Meet the 2026 Nova 111 Supernovas of the Year
Every year, the Nova 111 list identifies and empowers the most talented 111 young professionals across the markets where Nova operates — currently Spain, Italy, Sweden, and the UK. The list spans 11 industries, from energy and sustainability to management consulting, with 10 winners in each vertical. But one candidate from each country shines brighter than the rest and earns the title of Supernova of the Year.
In the 2026 edition, that honor went to Sophia Radley-Searle in Spain — a pharmacist turned COO and co-founder of Aximmit, a B2B tech marketplace expanding access to medicines across Sub-Saharan Africa, Southeast Asia, and South America — and Giulia Musacci in Italy — a mechanical engineer who spent nearly a decade at McKinsey before joining GSmart, where she advises private equity funds, boards, and CEOs on finding the right leaders for the right moments.
Both women joined Signal by Nova to share how AI is fundamentally changing the way they work, hire, and build — and what it means for the rest of us.
From Pharmacist to Global Health Disruptor: Sophia Radley-Searle and Aximmit
Sophia's career has been shaped by a deeply personal mission. Growing up watching her mother live with multiple sclerosis, she developed a conviction that healthcare access was broken — and that technology could fix it. After eight years at GSK learning how big pharma operates (and where it falls short), she earned a Lasha Fellowship to Harvard Business School, met her co-founders, and launched Aximmit in 2022.
Aximmit is a B2B tech marketplace that aggregates pharmaceutical demand digitally at scale and uses AI to match it with qualified suppliers around the globe. The platform also includes a logistics layer that dramatically reduces last-mile delivery costs for medicines in low- and middle-income countries.
How Does Aximmit Use AI to Expand Access to Medicines?
Aximmit's customers are primarily governments, public health procurement agencies, NGOs, and multilateral organizations. The platform replaces a procurement process that historically ran on Excel spreadsheets posted on government websites. AI now dramatically reduces the time it takes to find qualified pharmaceutical suppliers, verify their credentials, and connect them with buyers through an open bidding system.
"We're now 52 people around the world. Before 2022, to build what we have built, we would have needed around 150. We can do what we're doing with 50." — Sophia Radley-Searle, COO & Co-founder of Aximmit
The company is active in more than 20 markets — primarily in Sub-Saharan Africa — with just two people in regulatory affairs. Sophia attributes this to AI-powered tools that handle everything from audit reporting to regulatory intelligence, noting that tasks that once required months of consulting work can now be answered by their internal AI engine.
From McKinsey to People Advisory: Giulia Musacci's Path to GSmart
Giulia's journey started with a deep love for math and engineering. After studying mechanical engineering at Milan's Polytechnic, she quickly realized that the daily work of a traditional engineer lacked the variety and intellectual challenge she craved. A McKinsey recruiting event changed everything — the promise of switching clients, topics, and teams every three to six months was exactly the pace she needed.
She spent nearly 10 years at McKinsey, earned an MBA at London Business School, and rose from business analyst to junior associate partner. But a recurring frustration — delivering 200-page strategy decks that collected dust — pushed her to seek more tangible impact.
That led her to GSmart, a consulting firm that helps private equity funds, boards, and CEOs find and assess the right leaders for specific company moments. For Giulia, the shift from advising on the what to advising on the who has been transformative.
"If the right person is there, there is a very big uplift in whatever company performance or people performance." — Giulia Musacci, GSmart
What Are the Keys to Building a Great Career in 2026?
Drawing from her experience assessing top executives, Giulia identified three pillars for career success in 2026.
International and cross-context exposure. The most successful professionals Giulia encounters have worked across countries, company sizes, and cultures. This breadth builds adaptability — the single most valuable trait in a fast-changing world.
Technology fluency, especially in AI. AI is no longer a nice-to-have on a resume. It is becoming a core part of the professional equation. Understanding how to use it, when to trust it, and when to challenge it is rapidly becoming table stakes.
Business sense beyond textbook finance. Giulia stresses that this is more of a soft skill than an academic one — understanding how organizations actually work, how decisions are made, what to prioritize, and what to let go. This instinct is rarely taught in university but is essential for leadership.
How Is AI Changing What CEOs Look for in Talent?
From her seat at GSmart, Giulia sees a nuanced shift in executive hiring priorities. The majority of CEOs she advises remain cautious about AI, not because they dismiss it, but because they recognize a critical asymmetry: AI always produces a confident-sounding answer, but it is frequently wrong or partially right — and most people lack the expertise to spot the difference.
This has led to a measurable shift: companies are increasingly prioritizing experienced hires over juniors. Senior professionals bring the domain expertise needed to challenge AI outputs, while juniors — who traditionally handled research, data gathering, and document synthesis — find those tasks increasingly automated.
"You need people that already know the answer. AI fluency without expertise is dangerous." — Giulia Musacci, GSmart
What Happens to Junior Talent in an AI-First World?
This is one of the most pressing questions in talent strategy today. If companies stop hiring juniors for entry-level analyst and research roles, how does the next generation build the judgment that only comes from experience? Giulia draws a parallel to previous technological disruptions — the telephone switchboard, the calculator, the personal computer — each of which eliminated certain roles while creating new ones.
Her advice for students and early-career professionals: specialize. Whether in vertical domain knowledge, AI prompting, or programming, generalists will face a tougher market. The professionals who thrive will be those who combine curiosity with the ability to challenge AI — not just use it.
Startups vs. Big Corporates: Two Centuries Apart in AI Adoption
One of the most striking takeaways from the conversation was the gulf between how startups and large enterprises are adopting AI. The contrast is so stark that Giulia described it as being in different centuries.
How Are Startups Using AI in 2026?
At Aximmit, AI is not a side project — it is the operating system. With 52 people doing the work that would have required 150 pre-AI, the company uses AI across quality assurance, regulatory intelligence, supplier matching, procurement, and even internal tooling. Sophia's team recently switched entirely to Claude for enterprise AI, and they are now building internal tools directly with code generated by AI rather than relying on no-code platforms.
The shift in software development has been equally dramatic. Traditional agile sprints have given way to continuous shipping. Non-technical team members are building and deploying their own internal tools. The bottleneck has moved from building to reviewing — prompting Sophia's team to adopt AI code review agents.
"Shipping is no longer just for tech. Anybody can start shipping products and updates across an organization." — Sophia Radley-Searle
Why Are Large Enterprises Falling Behind on AI?
Giulia paints a very different picture for the big corporates she advises. Many employees still use personal ChatGPT accounts to polish emails — unknowingly feeding company data into public models. Formal AI training is rare. Most employees use only the tools already embedded in Microsoft Office, and many do not even know those features exist. The barrier is not technology; it is organizational inertia, leadership hesitancy, and the real cost of change when you have tens of thousands of employees.
"They are still working in silos and delivering new products in years, not months. It really is another century." — Giulia Musacci, GSmart
Giulia argues that the key enabler for AI transformation in large organizations is finding even one or two leaders with the right mindset — executives willing to experiment and champion change. Without visible, relatable examples of success, most large companies will continue to hesitate.
AI Fluency as a Hiring Criterion: The Sapia Model and Internal Certification
Aximmit has gone further than most in operationalizing AI fluency. The company started with Microsoft's AI fluency model and has since adopted the Sapia model, which tiers AI competency more aggressively. The lowest level — using AI merely as a chat assistant for polishing emails — is explicitly classified as "unacceptable." The company now requires internal certifications before employees receive enterprise AI licenses, and it ties access directly to demonstrated competency.
This matters financially. Enterprise AI licenses are expensive, and usage costs scale unpredictably. Sophia describes managing Claude credits as a genuine operational challenge — one employee inadvertently burned through $1,000 in a single week. For startups watching their runway, AI spend is quickly becoming as critical to forecast as headcount.
How Do You Measure the ROI of AI Adoption?
Sophia is candid about this challenge: measuring AI ROI is almost impossible at this stage. There is a meaningful difference between feeling hyper-productive and actually moving company KPIs. Many organizations are still in the early excitement phase, where unlocked capabilities feel transformative but bottom-line metrics have not yet moved. Aximmit is actively trying to quantify the impact, but acknowledges that the frameworks for doing so are still immature.
The AI Complacency Trap: Why Working in Isolation with AI Is Dangerous
One of the most thought-provoking moments in the conversation centered on what the hosts called the AI complacency effect. Research suggests that most LLMs agree with user inputs roughly 45% more than a human interlocutor would. This validation feels good — it makes people more confident in their ideas and more likely to keep using the tool.
The problem emerges in team settings. When every team member returns from their individual AI sessions with reinforced and validated ideas, meetings become clashes of AI-strengthened convictions rather than genuine explorations of different perspectives. The result is divergence rather than convergence — the opposite of what collaborative decision-making requires.
Can Judgment Be Engineered? The Agent Council Approach
A potential solution comes from Andrej Karpathy, co-founder of OpenAI, whose recent posts have gone viral. Karpathy has described building a council of AI agents — five agents, each with a different reasoning style. One plays the contrarian, another explores unconventional angles, and so on. The goal is to systematically challenge your own thinking rather than having it validated.
The Signal by Nova hosts report having tested the approach and finding it genuinely useful for complex decisions — it surfaces blind spots that a single AI conversation would miss entirely. This may be one of the most practical frameworks for combating AI complacency at both individual and organizational levels.
Industry Signals: Anthropic's Rise and the European Startup Ecosystem
Anthropic Overtakes OpenAI in Annual Recurring Revenue
In one of the most significant shifts in the AI industry, Anthropic has reportedly surpassed OpenAI in ARR, reaching an estimated $30 billion. This follows a period of extraordinary shipping velocity — roughly 50 product and model releases in two months. The company's partnerships with Apple, Nvidia, and other industry leaders have strengthened its enterprise positioning.
Sophia's experience mirrors this trend — Aximmit recently switched its entire organization to Claude after testing multiple models, calling the switch transformative for their workflows.
Spain's Startup Ecosystem Reaches a Tipping Point
The conversation closed with an optimistic signal for European founders. Tier-one US investors — including a16z, Lightspeed, and General Catalyst — are now investing directly in Spain for the first time. Notable milestones include Supersonic receiving Spain's first pre-seed investment directly from a16z, and PLD, a Spanish rocket company, raising a €180 million round.
However, both guests emphasized that the ecosystem still faces structural challenges. Check sizes in Europe remain small relative to the US, pension fund allocations to venture capital are minimal, and Italian founders in particular still need to leave the country for Series B and beyond.
"The size of the check determines the size of the problem you can solve. You cannot solve access to medicines worldwide with a 300K pre-seed." — Sophia Radley-Searle
Key Takeaways for Professionals and Organizations
For individuals: AI fluency is no longer optional — but fluency without judgment is dangerous. Invest in domain expertise alongside AI skills. Specialize rather than generalize. And be aware of the complacency trap when working with AI in isolation.
For startups: AI is not just a product feature; it is an operating model. Companies like Aximmit are achieving 3x leverage on headcount through systematic AI integration. But costs are real and unpredictable — budget for AI spend as seriously as you budget for salaries.
For enterprises: The gap with AI-native startups is widening fast. Internal champions, visible examples of success, and formal AI training programs are the minimum viable response. The organizations that treat this as a leadership challenge, not just a technology one, will adapt fastest.
For the ecosystem: Europe is accelerating, but structural funding gaps persist. Founders, investors, and policymakers all have a role in closing the gap between talent density and capital availability.
About Signal by Nova: Signal is a weekly show by Nova where we curate the latest news on tech, AI, and the future of work — cutting through the noise to bring you what matters most. Watch the full episode on YouTube or subscribe wherever you get your podcasts.
About the Nova 111 List: The Nova 111 list identifies and empowers the most talented 111 young professionals in Spain, Italy, Sweden, and the UK across 11 industries. Learn more about the 2026 list.
