The elimination of federal renewable energy tax credits in Trump’s One Big Beautiful Bill Act has major implications for the global AI race.

Ultimately, the shift means slowing down US progress on new energy production, which is key to winning the technology Cold War with China. There is no possible way tech companies can power the massive rollout of AI factories without solar, and now it will be that much more expensive.

But the attempt to throw a lifeline to the fossil fuel industry could be too little, too late, as detailed in this New Yorker article by Bill McKibben. The rate of solar adoption is now about a gigawatt every 15 hours. A gigawatt is the output of a typical nuclear power plant.

Solar isn’t just cheaper than fossil fuels. It’s also faster to deploy, which is crucial in the AI race. The expansion of AI data centers is creating new economic incentives for innovation in renewables, from geothermal to fusion to new battery chemistries, which can store all that new solar power. It’s a topic I expect we’ll be covering more and more here in the coming months.

The Solutions Review and Insight Jam team has identified several soft skills that professionals throughout the enterprise technology market must prioritize in the AI era, according to proprietary research.

As artificial intelligence (AI) reshapes industries and transforms the nature of work itself, professionals face an unprecedented challenge: how to remain relevant and thrive in an increasingly automated world. While technical skills and AI literacy are undoubtedly important, the most successful professionals of the AI era will be those who master distinctly human capabilities that complement rather than compete with artificial intelligence.

The traditional career playbook gives way to a new paradigm where human insights fueled by adaptability and emotional intelligence become the primary drivers of professional success. Organizations are discovering that their most valuable employees aren’t necessarily those who can outperform AI at computational tasks, but those who can work alongside AI systems while bringing irreplaceable human judgment, creativity, and connection to their roles.

However, a proprietary study of over 200 senior tech professionals across markets and roles (you can check out the Solutions Review team’s research here) reveals a disconnect. While 94 percent of tech leaders agree that soft skills are more critical than ever, most admit their organizations lack the structure, time, or training mechanisms to develop them.

Findings like this should be a wake-up call for professionals, now more than ever. That’s why our team conducted the research in the first place and compiled some of the soft skills respondents identified as particularly valuable for the current market trends we’re seeing. Professionals prioritizing the five skills below will differentiate themselves from AI systems, enabling them to leverage AI tools more effectively, lead diverse teams through constant change, and create value that transcends what technology alone can provide.

5 AI Era Soft Skills Professionals Must Prioritize for Career Growth

Curiosity

In an era when information becomes obsolete faster than ever, curiosity has evolved from a “nice-to-have” trait to a career-critical capability. The half-life of skills continues to shrink as AI automates routine tasks and creates entirely new categories of work, which means professionals who maintain an active, systematic approach to learning and questioning will consistently outperform those who rely on static knowledge.

Our respondents agree, as 93.3 percent rate curiosity as “very” or “extremely important” to their careers. The problem is that nearly half of them also say they lack the time to commit to that learning. Curiosity in the AI era must go beyond passive learning, as professionals must actively seek an understanding of how AI systems work, where they excel, and crucially, where they fall short. The most successful professionals will be those who ask probing questions about AI outputs, challenge assumptions, explore the boundaries of what these systems can (and cannot) do, and identify opportunities for human-AI collaboration that others might miss.

Relationship-Building

The ability to build and maintain meaningful professional relationships has become more valuable, not less, especially with work becoming increasingly digital and AI-mediated. While AI can analyze communication patterns and even generate personalized messages, it cannot replicate the trust, empathy, and genuine connection that form the foundation of effective collaboration.

The complexity of modern organizations requires professionals who can navigate intricate networks of stakeholders, each with different priorities, communication styles, and levels of comfort with new technologies. While many tech professionals prefer working alone, despite recognizing the need for strong networks, 84.5 percent still acknowledge the importance of relationship-building. Once again, though, industry leaders say that prioritizing professional relationships is a struggle.

As AI democratizes access to information and tools, competitive advantages come from having access to diverse perspectives and early insights into emerging trends. The ability to cultivate relationships with thought leaders, potential collaborators, and industry pioneers becomes a significant differentiator.

Humility

Perhaps counterintuitively, humility has become one of the most powerful professional attributes in the AI era. As the pace of change accelerates and the complexity of challenges increases, the professionals who acknowledge the limits of their knowledge and actively seek input from others, including AI systems, often outperform those who rely solely on their existing expertise.

81 percent of tech leaders say humility (seeking and using feedback) is essential to their career success.

Intellectual humility manifests in several ways that can directly impact career growth. First, it enables professionals to embrace AI as a collaborator rather than a threat. Instead of viewing AI capabilities as diminishing their value, humble professionals recognize that these tools can amplify their effectiveness when used thoughtfully. They’re comfortable saying “I don’t know” and turning to AI systems for analysis, while also recognizing when human judgment is necessary to interpret and apply AI-generated insights.

This humility also extends to learning from failures and mistakes. In an environment where experimentation with new technologies and approaches is essential, the professionals who can quickly acknowledge when something isn’t working and pivot their approach are more likely to succeed than those who persist with failing strategies to protect their ego or reputation.

Resilience

Thanks to AI, technologies that once seemed permanent have become obsolete within years, entire job categories are disappearing as new ones emerge, and the skills required for success continue to evolve. In this context, resilience has become essential for long-term career success. Professionals and executives are well aware, too, with over 90 percent claiming resilience is a crucial skill, while also acknowledging that it can be challenging to recover from setbacks.

That’s why continuous learning remains essential. The more attention given to projects that promote resilience, the easier it will be for teams to acquire new skills, adapt to changing workflows, and maintain productivity during organizational transformation. Whether it’s an AI implementation that doesn’t deliver expected results or a skill that becomes automated, resilient professionals can bounce back quickly and extract valuable lessons from positive and negative experiences.

Perspective-Taking

According to survey findings, 84 percent of tech professionals value perspective-taking, yet 31 percent struggle to reconcile conflicting viewpoints. Further, an alarming 29 percent believe their perspective is the “best” one, even in teams with diverse views, which can become a major blocker to innovation.

Understanding and considering multiple viewpoints has become critical as AI systems reshape how work gets done and decisions are made. This ties into the importance of “humility” as a soft skill, as effective perspective-taking requires professionals to serve as interpreters between different stakeholders who may have vastly different comfort levels with AI and other assumptions about its capabilities. This skill proves particularly valuable when implementing AI solutions that affect diverse groups of users, customers, or colleagues.

The professionals who excel at perspective-taking often become the most trusted advisors and change leaders in their organizations, helping others see opportunities and navigate challenges that might otherwise seem overwhelming.

Conclusion

The AI era represents both an unprecedented opportunity and a significant challenge for professional development. While technical skills and AI literacy are important, the professionals who will thrive are those who develop distinctly human capabilities that complement rather than compete with artificial intelligence. The report shows that 94 percent of professionals agree that curiosity, resilience, and other critical soft skills are required for the future. And yet, most also state they lack the time, coaching, and feedback to improve these skills.

That lack of time is a problem, considering those soft skills are integral to successful business practices and professional development. For example:

• Curiosity drives continuous learning and innovation.
• Relationship-building creates the trust and networks necessary for effective collaboration.
• Humility enables productive partnerships with both AI systems and human colleagues.
• Resilience provides the foundation for adapting to constant change.
• Perspective-taking facilitates understanding across diverse viewpoints and technologies.

These soft skills work synergistically, reinforcing each other and creating a foundation for sustained career growth regardless of how AI continues to evolve. Promoting these soft skills requires support from the top down, and vice versa. Individuals should voice their desire for upskilling or ongoing learning programs, and executives must respond by providing them with the time and resources they need to focus on those skills. Doing so will create value that transcends what technology alone can provide and build careers that remain relevant and rewarding in an increasingly automated world.

The best technologists of the future will not simply know how to build, prompt, or deploy AI. They’ll learn how to work with others, weather change, and see the bigger picture. Human-centered skills are the foundation of that future, and the time to start building them—systematically, strategically, and sincerely—is now.

To that end, the Solutions Review and Insight Jam teams are conducting a follow-up study to deepen our understanding of the human-AI skills gap, and we need your input.

Take the survey now to contribute your perspective and help define the next wave of workforce readiness.

Note: These insights were informed through web research and generative AI tools. Solutions Review editors use a multi-prompt approach and model overlay to optimize content for relevance and utility.

Bill Gates has spent almost fifty years pushing software forward, so when he says a single profession will stay human long after artificial intelligence rewires the rest of the economy, people listen. Speaking in early July, the Microsoft co-founder told interviewers that programming will “remain a human job for at least a century”. He is not brushing off AI’s power as the WEF (World Economic Forum) 2025 report projects automation could erase 92 million roles by 2030, while creating about 170 million new ones, but he draws a clear line around code. Writing software, he argues, is less about typing syntax and more about spotting unseen patterns, judging trade-offs and making a leap no algorithm can anticipate. AI can already draft snippets, debug routine errors and suggest architectural templates, yet the spark that turns a half-baked idea into working logic still comes from a person at a keyboard.

Bill Gates’ latest AI prediction

Gates shared the view in separate conversations with The Economic Times and The Tonight Show, then echoed it during a podcast with Zerodha’s Nikhil Kamath. Each time he circled back to the same point: tools like Copilot and ChatGPT are power chisels, not replacement carpenters. They shorten grunt work but leave the blueprint to us.

Why programming, of all careers, stays human

Code often starts with an ill-formed idea—say, turning sensor data into a flood-prediction dashboard. A machine can crunch numbers, yet deciding which signals matter and how the user will act on the output involves judgment, negotiation and a few flashes of intuition. Gates calls that the “creative leap” AI cannot copy.Large models spit out plausible text, but a misplaced bracket or misunderstood requirement can crash a critical system. Spotting edge cases takes domain insight and lived experience, qualities still thin in training data. Gates says AI can help with “boring stuff like debugging,” but final responsibility sits with a human reviewer.APIs are deprecated, laws shift, and users click in ways nobody predicted. The best programmers keep adapting code to fit messy reality. AI excels at frozen snapshots; humans excel at moving targets.

Other jobs that Bill Gates thinks are safer

Gates singles out biology and energy as disciplines where scientific curiosity, ethical trade-offs and crisis management require a person in charge. Sports, he jokes, will also stay human because nobody wants to watch robots play baseball.

How does this prediction fit broader job-market numbers

The World Economic Forum projects a net gain of +78 million jobs by 2030, despite losses in clerical and routine design roles. Programming sits on the creation side of that ledger, not because AI is weak, but because software problems keep mutating faster than the models that try to automate them.

Related FAQs

1. What job did Bill Gates say AI will not replace?

2. Why does he think coding is safe?

3. Can AI still help programmers?

4. Are any other careers safe according to Gates?

5. Does Gates dismiss AI risks?