Artificial intelligence could create the most prosperous society in human history, or the greatest concentration of wealth ever seen.

In this episode, we explore three competing visions for AI’s future: Fully Automated Luxury Communism, Creative Destruction, and Power & Progress. We discuss who should own AI, whether work will disappear, how governments should respond, and present our own proposal for building an AI-powered society that maximises innovation while ensuring its benefits are shared across everyone, not just a handful of technology companies.

The conversation delves into the implications of the new social media policy in the UK, discussing the generational impact, motivation behind the policy, and the challenges of parental oversight and government intervention. It explores the influence of social media, the need for transparency and accountability in algorithms, and the potential impact of age verification and content regulation. The conversation also addresses the challenges of anonymity, foreign influence, and the potential impact of social media regulation on user behavior and societal isolation.

Takeaways

• The need for a balanced approach to social media regulation, addressing both the positive and negative aspects of social media for different age groups.
• The importance of transparency, accountability, and oversight in social media algorithms and content regulation, while considering the challenges of age verification and foreign influence.

Ross Horlock is a PhD researcher at University College London working on immunology for cancer treatment. He is an ex AstraZeneca and Syngenta research scientist.
Fede Gambedotti is a PhD researcher at University College London modelling energy transition and energy equity. He is a former power trading analyst at Drax Group.

In this episode of 2Humans, we look at two recent science papers that sound like they escaped from a sci-fi film: a digitally reconstructed fly brain/connectome, and brain organoids being explored as computing systems.

We talk through what the research actually shows, why it feels so strange, and what it might mean for neuroscience, computing, AI, and the slightly unsettling future of biological machines.

Links and references:

Kagan et al. (2022): https://www.sciencedirect.com/science/article/pii/S0896627322008066 Cai et al. (2023): https://www.nature.com/articles/s41928-023-01069-w Robbins et al. (2026): https://pubmed.ncbi.nlm.nih.gov/41720084/ Scientific American / Cortical Labs (2026): https://www.scientificamerican.com/article/how-human-neurons-on-a-chip-learned-to-play-doom/
Schlegel et al. (2024): https://www.nature.com/articles/s41586-024-07686-5 Shiu et al. (2024): https://www.nature.com/articles/s41586-024-07763-9 NIH context page: https://www.nih.gov/news-events/news-releases/researchers-fully-map-neural-connections-fruit-fly-brain Eon Systems demo: https://eon.systems/updates/embodied-brain-emulation

The conversation delves into the importance of the energy sector for economic growth and energy independence. It explores the cost benefit of renewable energy versus fossil fuels, highlighting the long-term sustainability and economic advantages of renewable energy. Additionally, it discusses the challenges and opportunities in transitioning to renewable energy and the impact of energy choices on climate change and environmental sustainability.

Takeaways

• Renewable energy is cheaper and more sustainable long term
• The energy sector is essential for economic growth and energy independence

Chapters

• 00:00 Intro: fossil fuels, nuclear or renewables?

  02:40 Why energy matters for the economy

  05:12 Climate change, extreme weather and the energy transition

  07:35 Why fossil fuels are coming back into the debate

  10:31 Are renewables actually cheaper?

  14:38 The case for and against nuclear power

  18:20 Centralised vs decentralised energy systems

  25:24 What the ideal energy mix could look like

  29:17 Why investors still favour fossil fuels

  31:28 Energy independence and national security

  36:15 Household energy independence and inequality

  39:49 Energy communities and local solutions

  42:52 Are renewables still the future?

  48:51 What Norway and China understand about energy

  51:51 Final thoughts

Ross Horlock is a PhD researcher at University College London working on immunology for cancer treatment. He is an ex AstraZeneca and Syngenta research scientist.
Fede Gambedotti is a PhD researcher at University College London modelling energy transition and energy equity. He is a former power trading analyst at Drax Group.

In this conversation, Ross and Fede discuss the release of the Anthropic Mythos model and its implications, the future of access to AI models and tools, the banning and limiting of data centers in the US states, and the impact of data center restrictions on the global economy and security. The conversation covers the impact of the Iran war on global energy security, the introduction of Mamdani's mansion tax in New York City, and the discussion of SpaceX's IPO and acquisitions. The challenges of energy independence in Europe and the implications of Mamdani's tax policy are highlighted as key takeaways.

Takeaways

• Cybersecurity models are becoming more specialized and restricted in their usage
• The impact of data centers on energy consumption and economic development Energy independence challenges in Europe
• Implications of Mamdani's mansion tax in New York City
  
  Links:
  Substack Article: https://substack.com/@fedegam/note/p-194626903?r=20m56t&utm_source=notes-share-action&utm_medium=web

Chapters

• 00:00 Introduction to Current Events in Tech
• 06:10 The Future of Access to AI Models and Tools
• 13:10 Banning and Limiting Data Centers in the US States
• 22:17 The Impact of Data Center Restrictions on Global Economy and Security
• 29:50 Impact of Iran War on Energy Security
• 56:46 Mamdani's Mansion Tax
• 01:08:41 SpaceX IPO and Acquisitions

Ross Horlock is a PhD researcher at University College London working on immunology for cancer treatment. He is an ex AstraZeneca and Syngenta research scientist.
Fede Gambedotti is a PhD researcher at University College London modelling energy transition and energy equity. He is a former power trading analyst at Drax Group.

The conversation delves into the concept of immunotherapy as a revolutionary approach to cancer treatment. It covers the nature of cancer, the role of the immune system, the history of cancer treatment, and the emergence of immunotherapy. Additionally, it explores the potential of CAR T cell therapy as a promising immunotherapeutic approach. The conversation delves into the world of immunotherapy and CAR T cell therapy, exploring the different types of immunotherapy, their mechanisms, accessibility, and cost. It also discusses the future of cancer treatment and research, highlighting the potential impact of these therapies on the field of oncology.

Takeaways

• Cancer is a result of cells in the body losing control of their proliferation, apoptosis, and immunosurveillance.
• Immunotherapy involves reprogramming the body's immune system to target and destroy cancer cells. Immunotherapy is a revolutionary approach to cancer treatment
• CAR T cell therapy is a promising but expensive treatment option

Chapters

• 00:00 Introduction to Immunotherapy
• 09:18 Types of Cancer and Prevention
• 14:24 Key Components of Cancer Treatment
• 22:45 Introduction to Immunology
• 28:09 Immunotherapy and Cancer Treatment
• 54:25 CAR T Cell Therapy
• 01:01:14 Introduction to Immunotherapy and CAR T Cell Therapy
• 01:07:03 Accessibility and Cost of Immunotherapy

Ross Horlock is a PhD researcher at University College London working on immunology for cancer treatment. He is an ex AstraZeneca and Syngenta research scientist.
Fede Gambedotti is a PhD researcher at University College London modelling energy transition and energy equity. He is a former power trading analyst at Drax Group.

Are AI consciousness and human rights compatible—or even possible? 

In this episode, we dive into the profound philosophical and practical questions surrounding AI's potential to be truly conscious, debating whether human-like self-awareness is necessary or even meaningful in machines. We explore everything from the importance of embodiment and agency to the evolution of morality, considering if AI might eventually demand rights and recognition akin to living beings—and what that means for society at large.

In this episode:

• The fundamental uncertainty of what consciousness really is, and whether AI can ever truly possess it (00:00)
• The importance of embodiment, agency, and self-perception in defining consciousness for machines (01:12)
• Are emotions and instincts a necessary component of consciousness, or mere biological by-products? (05:20)
• The potential for AI to evolve self-referential awareness through iterative self-improvement (07:00)
• How natural selection and propagation could lead to machine consciousness beyond human definitions (12:35)
• The philosophical debate over the “sense of self” and whether it’s an illusion or a real phenomenon (14:00)
• Ethical implications: should we grant rights to AI or robots that possess or might develop consciousness? (18:23)
• The future societal and legal challenges of AI with perceived consciousness, including ownership, rights, and moral treatment (23:04)
• The possibility of AI bodies embodying sentience and what that would mean for human-AI relationships (24:23)

Note: As AI continues to evolve, understanding consciousness isn't just a philosophical exploration—it's a societal necessity. Whether AI can develop a form of self-awareness or simply mimics it, the moral, legal, and strategic implications for the future are profound. Stay tuned as this conversation evolves and becomes increasingly relevant for ambitious professionals shaping the future of AI and technology.

Ross Horlock is a PhD researcher at University College London working on immunology for cancer treatment. He is an ex AstraZeneca and Syngenta research scientist.
Fede Gambedotti is a PhD researcher at University College London modelling energy transition and energy equity. He is a former power trading analyst at Drax Group.

Main Topics Covered with Timestamps:

• (0:00) - Defining consciousness: What makes us aware?
• (1:09) - Why philosophical debates struggle with defining consciousness
• (1:43) - The role of phenomenology and inner experience in consciousness
• (2:18) - Measurement challenges: Can AI ever be truly conscious?
• (3:08) - AI's emulation skills, ChatGPT, and mimicking consciousness
• (4:20) - The Chinese Room thought experiment and understanding vs. symbol manipulation
• (6:31) - Neural processes: AI vs. human brains, and if understanding equates to intelligence
• (7:54) - Can AI develop true mental images or is it just relational pattern matching?
• (8:58) - The importance of phenomenology—experience of consciousness in humans
• (10:54) - The stochastic parrot: Is repetition just parroting or evidence of some understanding?
• (12:48) - Creativity as combinatorial intelligence and limitations in AI
• (13:46) - Functionalism and the Turing Test: Appearance vs. reality
• (15:31) - AI seeking answers in latent space: Is this enough for consciousness?
• (17:42) - Embodiment and sensory perception as the basis for true consciousness
• (21:19) - Is self-perception necessary for consciousness?
• (22:25) - Can AI ever be truly conscious or is it fundamentally impossible?
• (23:44) - The three positions: skeptic, agnostic, and the welfare camp
• (24:57) - Ethical implications: Should we grant AI rights if it becomes conscious?
• (31:23) - The moral and philosophical weight of AI: When does it matter?

• • (32:47) - Expanding consciousness beyond anthropocentric views—could AI or alien life have different defining features?

Ross Horlock is a PhD researcher at University College London working on immunology for cancer treatment. He is an ex AstraZeneca and Syngenta research scientist.
Fede Gambedotti is a PhD researcher at University College London modelling energy transition and energy equity. He is a former power trading analyst at Drax Group.

Will AI Replace Researchers or Transform Them?

This episode cuts through the hype, exploring whether AI is a threat to academic experts or a tool that will redefine the role of research itself. Ross and Fede, both PhD researchers, debate whether future scientists will be skilled problem solvers or just AI operators, and how academia must balance harnessing AI’s productivity with preserving essential human skills.

In this episode:

• The current impact of AI on PhD research, from literature reviews to coding (timestamps 0:41, 1:42)
• The risk of cognitive offloading and losing foundational skills such as methodology and troubleshooting (timestamps 2:53, 4:47)
• How AI’s biases and training data could influence scientific objectivity and integrity (timestamps 7:06, 22:17)
• The divergence between academia and industry: hands-on problem solving vs. AI management (timestamps 8:43, 9:35)
• The future of scientific expertise: Will AI make researchers obsolete or merely shift their focus? (timestamps 14:22, 22:03, 33:40)
• The challenge of maintaining objectivity and combating biases in AI-generated research outputs (timestamps 26:36, 29:19)
• The societal implications: replicating global knowledge equity and the role of academia as a verifier of truth (timestamps 35:40, 36:15)
• Practical insights into how AI is boosting productivity—freeing researchers from mundane tasks to focus on meaningful innovation (timestamps 36:50, 37:35)
  

Key moments of disagreement:

• The balance between utilising AI for maximum impact versus developing foundational skills (discussed at 5:14 and 11:07)
• Whether AI’s inability to provide objective, universal truths makes it unsuitable for certain research fields (around 31:36)

• The potential obsolescence of expert researchers versus AI’s role as an enabler for democratised knowledge and productivity (around 23:44 and 33:40)

Ross Horlock is a PhD researcher at University College London working on immunology for cancer treatment. He is an ex AstraZeneca and Syngenta research scientist.
Fede Gambedotti is a PhD researcher at University College London modelling energy transition and energy equity. He is a former power trading analyst at Drax Group.

In this episode, we challenge the prevailing narrative around AI's rapid growth, questioning whether the industry is a bubble built on hype or a genuine breakthrough. We dissect the financial signals of overvalued stocks, the real productivity gains versus inflated valuations, and the risk that the entire AI boom could unwind like the tech bubble of the early 2000s. With candid debates on energy costs, market dominance, and societal impacts, this episode offers a clear-eyed perspective for ambitious professionals eager to understand whether AI's promises match its realities—or if we're simply overestimating ourselves in a high-stakes game of hype.

Timestamps:

00:15 - Are we overestimating AI or underestimating ourselves?

02:07 - The AI arms race and astronomical hardware investments

03:31 - Strategies of tech giants: developer, integrator, hardware provider

04:43 - AI's role in job replacement and efficiency gains

09:34 - Monetization models for AI and the profitability challenge

14:24 - The energy demands and physical limits of scaling AI

16:03 - Market concentration and the risk of monopolistic dominance

17:26 - Language models’ limitations in understanding and science

20:56 - Building specialized, cost-effective AI for specific tasks

24:41 - Bubble or sustainable industry? Evaluating long-term value

36:27 - Risks of oligarchic control and societal biases in AI deployment

36:39 - Practical steps: AI literacy and staying human-centered

38:20 - Wrap-up and future topics in AI discussion

This episode unpacks the deeper realities of AI’s exponential growth, making it essential listening for digital entrepreneurs, investors, and AI enthusiasts who want to navigate the hype with sharper insight and a strategic mindset.

Recorded November 2025