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Entrepreneur
28-05-2025
- Business
- Entrepreneur
How to Build an AI-Driven Company Culture
A practical guide for business leaders on how to build a company culture that embraces AI through curiosity, experimentation and hands-on learning. Opinions expressed by Entrepreneur contributors are their own. In the early 1900s, as the automotive revolution reshaped industries, blacksmiths and carriage-makers struggled to adapt. More than a century later, we face a similar inflection point with AI. Just as horse-drawn carriages gave way to automobiles, entire industries are being redefined by algorithms today. The question isn't whether your company will adopt AI, but how. And the answer hinges on one critical factor: culture. Related: How to Create a Workplace Culture That Supports Digital Transformation (and Why It's Important) What does an "AI culture" look like? Building an AI-driven culture isn't always about buying tools or hiring machine learning scientists. It's about fostering a mindset where experimentation, learning and human-AI collaboration are core to your company's DNA. Here's how to start: Model curiosity to dispel fear: Leadership must champion AI, but grassroots innovation is what embeds it into real workflows. At CodeSignal, our engineering team doesn't just use AI — they build with it. From leveraging GitHub Copilot for complex refactoring to fine-tuning custom LLM agents for internal tools, AI is part of their daily toolkit. And it's not just engineering. Our marketers, for instance, prototype campaign ideas in Claude and validate messaging variations with Gemini. The key? Leaders must model curiosity. Share your own AI experiments — and failures — with your team. CodeSignal has a Slack channel dedicated to experimentation with LLMs, where team members share how they've been using AI and what they're learning ("productivity hacks" are a team favorite). I have been studying AI technology and building AI-native products for over a decade, but this doesn't stop me from continuing to learn. I regularly share my learnings, from using the latest LLM models for everything from code writing to email writing to image generation, and debate with my colleagues on how different models perform on complex math challenges. The point of me doing this is to set the example that incorporating AI into your daily workflow doesn't have to be intimidating, and in fact it can be quite enjoyable. It also reinforces that we're all learning this new technology and figuring out how best to use it to do our work together. Provide access to the right AI tools: Today, tools like ChatGPT and Midjourney are free, yet many companies still gatekeep access. That's a big mistake. We give every team member a ChatGPT Teams subscription, with the expectation that they'll play around with it and even create their own GPTs to augment their workflow. In the past year, our employees have created over 50 custom GPTs that help them draft sales emails, gather market insights, extract data, answer HR questions and more. Make AI literacy a core expectation — then build on it: Giving people access to AI tools is necessary, but it's just the first step. To create a meaningful impact, leaders must pair access to tools with training. CodeSignal does this by asking every team member to complete AI literacy training, where they build skills in using and interacting with LLMs with hands-on practice. Our team recently finished a "spring training" in generative AI literacy, where everyone at the company (even me!) completed a series of experiential learning courses online and shared our learnings, questions and ah-ha moments in a Slack channel. We boosted motivation for completing the training by setting up a goal of 95% participation — rewarded by cool new swag when we met the goal. Next, we're building on this foundation of AI literacy by running an AI hackathon at our next in-person meetup. Here, team members will break into teams based on how they use AI and their depth of knowledge. Some teams will explore using LLMs to draft creative campaigns and set project timelines, for example, while others will be building custom GPTs to automate actual parts of their job. The machine learning experts on our team, meanwhile, will be working on building innovative new AI applications from the ground up. The goal here is to set the expectation that everyone uses AI, yes — but more than that, to give team members ownership of what they do with it and the freedom to choose which parts of their job can best be complemented by AI. Related: AI is the Coworker of the Future — 3 Ways Employers Can Get Ready The stakes have never been higher For some organizations and teams, adopting AI will be uncomfortable at first. AI tools raise a range of new technical, regulatory and ethical questions. Many employees fear that AI will displace them from their jobs. That discomfort is real — and it deserves our attention. As leaders, our responsibility is to guide our teams through uncertainty with integrity and transparency by showing how embracing AI can help them become even more impactful in their jobs. I do this by modeling AI use in my everyday work and openly sharing my learnings with my team. This gives team members permission to experiment on their own and helps move them from a mindset of fear to curiosity about how AI can be a partner to them in their jobs. To return to the analogy of the automotive revolution: We're teaching our carriage-makers how to build self-driving cars. If you're a business leader, ask yourself: Am I modeling what it looks like to learn and take risks? Am I giving my team the tools and training they need to build AI literacy? Am I fostering a culture of exploration and experimentation on my team? The AI revolution is already here, and the future isn't going to wait for companies to catch up. Neither should we.
Forbes
28-05-2025
- Business
- Forbes
CodeSignal's AI-Assisted Tests Redefine How Tech Skills Are Evaluated
The ability to effectively use artificial intelligence tools increasingly separates typical employees from highly productive '10x" employees. In technical and engineering roles, where the effective use of AI tools is becoming an essential skill, traditional methods of assessing talent are rapidly becoming outdated. It is no longer sufficient to know what technical skills an employee possesses; one must also understand their ability to collaborate effectively with multiple intelligent tools simultaneously. CodeSignal's recent announcement of its AI-Assisted Coding Assessments marks a significant milestone in acknowledging this important shift in skill evaluation and development. Historically, evaluating technical proficiency has involved assessing direct, tangible skills. Candidates were evaluated based on their individual capacity to write and debug code, solve mathematical problems, or showcase theoretical knowledge. By way of analogy, technical assessments traditionally focused on individuals as virtuoso musicians, with the evaluation determining how well they could play various instruments. The rise of powerful AI-driven tools has fundamentally transformed this landscape. Tigran Sloyan, CEO of CodeSignal, highlights this evolution. 'Working with AI in many ways is similar to management. It is like telling somebody other than yourself what to do. Clear communication, the ability to break things down into clear parts, and put them back together are essential. It is not merely about understanding tools; it's about effectively managing multiple intelligent systems simultaneously.' Rather than being a virtuoso musician, the role has become more akin to that of a conductor. The conductor doesn't play every instrument but directs multiple musicians to create harmonious outcomes. Similarly, the modern technical professional must seamlessly orchestrate various AI tools, each capable of intelligent outputs. Recognizing this shift, CodeSignal has introduced a suite of coding assessments designed to evaluate candidates' abilities to leverage AI-powered coding assistants effectively. Rather than ignoring the reality that individuals will inevitably use these intelligent tools to complete their work, CodeSignal has embraced this fact. Their new assessments directly test a candidate's proficiency in collaborating with AI to quickly understand complex problems, devise strategic solutions, and execute them efficiently. Traditional pre-employment assessments often simplify tasks to fit within a short evaluation window, typically lasting one to two hours. 'You can't just take existing pre-hire assessments and add AI to them, because the old questions were oversimplifications of reality. Simplifications are too simple for AI, so the AI would solve the problems instantly, demonstrating no skill from the candidate,' says Tigran. As a result, CodeSignal's new approach introduces real-world scenarios with greater complexity, ensuring the AI assistant enhances rather than replaces candidate skills. Candidates must be able to ask strategic questions, rapidly assimilate AI-driven insights, and effectively integrate outputs to address sophisticated challenges. For example, a candidate might encounter a complex codebase in a typical software engineering scenario. Rather than manually sifting through thousands of lines of code to grasp its functionality, candidates who collaborate with AI can quickly summarize, interpret, and identify core focus areas using AI-powered tools. Those who haven't mastered this collaboration will soon fall behind, overwhelmed by the complexity. Therefore, success in these assessments is less about the ability to code itself and more about effectively managing intelligent resources to get the job done. This shift in assessment philosophy underscores a broader transformation across the tech industry and beyond. AI tools like ChatGPT, Xai's Grok, Anthropic's Claude, and Google's Gemini have become routine companions in many workplaces, leading to a radical shift in job expectations. Companies no longer seek individuals proficient in existing technical frameworks or languages; they require professionals capable of continual learning, adapting, and effectively leveraging evolving AI tools. Historically, university engineering and technology programs' curricula have evolved slowly, and they often struggle to keep up with rapidly changing industry demands. With AI reshaping skill requirements, this issue has become even more pressing. Unless universities can adapt quickly and provide the higher-order skills employers need, they risk graduating students who are ill-prepared for the modern workforce. Tigran notes, 'There's a massive disconnect between what companies and industries want, and what university curricula teach. Universities want to know what skills they should be teaching students right now. The universities whose students perform well on our assessments do two things: first, they understand what companies are hiring for, and second, they provide students with plenty of opportunities to practice those skills.' Educational institutions must incorporate explicit instruction in AI collaboration skills into their curricula. Students should be trained in traditional coding and effectively manage and orchestrate multiple intelligent tools. Universities aiming to produce students who excel in these new types of technical assessments must develop exercises that reflect authentic workplace complexities, requiring students to strategically engage with and leverage AI technologies to solve sophisticated real-world problems. Beyond the explicit teaching of AI collaboration skills, educational institutions must navigate an ever-evolving distinction between core and emerging competencies. At the core will be the skills and knowledge that every professional should possess, regardless of the changing tools. In contrast, emerging competencies are rapidly evolving skills closely linked to specific technologies or methods that may quickly become obsolete but are crucial for immediate productivity. These competencies are most likely to be assessed during a technical interview, and providing this level of education will enable university programs to have the most significant impact on their graduates. This distinction also demands new strategies. Institutions must focus not only on current technological skills but also on cultivating students' abilities for rapid learning and adaptability. Critical capability becomes less about thoroughly knowing any tool and more about quickly mastering and integrating whatever tools become relevant next. The capacity to rapidly assess a situation and deploy the appropriate complex set of tools to address a problem is precisely what students will need to demonstrate to succeed in an interview. In light of these implications, CodeSignal's AI-Assisted Coding Assessments represent more than just a new testing method—they reflect a significant philosophical shift. By explicitly assessing the skill of orchestrating AI systems, CodeSignal sends a clear message to educators and employers alike: success in AI relies on adaptability, strategic collaboration, and rapid response learning. The future workplace is here now. It is defined by intelligent collaboration rather than just individual technical execution. Those who master orchestrating multiple intelligent tools will find themselves invaluable. As AI integrates rapidly into nearly every industry, developing these management skills will become essential to being a 10x engineer. These skills will not only enhance individual careers but also transform them. CodeSignal's AI-Assisted Coding Assessments illuminate this path, urging employers and educational institutions to prepare individuals for yesterday's challenges but for the evolving demands of tomorrow.
Epoch Times
22-05-2025
- Business
- Epoch Times
San José State University Ranked in Top 10 for Computer Science
San José State University (SJSU) has been ranked 9th among the top 50 universities for computer science in the United States in 2025, 'I'm super happy and proud of our students for having achieved this,' Chris Pollett, chair of the university's computer science department, told The Epoch Times. The ranking assesses students' coding skills using the industry standard General Coding Assessment. CodeSignal, the platform behind the rankings, provides major tech companies such as Google and Meta with assessments to ensure that incoming candidates are qualified. Despite being one of the two top 10 schools in the list that were not included in the U.S. News & World Report's top 30 for engineering programs, SJSU is home to top coding talent. CodeSignal's tip for recruiters? Target overlooked schools like SJSU, which, according to the report, are significantly less likely to be targeted than Stanford, MIT, and UC Berkeley. Related Stories 5/20/2025 5/16/2025 The university 'Talent comes from everywhere—not just the schools traditionally recognized as top engineering schools,' the report states. Pollett said that in Silicon Valley, it's possible to 'perform above your weight class.' The university's location in the heart of Silicon Valley gives it a unique edge in students' job and internship opportunities. The area is home to tech giants like Apple, Google, Meta, Nvidia, and several others that are headquartered there. 'Silicon Valley is just chock-full of our graduates,' said longtime computer science professor Jon Pearce. Being at SJSU affords students plenty of internships that they wouldn't be able to find in other areas, Pearce told The Epoch Times. Pollett agreed that SJSU is a major supplier of computer science students to Silicon Valley companies. He said the university tries to give students opportunities to work with local companies. One such opportunity was taken by software engineering student Samarth Sharma, who currently interns at a venture capital firm. Sharma told The Epoch Times that his manager said he really likes SJSU because he's had great experiences with previous interns from the university. Software engineering graduate student Nivedita Nair and several of her friends have also found internships during their time at SJSU. She told The Epoch Times that the school hosts career fairs where students have opportunities to connect with recruiters from local tech companies, which can lead to internships. For Nair, the university's proximity to major tech headquarters was part of the reason she chose to attend. Besides recruiters, faculty members themselves often have experience in tech. They stay on top of current trends and advancements such as AI, so they're able to teach students what really matters in the industry, according to Sharma. He said that all of his current professors are industry professionals. As all these advantages have become available, the program has become more competitive, in recent years. Admissions requirements such as GPA are higher than ever, which Pollett said is improving the quality of students they receive. Pearce's impression is that the above average students are now 'way above average' and could excel at any university. Both Sharma and Nair appreciate how helpful their student peers have been, saying that they've learned and benefited a lot from them, especially those with industry experience. SJSU was 'That's certainly been the wisdom for a while now,' he said.
Miami Herald
15-05-2025
- Business
- Miami Herald
San Jose State University beats Stanford, Cal in computer coding
San Jose State University has shot past Stanford and UC Berkeley to a top-10 spot in a ranking of U.S. universities based on a standardized computer coding test. The school leapt to the No. 9 spot this year in rankings by CodeSignal, a San Francisco company whose General Coding Assessment is widely used by major technology companies to evaluate potential hires. That position put San Jose State in front of Berkeley at No. 19 and Stanford at No. 25, a giant leap from last year, when the school was ranked 32nd, and from 2023, when it ranked 48th. "This is great news," said San Jose State engineering school dean Sheryl Ehrman, who attributed the result to eager students, talented tenure-track faculty, and part-time instructors with tech industry experience who are "really trying to impart those real-world skills." Whether the university could continue its trajectory to the top of the rankings would require a dramatic upset. This year and last year, Carnegie Mellon took No. 1 and Massachusetts Institute of Technology came in No. 2, while in 2023, MIT came out on top, followed by New York's Stony Brook University, with Carnegie Mellon at No. 3. The downtown San Jose school is an "under-told story" behind Silicon Valley's success, said South Bay Democratic Congressman Ro Khanna. "It's always been such a key component of churning out engineers, churning out people in technology," Khanna said this week. "A lot of headlines go to Stanford and Berkeley. San Jose State and Santa Clara (University) are really important contributors, and San Jose, of course, being a public school, is more accessible for folks that can't afford Stanford or Santa Clara." CodeSignal CEO Tigran Sloyan said the general coding assessment is taken by the vast majority of U.S. computer science students, and is intended to provide a "data-driven view" of people's coding ability. Students generally take it annually starting in their junior year, and can share their results with prospective employers, he said. The 70-minute test includes four questions to measure different coding skills. Launched six years ago, CodeSignal's assessment has become very popular among tech and financial companies, Sloyan said. The test, Sloyan contended, gives prospective employers a much better idea of a software engineering or software development candidate's qualifications than a resume, which may attract an employer's attention for the presence of a particularly prestigious school without any guarantee the student or graduate developed the commensurate skills. Every school has brilliant, average and mediocre students, Sloyan said. "Most companies want to go beyond resumes and find great people regardless of which schools they came from," Sloyan said. Sloyan believes San Jose State's rapid climb toward the top of the university pack in CodeSignal's rankings reflects the effectiveness of the school's faculty and programs. "Clearly San Jose State is doing something right when it comes to tech education," Sloyan said. "So far, the observation is that what they might be doing different from other schools is having a more hands-on approach to education." UC Berkeley and Stanford declined to comment on the rankings. Harshil Vyas, soon to graduate from San Jose State with a master's in software engineering, pointed to the school's tech-veteran instructors as a key benefit, along with large numbers of fellow students like him who have worked in tech and share their varied experiences with each other. The school's location in Silicon Valley is another boon, said Vyas, 25. "It's somewhat a motivation when you see the tech industry around you," Vyas said. "It helps you push to the goal." Copyright (C) 2025, Tribune Content Agency, LLC. Portions copyrighted by the respective providers.
Yahoo
14-05-2025
- Science
- Yahoo
CodeSignal Report Ranks Universities by Measurable Technical Skills, Highlighting Top Engineering Talent Nationwide
Nearly 1 in 3 top-performing students come from universities overlooked by traditional rankings SAN FRANCISCO, May 14, 2025 /PRNewswire/ -- CodeSignal, a leading skills assessment and experiential learning platform, today unveils its fourth annual University Ranking Report, an university ranking methodology based purely on students' verified coding skills. Unlike traditional rankings that rely on legacy signals, CodeSignal's report offers an objective, data-driven alternative: one that evaluates universities based on how well their students perform on an assessment of real-world coding skills. In an AI-transformed workforce, the ability to think computationally, solve problems, and write strong foundational code remains critical, regardless of where a student went to school. By analyzing thousands of General Coding Assessments (GCA) completed by students worldwide, CodeSignal's Talent Science Team reveals a powerful conclusion: top engineering talent is everywhere. Here are the top 15 universities for 2025: Carnegie Mellon University Massachusetts Institute of Technology Stony Brook University University of California, Los Angeles University of Pennsylvania California Institute of Technology University of California, San Diego Duke University San José State University University of Southern California Rice University Yale University Georgia Institute of Technology Johns Hopkins University Indiana University High-level results: 28.4% of high-scorers come from schools not included in the US News & World Report's top 50 undergraduate engineering programs. 12 of the top 50 schools in our skill-based ranking did not make the US News & World top 50. Two of the top 10 US schools in our rankings, Stony Brook University (#3) and San José State University (#9), didn't make the US News & World top 50. Korea Advanced Institute of Science & Technology is the top non-US school for software engineering talent this year, ranking just below Rice University (#12 on the US list). "This report is a celebration of the universities equipping students with the skills that matter most," said Tigran Sloyan, CEO and Co-Founder of CodeSignal. "When we focus on what students can actually do, not just where they studied, we uncover incredible talent from institutions of all types. It's a reminder that great engineers are everywhere, and we need to broaden how we recognize and recruit them." While traditional rankings reward legacy signals, CodeSignal's 2025 University Ranking Report focuses on outcomes – what students can actually do when faced with real-world engineering challenges. CodeSignal's data makes the case that technical talent isn't confined to a short list of name-brand schools. It's everywhere. For employers competing in an AI-driven economy, this report is a call to rethink where, and how, they discover their next generation of engineers. To view the full report, please visit: About CodeSignalCodeSignal is how the world discovers and develops the skills that will shape the future. Our AI-native skills assessment and experiential learning platform helps organizations hire, train, and grow talent at scale while empowering individuals to advance their careers. Whether you're growing your team's potential or unlocking your own, CodeSignal meets you where you are and gets you where you need to go. With millions of skills assessments completed, CodeSignal is trusted by companies like Netflix, Capital One, Meta, and Dropbox and used by learners worldwide. For more information, visit or connect with CodeSignal on LinkedIn. View original content to download multimedia: SOURCE CodeSignal
