rung on the career ladder.

But another explanation is too tantalizing to ignore: What if it’s AI? Tools like ChatGPT aren’t yet super-intelligent. But they are super nimble at reading, synthesizing, looking stuff up, and producing reports—precisely the sort of things that twentysomethings do right out of school. As I wrote:

Reenvisioning the college major

Assuming the requirement for students to complete a major in order to earn a degree, colleges can also allow students to bundle smaller modules – such as variable-credit minors, certificates or course sequences – into a customizable, modular major.

This lets students, guided by advisers, assemble a degree that fits their interests and goals while drawing from multiple disciplines. A few project-based courses can tie everything together and provide context.

Such a model wouldn’t undermine existing majors where demand is strong. For others, where demand for the major is declining, a flexible structure would strengthen enrollment, preserve faculty expertise rather than eliminate it, attract a growing number of nontraditional students who bring to campus previously earned credentials, and address the financial bottom line by rightsizing curriculum in alignment with student demand.

Uber: The Evolution of Uber’s Search Platform

Shopify: Introducing Roast – Structured AI Workflows Made Easy

Sem Sinchenko: Why Apache Spark is often considered as slow

Meta: Collective Wisdom of Models: Advanced Feature Importance Techniques at Meta

We all love our little language wars: the vitriol, the expletives, the dank memes that accompany them.

You may recall, as your friendly neighborhood Anonymous Rust Dev, that I’ve been magnanimous and kind to the Python language. This is because I’m above it all – I can find the beauty in any language if I look hard enough, and I’m willing to give credit where it’s due.

But even I have my breaking point, which I’d like to explore today. How clunky does a language need to be to make itself my foe?

1) Avoid Being The Only Engineer On A Team Early On In Your Career

2) Hype Won’t Fix Your Business Problems

3) Solving Business Problems Is Hard

4) Don’t Skip The Fundamentals

5) Don’t Be Above Putting in the Work

6) Ask Why – But Don’t Always Expect A Great Answer

7) Learn to Communicate with Non-Data People

What’s Next? Predictions for the Future of Search and SEO

• Visibility Shifts from Keywords to Topical Coverage
• Personalization Becomes Standard
• Paid Placements and Monetization in AI Mode
• The Agency Divide: Traditional vs. Modern Approaches
• New Analytics and Search Console Features
• Final Thought: Search Is Now a Conversation

As AI Mode evolves from an experiment to a default experience, search will feel less like a transaction and more like an ongoing conversation. Success will go to brands that can be present, relevant, and trusted at every step—no matter how the questions change.

To grow and thrive in the rapidly evolving AI landscape, organizations must strategically invest in their data engineering capabilities.

In today’s modern digital landscape, businesses are generating heavy data daily which can be processed, analyzed and interpreted for future scalability and growth. This is when AI-driven systems become integral across industries to help create real-time analytics, forecasting and initiating AI-driven automation. Beverly D’Souza, a Data Engineer at Patreon (previously worked at Meta) has played a key role in improving data workflows, processing data at pace and launching machine learning models. Having experience with ETL pipelines, cloud data systems, and AI analytics, she shared, “Building scalable AI-powered data pipelines comes with key challenges and to overcome these obstacles, organizations must implement distributed computing frameworks that can handle large-scale data processing efficiently. Incorporating AI-driven automation helps streamline data processing tasks, making the entire system faster and more efficient.”

The Efficacy of Standardized Interfaces in Reducing Manual Intervention, Improving System Resilience, and Enabling Advanced Governance in Enterprise Data Platforms.

Future Outlook
Agentic AI, MCP, and A2A are poised to reshape enterprise data platforms in the next 3–5 years. We will likely see middleware for agents become mainstream: platform vendors already integrate these protocols. Systems will evolve from static ETL pipelines into adaptive, self-optimizing networks. For instance, future data lakes might auto-tune storage tiers based on usage patterns discovered by agents, or data warehouses could self-partition hot tables. As Microsoft’s announcements highlight, AI is moving toward an “active digital workforce” [33]: think LLMs that don’t just suggest queries, but execute workflows end-to-end. With A2A, agents from different vendors and clouds will interoperate, breaking current silos. Enterprises will embed AI into governance: agentic systems continuously audit for compliance.

We may also see advances in model capabilities driving agentic efficiency — e.g., hybrid systems where a symbolic planner guides LLMs, or LLMs with built-in code execution (like Azure’s CUA) making some MCP calls redundant. Standards (MCP, A2A) will likely expand; Google’s A2A is already collaboration with 50+ partners [40], promising broader interoperability. In short, the data platform of the future could sense, reason, and act: as data patterns shift, agents reconfigure pipelines; when costs spike, agents throttle resources; when new regulations arrive, agents update data handling policies. This vision of an adaptive, self-driving data platform is on the horizon thanks to agentic AI and these new protocols.