What happens when AI (artificial intelligence) advances faster than society can adapt? The Gage podcast Episode 36 by Greengage dives deep into this pressing question, exploring the crossroads of finance, technology, and the future of work.
In this compelling episode, Jonathan (Viscount Camrose) Berry who is the Director of Camrose Management Limited, a member of the House of Lords, AI researcher, and former minister under Prime Minister Rishi Sunak is here, sharing his insights on the rapid evolution of AI, the critical role of governments in shaping tech policy, and the transformation of recruitment practices.
From digital identity to blockchain, this blog explores the bold insights into how innovation is reshaping the way we work and live.
Jonathan’s journey into AI began early in life, sparked by his father’s work and a passion for science fiction classics like 2001: A Space Odyssey. These early influences made him curious about AI.
Jonathan mentioned in the podcast Episode 36 – “I’ve been interested in AI since I was a child… My father, who was a science journalist, wrote one of the very early popular books about AI… From then, I’ve always taken an interest.”
Jonathan studied at illustrious universities like Carnegie Mellon and Birkbeck, where he explored the capabilities and limitations of AI. His academic experience there was not only intellectual – it was a personal quest into a technology that was still in its infancy and untested.
Long before AI became a regular term, Jonathan was already intrigued in its possibilities. This connection gave him an understanding of AI’s evolution, positioning him as both a pioneer and thoughtful commentator on the future of intelligent technology.
How ChatGPT Marked a Paradigm Shift
Traditional AI has historically been based on efforts to mimic the human brain, using neuroscience as inspiration to mirror mental processes.
The first AI models tried to mimic the way that neurons behave, seeking to reproduce human intelligence by mimicking brain-like structures. But the arrival of ChatGPT created a major paradigm shift in this thinking.
Rather than attempting to mimic the human brain, ChatGPT uses data inference – learning patterns from huge volumes of text data in order to respond. This move away from brain replication and toward pattern recognition unlocked new potential in AI uses.
The foundation of this revolution was the emergence of big data, which brought with it the sheer volume of information required to train robust models like ChatGPT.
Through the power of big data, today’s AI systems have transcended conceptual models to be usable, scalable technologies capable of understanding and producing human language, revolutionising how we communicate with machines and the extent of AI’s practical applicability.
The UK’s Position in Global AI & Blockchain Innovation
The UK has long been a pioneering force in computer science, with historical figures like Ada Lovelace and Alan Turing laying the foundations for modern computing. Such ground-breaking work set the stage for innovations that continue to influence technology worldwide.
Jonathan mentioned in the podcast Episode 36 – “Our strong view was that AI needed to be adopted very widely… and if it was going to be adopted widely, it had to be trustworthy.”
Today, the UK is proactively working to sustain its competitive edge in emerging fields like AI and blockchain. Government initiatives, such as the AI Safety Summit held at Bletchley Park – a site symbolic of the country’s codebreaking heritage, which highlights the willingness for the UK to advance responsible AI development. This summit also reflects the UK’s proactive stance on managing the ethical and security challenges of new technologies.
Interestingly, the UK’s strategy on AI innovation is reflected in its response to blockchain technology and that focuses on transparency, regulation, and cross-sector collaboration between private and public.
Combined, these initiatives make the UK a world leader pushing on with state-of-the-art technologies while ensuring that innovation does not jeopardise responsibility. But as these innovations evolve, they raise a critical question: are legacy systems – especially in recruitment – keeping pace?
Recruitment Today
Recruitment has not changed significantly over the past centuries, relying on traditional methods of matchmaking. This legacy framework struggles to keep pace with the sheer volume of applications flooding in, especially with some platforms making it easier than ever to submit low-effort, generic applications.
As a result, recruiters face an overwhelming task just to sift through candidates, often leading to missed opportunities for both employers and job seekers.
Meanwhile, there is a race between applicant-boosting AI designed to enhance candidate profiles – assisting candidates in creating more refined resumes and cover letters – and equally sophisticated AI software used by recruiters to screen and sort applications.
This technology pull contest also makes the environment for hiring more complex, with issues surrounding fairness, authenticity, and whether today’s recruitment methods really work in placing the most qualified individual in a position.
How to make Smarter, Human-Centered Recruitment
The future of recruitment is in a need for smarter, human-centered approach that goes beyond simply automating resume screening.
Instead, recruitment systems should be designed around the real needs of organisations and the true potential of candidates.
Jonathan mentioned in the podcast Episode 36 – “You end up in a system of adversarial AI agents, some polishing the applications, and some rejecting the applications.”
By leveraging AI not just to screen resumes, but to deeply assess skill fit and predict job performance, companies can make more informed and meaningful hiring decisions. This change can reduce the emotional toll on candidates who face endless rejections or mismatched roles and lessen the economic costs businesses pay from high turnover and bad hires.
A recruitment process grounded in empathy and understanding empowers both employers and job seekers, creating opportunities for growth and success.
Ultimately, embracing AI as a supportive tool – not a gatekeeper – can transform hiring into a fairer, more effective, and genuinely human experience.
A Future Vision: Skills, Blockchain, and Trust
In the podcast, Jonathan proposes a transformative future where a universal, blockchain-based taxonomy of human skills becomes the new standard.
Rather than trusting fuzzy job descriptions or stodgy degrees, this system would index and lock down verified competencies on an unalterable ledger.
Artificial intelligence would be instrumental, not only in matching the right candidates to right opportunities, but in rigorously testing and verifying individual capabilities in particular domains.
The result? A job market where recruitment is more precise, inclusive, and efficient. This future also promises greater personalisation and privacy: people could selectively share verified credentials, controlling who sees what.
Jonathan mentioned in the podcast Episode 36 – “Those who get an offer are going to be better qualified even without seeing a CV…. Those who don’t get an offer… have learned something very concrete… which they can take to market for more suitable roles.”
Beyond recruitment, this concept has broad implications for lifelong learning, workforce mobility, and trust in digital identities.
The Impact on Employment Models
As recruitment becomes smarter and more precise, the very structure of employment may begin to change. Jonathan discusses how efficient, skill-based recruitment could unlock an extraordinary workforce strength, where individuals move fluidly between projects, industries, or geographies based on verified capabilities rather than static resumes.
In this changing environment, the conventional full-time jobs will yield to result-based contracts, wherein individuals are contracted for certain objectives and remunerated according to outcomes. This format benefits both organisations and workers: businesses attain flexibility, and individuals gain more freedom and exposure to diverse opportunities.
Jonathan mentioned in the podcast Episode 36 – “That suggests to me… far more contracting for a particular outcome type of work and less full-time employment.”
At its core is the capability to authenticate performance history, perhaps secured through blockchain, which aligns incentives and diminishes risks of hiring. With increased faith in credentials, there is greater faith in fluid employment models.
The outcome may be a dynamic, on-demand labour force centred on skill, openness, and shared responsibility, remaking the future of work we know.
Rethinking Education for the AI Era
In the age of AI, traditional education models are proving too slow and rigid to meet the demands of a rapidly evolving workforce.
Jonathan emphasises the urgent need for “rapid apprenticeship” approaches – short, intensive learning experiences grounded in real-world challenges and immediate skill assessment.
Rather than relying on static degrees, the focus must shift to show skills that reflect current capabilities and readiness to contribute. This model values what people can do now, not just what they studied years ago. It also promotes continuous, lifelong learning, treating education as an ongoing, applied process rather than a one-time credential.
With AI transforming industries at speed, this dynamic approach empowers individuals to stay relevant, adaptable, and employable.
Jonathan’s core message is clear: recruitment and the nature of work must evolve to keep pace with AI’s rapid advancements. He argues that current hiring systems are outdated, inefficient, and fail to reflect the real capabilities and potential of candidates.
Instead, we need smarter, human-centred approaches that leverage AI not just for automation, but for deeper insights into skills and performance. As work shifts toward more dynamic, outcome-driven models, recruitment must become more personalised, transparent, and equitable.
To learn more, listen to our podcast series, The Gage Episode 36.
