In this episode, we had the privilege of speaking with Akhil Patel, a globally recognized expert in economic cycles, discusses the 18-year boom-bust pattern and warns that we're approaching the peak of the current cycle in 2026, with a major financial crisis likely in 2027. He analyzes the AI investment bubble, draws parallels to historical manias, and provides practical strategies for businesses and investors to prepare for the downturn.

Episode Summary

1. Understanding Economic Cycles - Akhil Patel explains why cycles matter, emphasizing that cyclical patterns appear throughout nature and human behavior, particularly in stock markets and economies. Understanding these rhythms helps predict both prosperity and crisis periods.

2. The 18-Year Cycle Theory - the hypothesis of a regular 18-year boom-bust cycle (sometimes 16-20 years) in Western economies, particularly the US and UK. This pattern, first identified by economist Homer Hoyt in the 1930s through Chicago land sales data, has preceded every major financial crisis over the past 200 years.

3. Land Values Drive Cycles - Land is identified as the key indicator because it's a scarce, monopolistic asset that captures economic surplus. Property prices and speculation patterns serve as the primary mechanism driving both the boom and bust phases, with banking credit amplifying these movements.

4. Current Cycle (2011-2026) - Walking through the present cycle, Akhil identifies 2011-2012 as the starting point following the 2008 crisis. The COVID pandemic compressed what would normally be a 7-year second half into just 2 years of mania (2020-2022), though we're still seeing bubble behavior in AI investments arriving on schedule.

5. AI Investment Bubble Analysis - The current AI sector exhibits classic bubble characteristics: inflated valuations disconnected from fundamentals, enormous capital investment with questionable returns, and incestuous interconnections between major players (Nvidia, OpenAI, Oracle). Parallels are drawn to the dot-com bubble, 1980s Japan, and 19th-century railway booms.

6. Crisis Timing: 2026-2027 - Akhil predicts the property market will peak in 2026, with a major financial crisis following 6-12 months later in 2027. The trigger location is uncertain but likely in areas with extreme speculation—possibly the Middle East, parts of Asia, or unexpectedly in Germany, rather than the US which remains cautious after 2008.

7. Practical Preparation Strategies - Key recommendations include: avoid leverage, build cash reserves, ensure businesses can survive revenue declines, don't buy based solely on capital gains momentum, and position to acquire assets during the downturn. The advice emphasizes survival first, then opportunistic expansion during recovery.

8. Future Outlook Beyond Crisis - Despite the predicted downturn, Akhil remains optimistic about the next cycle (post-2030), believing AI and blockchain technologies are genuinely transformative once properly applied. The tech sector typically leads recovery, offering significant opportunities for those who survive the crisis with resources intact.

Peter Bärnreuther from Munich RE discusses the emerging field of AI insurance, explaining how companies can manage the inherent risks of probabilistic AI systems through specialized insurance products. The conversation covers real-world AI failures, different types of AI risks, and how insurance can help both corporations and AI vendors scale their operations safely.

Key Topics Discussed

Peter's Career Journey: Peter Bärnreuther transitioned from studying physics and economics to risk management at Accenture, then Munich RE, where he developed crypto insurance products before joining the AI risk team to create coverage for AI-related risks.

Probabilistic vs Deterministic Systems: Unlike traditional deterministic systems where errors can be traced, AI systems are probabilistic - they can be 99.5% accurate but never 100% certain, creating fundamental new risks that require insurance coverage.

AI Risk Categories: Two main types exist - traditional machine learning risks (classification errors like fraud detection) and generative AI risks (IP infringement, hallucinations, legal compliance issues), each requiring different insurance approaches.

Real-World AI Incidents: Examples include airline chatbots promising unauthorized discounts, lawyers using fake legal cases, and AI house valuation systems losing $300M+ by failing to adjust to market changes during price drops.

Insurance Product Structure: Munich RE offers two main products - one for corporations using AI internally for risk mitigation, and another for AI vendors needing trust-building to scale their business and attract enterprise clients.

Specific Use Cases: Successful implementations include solar panel fault detection (100% accuracy guarantee), credit card fraud prevention (99.9% performance guarantee), and battery health assessment for electric vehicles with compensation guarantees.

Market Challenges: Key difficulties include pricing models with limited historical data, concept drift where AI performance degrades over time, accumulation risk when multiple clients use similar foundation models, and "silent coverage" issues in existing insurance policies.

Future Market Outlook: AI insurance may either become a separate line of business (like cyber insurance) or be integrated into traditional policies, with current focus on US and European markets and strongest traction in IT security applications.

Chris Parmer (Chief Product Officer & Co-Founder, Plotly) and Domenic Ravita (VP of Marketing, Plotly) discuss the evolution of AI-powered data analytics and how natural language interfaces are democratizing advanced analytics.

Key Topics Discussed

1. AI's Market Category Convergence Domenic describes how AI is collapsing traditional boundaries between business intelligence tools (Power BI, Tableau), data science platforms, and AI coding tools, creating a quantum leap similar to the drag-and-drop revolution 20 years ago.
2. The 30/70 Engineering Reality Chris reveals that LLMs represent only 30% of AI analytics products, with 70% being sophisticated tooling, error correction loops, and multi-agent systems. Raw LLM output succeeds only one-third of the time without extensive supporting infrastructure.
3. Code-First AI Architecture Plotly's approach generates Python code rather than having AI directly process data, creating more rigorous analytics. The system generates 2,000-5,000 lines of code in under two minutes through parallel processing while maintaining 90%+ accuracy.
4. Natural Language as Universal Equalizer Discussion of how natural language interfaces eliminate the learning curves of different analytics tools (Salesforce, Tableau, Google Analytics), potentially democratizing data visualization across organizations by providing a common interface.
5. Vibe Analysis Concept Introduction of "vibe analysis" - the data equivalent of "vibe coding" - enabling fluid, rapid data exploration that keeps analysts in flow states through natural language interactions with AI-powered tools.
6. Transparency and Trust Building Exploration of building user trust through auto-generated specifications in natural language, transparent logging interfaces, and making underlying code assumptions visible and adjustable to prevent misleading results.
7. Human-AI Collaboration Balance Chris emphasizes that while AI accelerates visualization creation and data exploration, human interpretation remains essential for generating insights. The risk lies in systems that attempt to "skip to the finish" with fully automated decision-making.
8. Infrastructure Misconceptions Domenic predicts people will wrongly assume AI analytics requires extensive data warehouses and semantic layers, when effective analysis can work with standard databases and file formats, making advanced analytics more accessible than many realize.

In this episode, we had the privilege of speaking with Nikhil Srinidhi from Rewire.

Nikhil helps large organizations tackle complex business challenges by building high-performing teams focused on data, AI, and technology. With practical experience in data and software engineering, he drives impactful and lasting change. Before joining Rewire in 2024, Nikhil spent over six years at McKinsey and QuantumBlack, where he led holistic data and AI initiatives, particularly for clients in life sciences and healthcare. Earlier in his career, he worked as a data engineer in Canada, specializing in financial services. Nikhil holds a degree in Electrical Engineering and Economics from McGill University in Montreal, Canada.

In this episode, we had the privilege of speaking with Walid Mehanna, Chief Data and AI Officer at Merck Group. Walid shares deep insights into how large, complex organizations can scale data and AI and create lasting impact through thoughtful leadership.

As Chief Data & AI Officer of Merck Group, Walid led the Merck Data & AI Organization, delivering strategy, value, architecture, governance, engineering, and operations across the whole company globally. Hand in hand with Merck’s business sectors and their data offices, we harnessed the power of Data & AI. Walid is glad to be part of Merck as another curious mind dedicated to human progress.

In our latest episode of the Data Science Conversations Podcast, we spoke with Christoph Sporleder, Managing Partner at Rewire, about the evolving role of consulting in the data and AI space.

This conversation is a must listen for anyone dealing with the challenges of integrating AI into business processes or considering an AI project with an external consulting firm. Christoph draws from decades of experience, offering practical advice and actionable insights for organizations and practitioners alike.

Key Topics Discussed

1. Evolution of Data and Cloud Computing

The shift from local computing to cloud technologies, enabling broader data integration and advanced analytics, with the rise of IoT and machine data.

2. Data Management Challenges

Discussion on the evolution from data warehouses to data lakes and the emerging concept of data mesh for better governance and scalability.

3. Importance of Strategy in AI

Why a clear strategy is crucial for AI adoption, including aligning organizational leadership and identifying impactful use cases.

4. Sectoral Adoption of Data and AI

Differences in adoption across sectors, with early adopters in finance and insurance versus later adoption in manufacturing and infrastructure.

5. Consulting Models and Engagement

Insights into consulting engagement types, including strategy consulting, system integration, and body leasing, and their respective challenges and benefits.

6. Challenges in AI Implementation

Common pitfalls in AI projects, such as misalignment with business goals, inadequate infrastructure planning, and siloed lighthouse initiatives.

7. Leadership’s Role in AI Success

The critical need for senior leadership commitment to drive AI adoption, ensure process integration, and manage organizational change.

8. Effective Collaboration with Consultants

Best practices for successful partnerships with consultants, including aligning on objectives, managing personnel transitions, and setting clear engagement expectations.

9. Future Trends in Data and AI

Emerging trends like componentized AI architectures, Gen AI integration, and the growing focus on embedding AI within business processes.

10. Tips for Managing Long-Term Projects

Strategies for handling staff rotations and maintaining project continuity in consulting engagements, emphasizing planning and communication.

KP Reddy, founder and managing partner of Shadow Ventures, explains how AI is set to redefine the startup landscape and the venture capital model. KP shares his unique perspective on the rapidly evolving role of AI in entrepreneurship, offering insights into:

• GENAI adoption in large companies is still limited
• How AI is empowering leaner, more efficient startups
• The potential for AI to disrupt traditional venture capital strategies
• The emergence of new business models driven by AI capabilities
• Real-world applications of AI in industries like construction, life sciences, and professional services

Early Interest in Generative AI

• Martin's initial exposure to Generative AI in 2016 through a conference talk in Milano, Italy, and his early work with Generative Adversarial Networks (GANs).

Development of GANs and Early Language Models since 2016

• The evolution of Generative AI from visual content generation to text generation with models like Google's Bard and the increasing popularity of GANs in 2018.

Launch of GenerativeAI.net and Online Course

• Martin's creation of GenerativeAI.net and an online course, which gained traction after being promoted on platforms like Reddit and Hacker News.

Defining Generative AI

• Martin’s explanation of Generative AI as a technology focused on generating content, contrasting it with Discriminative AI, which focuses on classification and selection.

Evolution of GenAI Technologies

• The shift from LSTM models to Transformer models, highlighting key developments like the "Attention Is All You Need" paper and the impact of Transformer architecture on language models.

Impact of Computing Power on GenAI

• The role of increasing computing power and larger datasets in improving the capabilities of Generative AI

Generative AI in Business Applications

• Martin’s insights into the real-world applications of GenAI, including customer service automation, marketing, and software development.

Retrieval Augmented Generation (RAG) Architecture

• The use of RAG architecture in enterprise AI applications, where documents are chunked and queried to provide accurate and relevant responses using large language models.

Technological Drivers of GenAI

• The advancements in chip design, including Nvidia’s focus on GPU improvements and the emergence of new processing unit architectures like the LPU.

Small vs. Large Language Models

• A comparison between small and large language models, discussing their relative efficiency, cost, and performance, especially in specific use cases.

Challenges in Implementing GenAI Systems

• Common challenges faced in deploying GenAI systems, including the costs associated with training and fine-tuning large language models and the importance of clean data.

Measuring GenAI Performance

• Martin’s explanation of the complexities in measuring the performance of GenAI systems, including the use of the Hallucination Leaderboard for evaluating language models.

Emerging Trends in GenAI

• Discussion of future trends such as the rise of multi-agent frameworks, the potential for AI-driven humanoid robots, and the path towards Artificial General Intelligence (AGI).