Have you used Google Search recently? Exactly. Most companies, and most people, still think about Google when they think about search. They’re still spending heavily to rank there and paying for the ads around it.

But more people are asking ChatGPT, Claude and Gemini what to buy, read, use or trust.

SEO isn’t disappearing. It’s evolving into GEO.

Awad Sayeed, co-founder and CTO of Parsnipp AI, joins Thinking on Paper to explain generative engine optimisation, or GEO, and how companies can become more visible inside ChatGPT, Claude, Gemini and other AI answer engines.

Traditional SEO focuses on keywords, backlinks and rankings. GEO is more dependent on context: who the user is, what they’ve already asked, what they’re trying to achieve and how an AI system retrieves and combines information.

In this episode, we discuss:

• How generative engine optimisation differs from SEO

• Why context matters more than keywords in AI search

• How ChatGPT, Claude and Gemini use information differently

• What persona-based agents reveal about brand visibility

• How structured data helps AI systems understand websites

• Why comparison pages and clear product information matter

• What black-hat GEO could look like

• How AI-generated content could pollute the internet

• Whether brands should create separate experiences for humans and AI agents

• How advertising may develop inside AI assistants

Awad argues that GEO doesn’t replace SEO. Strong websites, useful content and clear structure still matter. But companies now need to think about whether AI systems can retrieve, interpret and recommend their information in the right context.

And as this is Thinking On Paper, we ask about the human impact, the wider change in the structure of the internet, trust, data and consumerism.

Please enjoy the show.

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Chapters

(00:00) Introduction to Generative Engine Optimization

(03:36) Understanding Persona-Based Agents

(06:23) The Transition from SEO to GEO

(09:06) Context in LLMs and GEO

(11:41) Black Hat Strategies in GEO

(14:22) The Future of the Internet

(16:58) Advertising in the Age of GEO

(19:37) The Impact of GEO

(28:22) The Evolution of AI Models

(29:03) Integrating AI into Business Strategies

(29:52) Agents vs. Humans

(32:10) The Future of SEO and GEO

(34:08) Tools for Visibility and Analytics in AI

(36:00) Customer-Driven Development

(39:23) The Role of Storytelling in GEO

(42:04) Model Transparency and the Future of AI

The UK produces world-class technology and is home to exceptional tech entrepreneurs. All too often it watches them scale in America.

Rory Daniels, Head of Emerging Technology and Innovation at techUK, joins Thinking on Paper to discuss whether the United Kingdom can remain competitive as quantum computing, robotics, photonics, AI and advanced computing begin to converge.

The UK has strong research institutions, deep technical talent and globally significant companies. Its recurring problem is scale. Promising technologies are often developed in British universities and laboratories, then commercialised or funded elsewhere.

In this episode, we discuss:

• What makes the UK robotics industry different from the US and China

• Why British companies often focus on specialised robots for nuclear sites, wind turbines and industrial environments

• How autonomous driving companies such as Wayve combine AI, sensors and connectivity

• Whether robotaxis can coexist with London’s black-cab industry

• Why UK technology companies struggle to scale after the startup stage

• How access to long-term capital affects quantum, robotics and semiconductor companies

• The role of universities, technology-transfer offices and regional innovation clusters

• What is happening in Coventry, Edinburgh, Milton Keynes, Barnsley and other UK technology centres

• How digital twins and simulation are used to train robots and autonomous vehicles

• Why photonics matters for quantum computing

• How quantum, photonic, neuromorphic and biological computing could converge

• Whether AI can develop the judgement and wisdom required to solve complex technical problems

• How techUK connects companies, researchers and policymakers

• Why public trust and adoption matter as much as technical performance

Rory argues that the UK’s advantage may not lie in dominating a single technology. It may come from combining existing strengths in AI, chip design, robotics, quantum computing, photonics and connectivity.

The conversation examines what government, industry, universities and investors must do if the UK is to convert strong research into companies that can scale globally without leaving the country.

Please enjoy the show.

Thinking on Paper is a technology podcast about AI, Space, quantum computing, science, and the systems shaping the future.

🏠 Buy us a beer on Substack

🎧Get Up Close On YouTube

🎧 Remember Steve jobs on APPLE

📺 Get the clips and outtakes on Instagram

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Chapters

(00:00) The UK Technology Landscape

(03:14) Robotics: A UK Perspective

(05:54) Autonomous Vehicles in the UK

(08:39) The UK's Innovation Ecosystem

(11:05) Challenges and Opportunities for UK Tech Entrepreneurs

(13:27) Regional Innovation and Government Initiatives

(16:33) The Role of Universities in Tech Development

(19:15) Barnsley: A Blueprint for Tech Towns

(21:53) Government Initiatives in Robotics

(24:20) Digital Twins and the Future of Robotics

(27:12) Quantum Computing and Photonics in the UK

(29:24) The Role of Education in Emerging Technologies

(30:55) AI and Human Wisdom: A Complex Relationship

(38:02) Neuromorphic Computing: The Future of AI

(38:23) Convergence of Technologies: Opportunities for the UK

(42:42) The Human Element in Technology Adoption

The Vij brothers join Thinking on Paper to discuss Neo, an autonomous machine learning engineer designed to automate parts of the AI development process.

As demand for AI systems grows, companies and governments are competing for a limited pool of experienced machine learning engineers. The challenge isn’t only access to data or computing power. Many organisations also lack the technical expertise required to build, test and deploy effective models.

Neo uses a multi-agent system to perform tasks normally handled by machine learning engineers, including analysing datasets, selecting modelling approaches, running experiments and evaluating results. The aim is to automate repetitive technical work while allowing human engineers to concentrate on higher-level decisions and more creative problems.

In this episode, we discuss:

• What an autonomous machine learning engineer is

• How Neo’s multi-agent AI system works

• Why skilled machine learning engineers are in such high demand

• Which parts of AI development can be automated

• How autonomous agents compare with traditional machine learning workflows

• Why Kaggle Grandmasters are considered leading practitioners in applied machine learning

• Whether AI agents can match expert human performance

• How automation could affect machine learning jobs and salaries

• The evolution of GPUs from graphics hardware to AI infrastructure

• What the Vij brothers learned from working at CERN

• How autonomous AI systems could change business, creativity and technical work

  
  

Neo is intended to expand access to machine learning expertise rather than simply generate code. Its development raises a wider question: what happens when AI systems can perform the specialised work required to build other AI systems?

This conversation examines the technical capabilities of autonomous machine learning agents, the shortage of experienced AI talent and how automation could reshape the role of engineers

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Timestamps

(00:00) Why Are There So Few Machine Learning Engineers?(01:54) Meet Gaurav Vij and Saurabh Vij(02:57) Lessons Learned from Working at CERN(04:45) How to Explain The Importance Of A.I. to Your Parents(07:24) The World’s First Autonomous Machine Learning Engineer: What AI Problem Does NEO Solve?(08:17) AI Competitions and Kaggle Grandmasters(11:06) How Many A.I./ML Engineers Do We Need?(17:30) Fixing The A.I. Hallucination Problem(18:09) Hot Buttons: 5 AI Questions In 30 Seconds(18:46) Hollywood: Doomed by A.I, or Reborn?(20:26) AI News: Nvidia Digits Explained(21:51) Moore's Law And Could AI Models Be Motivated by Rewards?(25:42) AI And Quantum Computing(29:45) The Thinking on Paper Carry-Over Question(30:16) After Hours: Backstage Extra

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Check out NEO: https://heyneo.so/Learn more about the show: www.thinkingonpaper.xyzFollow Thinking On Paper On Instagram: https://www.instagram.com/thinkingonpaperpodcast/