## Google's Massive Texas Investment

Texas just landed a **tech investment of historic size** as Google sets its sights on the state. The multibillion-dollar commitment is expected to bring **thousands of jobs** and accelerate Texas' growth in next-age technology.

![State leaders and Google executives announce the company's $40 billion investment in Texas](https://s.hdnux.com/photos/01/55/21/30/28644877/3/ratio3x2_960.webp)
*State leaders and Google executives announce the company's $40 billion investment in Texas during an event at a Google data facility. Courtesy of Governor Greg Abbott's Office*

## What the $40 Billion Investment Includes

The tech giant is putting **$40 billion** into new data centers, workforce training, and energy-related programs to power the future of AI. The initiative, part of Google's national "**Investing in America**" effort, will fund new data center campuses, strengthen energy capacity, and expand training programs aimed at preparing Texans for jobs in an **AI-driven economy**.

## New Data Centers and Job Creation

Google says the plan will bring **three new data center facilities** to Texas, creating thousands of **construction and long-term tech jobs** across North Texas, West Texas, and the Panhandle.

The sites are expected to help power the **next wave of AI products and services**, requiring major upgrades in energy availability, efficiency, and affordability. Company leaders also highlighted new **workforce pipelines** for college students and electrical apprentices to support long-term tech growth in the state.

## Supporting Energy Infrastructure and Workforce Development

According to the release, the investment will support programs to **strengthen energy capacity and affordability** as the demand for advanced computing continues to rise.

Google's "Investing in America" initiative focuses on expanding **technical infrastructure**, increasing **energy capacity** for an AI-driven economy, and building a **workforce** to help the U.S. maintain its leadership in **AI development**.

Google is investing $40 billion in Texas to build data centers, expand energy capacity, and train workers for the state’s growing AI economy.

Google's $40 Billion Texas AI Expansion: Thousands of Tech Jobs Coming

## The Global Tech Job Market Shift

Tech job postings on the hiring platform **Indeed** have been declining globally since their peak in late 2022. By 2025, postings had fallen below pre-pandemic levels everywhere, though the drop has been **less severe in emerging market economies**.

European countries have seen some of the largest declines, while several **Middle Eastern economies have experienced strong growth** over the last five years.

## What Counts as 'Tech Jobs'?

Indeed classifies tech and mathematics roles under four categories: **software development**; **information design and technology**; **IT operations and help desk**; and **mathematics**.

Among the 27 countries analyzed, European nations saw the steepest fall in tech job postings between 1 February 2020 and 31 October 2025, based on a seven-day moving average.

## Europe's Steepest Declines

In absolute terms, the decline exceeded **40% in Switzerland (-46%) and the UK (-41%)**, with France (-39%) close behind.

The United States showed a similar trend, with a decline of 35%. Austria (-34%), Sweden (-32%) and Germany (-30%) were also at comparable levels.

Other European countries experienced more moderate drops: Ireland (-22%), Italy (-16%), Belgium (-14%), the Netherlands (-12%) and Poland (-10%).

Across all 27 countries tracked by Indeed, **global tech postings fell by 18%**.

## The AI Era Impact

"Both Europe and North America experienced a boom in tech job postings coming out of the pandemic that around 2022 began reversing into a bust," Brendon Bernard, senior economist at Indeed, told Euronews Business.

He said the tech sector appears to have undergone an intensified version of broader shifts in employer hiring appetite, with **demand fading as the economic backdrop weakened**.

"As the macroeconomic situation was already turning, we entered the **new AI era, marked by ChatGPT going public**, which has potentially added a further hit to demand for certain — particularly entry-level — tech jobs," he added.

## European Exceptions: Spain and Luxembourg

Spain and Luxembourg were the only European countries among the 13 analyzed where tech job postings did not decline between February 2020 and October 2025. **Postings rose 21% in Spain and 37% in Luxembourg**.

Bernard noted that tech postings in Spain are down from their mid-2022 peak but have not reversed their earlier boom to the same extent as in other advanced economies. "This could be related to broader strength in the Spanish economy, which has helped keep overall job postings relatively stable in recent years," he said.

## Middle Eastern Tech Boom

Several Middle Eastern countries recorded some of the largest increases in tech postings over this period.

**Qatar led with a 222% rise**, followed by Saudi Arabia (130%) and the United Arab Emirates (111%). In all three cases, postings more than doubled.

Bernard said strong growth in tech postings in the UAE and Saudi Arabia has gone hand in hand with substantial increases in overall job postings. "Rather than being unique to tech, demand for tech workers has been part of a broader transformation of these economies in recent years," he added.

## Other Emerging Markets Show Strength

Other emerging markets also saw strong increases: **Malaysia (70%), Mexico (50%), India (44%) and the Philippines (41%)**.

Looking at tech job posting indices by region since February 2020 (indexed at 100), **emerging markets have seen only a slight decline since 2022 but remain 45% above pre-pandemic levels** as of 31 October. This contrasts with sharp declines in the US and most advanced economies.

## Global Distribution Shifts

Globally, tech job postings are 18% below pre-pandemic levels. The decline is 28% in English-speaking advanced economies (excluding the US) and 23% in other advanced economies, while the US has seen a 35% drop.

While the downturn began slightly earlier, the **steepest declines followed ChatGPT's public release in late 2022**.

Indeed data also shows that a **growing share of global tech jobs is now located in emerging market economies**.

Among the countries tracked by Indeed, emerging markets accounted for about 16% of global tech job postings between 2020 and 2022. **Their share has since nearly doubled to 28% as of September**.

India makes up the largest share of emerging market tech postings due to the size of its labour market.

European countries saw steep declines in tech job postings between early 2020 and late 2025, while several Middle Eastern and emerging economies recorded sharp increases. Globally, postings remain below pre-pandemic levels but are holding up better in emerging markets.

Tech Job Exodus: Europe's 40% Decline vs. Middle East's 200% Boom

## The AI Hiring Crisis: Why Experience No Longer Matters

According to a **Nexford University study**, 26% of hiring managers now consider **AI fluency** a baseline job requirement, and 35% specifically look for AI experience or coursework on resumes.

But here's the problem: The work we're hiring for today didn't exist in the mainstream six months ago. The AI experience recruiters screen for is minimal or largely self-taught.

I've navigated the problem of hiring for non-existent technical expertise three times in my career. Each time, the same truth emerges — you can't always rely on credentials and direct experience for successful hiring in a space that's changing daily.

## The Performance Marketing Lesson That Changed Everything

At a marketing agency, we faced a version of this problem as we worked to hire more than 1,000 employees. We needed marketers who could analyze massive **data sets**, identify patterns, run experiments and optimize based on results.

Traditional marketers were trained to be creative storytellers, and while many candidates described themselves as analytical, our math test showed a different story. The test wasn't about passing or failing. It showed who could **think critically under pressure** and who approached problems with curiosity.

That insight changed how we hired by allowing us to realize what skills we needed. We expanded our search to include people from accounting, law and finance, then taught them marketing fundamentals. They became some of our strongest performers.

## Inside Today's AI Hiring Crisis

The situation facing companies is far worse today thanks to AI. In 2015, you could find professionals who'd spent years working with data. In 2025, however, meaningful AI experience is still measured in months.

An **Amazon Web Services study** revealed that close to three in four employers prioritizing AI talent can't find qualified candidates with the specific skills they're seeking. Hiring managers are screening for credentials that don't exist yet, then wondering why they can't fill roles. **The key is to shift your focus from formal experience to the qualities and soft skills that aren't usually found on a resume.**

## 6 Traits That Predict Success in AI-First Roles

After three decades of building companies through technological shifts, I've largely stopped screening for credentials. Instead, I look for six character traits that predict whether someone can thrive when expertise doesn't yet exist.

### 1. Team Over Ego

**AI work depends on collaboration.** Success comes from people who learn quickly from each other and stay open to feedback. I look for signs of that in a candidate's background via things like steady involvement in team sports, volunteer work or group projects that require commitment and accountability.

On a resume, this shows up as sustained participation, not just membership. Four years of varsity athletics tells me more than the phrase "team player" in a skill set section. Leading a college robotics team that competed nationally signals something different from a solo thesis project.

### 2. Coachability and Continuous Learning

The best AI practitioners are excited by the idea that mastery is a moving target. When I review resumes, I look for evidence of **learning across domains** — someone who taught themselves to code while working in finance or pursued certifications in fields adjacent to their main expertise.

Did they take lateral moves to learn new skills? Do they list courses, workshops or self-taught capabilities alongside formal education?

### 3. Courage to Take Smart Risks

**AI requires constant experimentation.** You try an approach. If it fails, you try another. I look for people who are comfortable with uncertainty and motivated by innovation rather than paralyzed by ambiguity.

This shows up as career pivots, entrepreneurial ventures (even failed ones) or roles where they built something new. A resume that shows someone left a stable job to join an early-stage startup tells me they can handle ambiguity.

### 4. Motivated by Collective Success

Individual brilliance matters less than team performance in AI development. I screen for people who've demonstrated they care about shared achievement by **mentoring others**, contributing to team wins, sharing knowledge publicly through teaching, writing or speaking.

This trait is harder to spot on paper, but it reveals itself. Did they mentor junior colleagues? Start a study group? Contribute to documentation or internal training?

### 5. Critical Thinking and Problem-Solving

**AI roles need analytical minds** that love figuring things out. These jobs require people who can break down complex problems, connect data to decisions and use evidence to guide action. Often, these are multilingual thinkers.

At my company, every new hire takes an AI assessment to understand where they are in their AI journey. It's not a test of knowledge. It's a snapshot of experience and comfort with AI tools.

### 6. Adaptable and Open to Change

**AI moves fast.** The best people see change as opportunity, not disruption. They thrive in dynamic environments where curiosity and flexibility drive momentum.

On resumes, this appears as changes in industry, roles or technology that someone navigated successfully. Someone who started in traditional finance, moved to fintech, then to AI consulting has demonstrated adaptability.

## Systematizing the Search for Soft Skills and Traits

Recognizing I couldn't review every resume for these patterns myself, I built an **AI agent** to help identify these traits.

Traditional applicant tracking systems screen for keywords like "five years Python experience," "machine learning certification," "xyz degree." Our AI agent analyzes resumes for the above character traits, going beyond traditional keyword matching to identify behavioral patterns.

### Language Pattern Analysis

Measures the ratio of individual versus collective pronouns throughout the resume. Candidates who naturally use "we" and "our team," even when describing personal achievements, consistently outperform solo-focused candidates.

### Learning Velocity Tracking

Identifies candidates who add two to three new technical skills per role, regardless of their starting point. A marketing coordinator who learned **SQL**, then **Python**, then **data visualization** shows the continuous learning pattern that succeeds in the fast-changing world of AI.

### Cross-Domain Bridge Detection

Flags candidates who work at intersections: finance people who learned UX, teachers who built apps, engineers who studied psychology. These non-linear careers indicate the translation skills essential for AI implementation.

### Failure Portfolio Recognition

Counterintuitively, candidates with one or two documented failures (failed startups, cancelled projects) who articulate learning from setbacks outperform others. The agent identifies specific language around resilience and iteration.

## What You Can Do Tomorrow

Whether or not you build an AI agent to start hiring better for AI roles, there are three steps any hiring manager can implement immediately.

### Rewrite Your Job Descriptions

Remove requirements for "X years of AI experience." Instead, be honest about what the role requires: "You'll learn new tools weekly. You'll experiment, fail and need to bounce back quickly." You're screening for resilience.

### Add a Practical Assessment

Don't test AI knowledge. Give candidates a tool they've never used before and a problem to solve in real-time. You're evaluating how they approach unfamiliar territory. Do they read documentation thoroughly? Ask clarifying questions? Try multiple approaches when the first one fails?

### Interview for Character, Not Credentials

Here are five interview questions to uncover all six traits that predict success in AI-first environments:

1. **The Learning Under Pressure Question:** "Tell me about a time when you had to quickly master something completely new while working with others who were depending on your contribution."

2. **The Pivot and Impact Question:** "Walk me through a significant change in your career or approach that initially felt risky but ultimately led to better outcomes for you and others."

3. **The Knowledge Sharing Scenario:** "Describe a situation where you developed expertise that gave you an advantage, and you had to decide whether and how to share that knowledge with colleagues or teammates."

4. **The System Breakdown Question:** "Tell me about a time when a process, system, or approach you had mastered stopped working effectively. How did you diagnose what was wrong and what did you do about it?"

5. **The Collective Challenge Question:** "Describe a project where success required genuine collaboration and where you had to let go of your own preferences for the team's benefit."

## The Challenge Ahead

**AI is becoming part of every job.** Marketing, finance, operations and HR are already feeling it. Leaders everywhere need to get better at recognizing people who can learn quickly and help them grow along the way.

The organizations that do this well will build stronger teams and more adaptable cultures. They'll move faster and stay ahead as technology keeps changing. Those who focus too narrowly on credentials will find it harder to keep pace.

An expert discussion of how to hire for AI roles.

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