Future Of Software Creation: Scaling Review

Hi {{ first name | there }},

Happy release day! Today we dive into the future of software development in a world of vibe coding. Tune in here 🎧

I sit down with Merrill Lutsky, CEO and co-founder of Graphite — a New York startup bringing AI-acceleration and automation to code review. Founded in 2020 and funded to the tune of $70M, Graphite has become a key part of the developer ecosystem with over 45,000 developers on its platform.

While the rest of the industry chases code generation with copilots and vibe coding, Graphite is focused on what happens after the pull request is created. As more code is generated with AI, they enable developers to scale the evaluation, testing, and review process before it is released. It points to a growing bottleneck that has become incredibly important in a world where the majority of code is generated by AI.

What role do developers play in an era of vibe coding? How can we scale ourselves to keep up with the pace of code being generated to ensure its quality and impact? We dive into these and more.

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My 5 Year Outlook:

• Agent-Led Code Generation By Default

  We know its coming; the move from AI-assisted coding to AI-led.

• The Multi-Disciplinary Developer

  The shift to higher order tasks has developers owning more of the outcomes.

• Adaptive Software Interfaces

  UI and UX dynamically personalized to each user.

Curious? Read on as I unpack each below 👇🏼

Agent-Led Code Generation By Default

Just as GitHub Copilot made autocomplete table stakes, the next wave is full agentic development: software that builds itself based on high-level specs, product requirements, or even natural language prompts.

The runaway adoption of vibe coding tools like Cursor, Windsurf, and Lovable is accelerating this shift. These tools don’t just help write code—they capture workflows, metadata, and developer behavior, which improve the performance of foundation models like Claude and GPT-4o. It’s a compounding feedback loop: more usage leads to better models, which leads to more agent capabilities.

And it’s not limited to autocomplete. A new generation of agents is targeting more complex engineering workflows. Tools like Devin—the autonomous software engineer from Cognition Labs—are now capable of handling multi-layered tasks: building full-stack apps, configuring CI/CD pipelines, resolving bugs across unknown codebases, and managing entire repositories from prompt to production.

Devin has demonstrated the ability to operate independently across multiple sessions, scoping, building, testing, and shipping usable code—all without human assistance. It’s not just producing snippets; it’s executing entire projects. That shows how close we are to handing off higher-order engineering work to AI agents.

Further up the frontier, Poolside AI (featured on 5YF #20) is going one step further: building its own foundation model, trained entirely on code. Unlike Devin, which relies on general-purpose models, Poolside is betting on verticalization—designing a model from the ground up with coding as the native language. The goal? Go beyond assistance and eliminate the developer loop entirely: request in, deployable software out. CEO Jason Warner argues the future will be owned by the foundation models, not the apps built on top.

Agent-led code generation is no longer theoretical. The speed, efficiency, and iteration cycles enabled by AI already point to a world where models drive most software creation. As inference costs drop and capabilities rise, the default dev environment may look less like an IDE—and more like managing an autonomous software factory.

But as generation accelerates, the bottleneck shifts. Reviewing, validating, and deploying code becomes the real challenge. That’s where tools like Graphite’s Diamond come in—an agent that automates pull request review and is already used by startups to merge code without human intervention. Programmatic refactoring, batch upgrades, and GitHub-native agent workflows are becoming the norm. In this new era, developers won’t just write code—they’ll review, direct, and orchestrate AI output at scale.

5YF #38: The 100X Engineer, Vibe Coding Bottlenecks, Agents As Evaluators, AI Roll-ups, And The Future Of Software Creation w/ Graphite CEO Merrill Lutsky

The Multi-Disciplinary Developer

In a world of AI-generated code, the role of the software developer shifts from creator to conductor. The edge will no longer go to the fastest coder—but to the engineer who can manage agents, review intelligently, and bring in architecture, design, and cross-functional thinking.

In this world, coding is not the bottleneck — judgment is. Developers will increasingly own product specs, user experience, AI prompting, and domain-specific decisions. A feature doesn’t start with lines of code, it starts with defining what should be built — and the best developers will be those who understand the user’s needs, data flows, security concerns, and business model impact.

The 10X engineer of the future may be the one who knows how to scope the right feature, not write the code themselves. One that appreciates the intent and overall outcome more than any model can.

We’ve seen this shift before in other industries. Designers who can write frontend code tend to ship better, tighter user experiences. Marketers who understand data and run SQL queries unlock better targeting and insights. In a world where AI is taking over execution, the differentiator becomes synthesis—blending skills across product, design, data, and domain expertise. The best developers in five years may not look like engineers today—they may be full-stack thinkers with a toolkit that spans across AI orchestration, interface logic, user research, and strategic execution.

Adaptive Software Interfaces

As more of the software lifecycle gets automated, what unlocks is a radically different end-user experience. UI and UX can become personalized, dynamic, and responsive to the user’s context — even adjusting features on the fly.

We already see this type of personalization in media, social, and content platforms. Netflix, YouTube, and TikTok all adapt what you see based on tight feedback loops of engagement. But the interface—the actual software design—remains static. That’s starting to change.

Imagine onboarding flows that adapt to your experience level, enterprise dashboards that prioritize features based on usage patterns, or creative tools like Figma that present different UI for power users vs. beginners. AI-native interfaces could shift layout, complexity, and control surfaces based on your goals, behaviors, or even role.

Industries like healthcare, finance, and B2B SaaS are moving in this direction with adaptive dashboards, modular interfaces, and embedded AI copilots. Companies like Salesforce (Einstein), Hubspot, and Notion are evolving their products into systems that learn from users and respond dynamically—blurring the line between static UI and conversational interface.

In five years, software won’t just react to user input—it will proactively reshape itself to fit the user.

The boundary between human intention and machine execution is blurring— in the future developers won’t just write software, they’ll direct living systems that learn, adapt, and build alongside them. The future of creation belongs to those who embrace AI not as a tool, but as a collaborator in shaping what comes next.

Collaborate and create.

Future Of Clean Energy: CO2 To Biofuel

Hi {{ first name | there }},

Happy release day! Today we dive into the future of carbon capture and sustainable energy. Tune in here 🎧

I sit down with Greg Constantine, CEO and co-founder of AIRCO—a company turning carbon dioxide from a problem into a power source. Based in New York, AIRCO has raised over $100 million to propel its ambition: rewire the way we think about carbon, not as waste, but as a raw input for jet fuel, rocket fuel, and even consumer-grade ethanol.

The innovation ushers in a future where carbon emissions are transformed to be sustainable aviation fuel (SAF), used to create the energy infrastructure on Mars, and introduce modular carbon repurposing stations to decentralize energy for the military.

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My 5 Year Outlook:

• We’ll Make Rocket Fuel on Mars

  Circular and localized energy production for interplanetary colonies and travel.

• Fuel Production Will Go Modular and Tactical

  Biofuel stations on aircraft carrier, military bases, and remote islands.

• Carbon Will Become a Platform for New Products

  Why settle for just capturing CO2 when you can create key fuel and ethanol inputs.

Curious? Read on as I unpack each below 👇🏼

We’ll Make Rocket Fuel on Mars

AIRCO’s collaboration with NASA explores the bold idea of producing fuel for interplanetary missions using CO2 captured directly from the Martian air and hydrogen produced via electrolysis from subsurface water or ice.

This concept, known as in-situ resource utilization (ISRU), represents a fundamental shift in how we think about long-duration space travel. Instead of carrying all the fuel from Earth—a massively expensive and mass-limited proposition—future space missions could generate fuel on the planet they intend to leave. If you can produce fuel on Mars, you suddenly open the door to round-trip missions and permanent habitation.

NASA has been actively funding this work through its Small Business Technology Transfer (STTR) program, signaling serious institutional belief in its feasibility. AIRCO is developing reactors small enough to be deployed in spacecraft cargo and robust enough to operate autonomously on another planet. This mirrors a wider trend in aerospace: SpaceX, Blue Origin, and others are exploring how off-world resource utilization could create permanent infrastructure for exploration and habitation. It’s a vision where fuel is not just carried, but created beyond the edge of civilization.

5YF #37: Global Energy Race, Creating With Carbon, CO2 As Fuel, Powering Mars, And The Future Of Clean Energy w/ AIRCO CEO Greg Constantine

Fuel Production Will Go Modular and Tactical

AIRCO’s work with the U.S. Department of Defense is driven by a sobering statistic: over 70% of U.S. military casualties in conflicts like Afghanistan were related to fuel transport.

To that end, AIRCO is building modular, container-sized systems that can generate synthetic fuel on-site using just carbon dioxide, water, and electricity. The company’s earliest systems were small enough to fit on a flatbed truck, and today the goal is complete portability: refineries that can be shipped to remote islands, military bases, or disaster zones and switched on like software.

AIRCO’s fully formulated sustainable aviation fuel (SAF), already tested with the U.S. Air Force, could become the cornerstone of flexible, carbon-based logistics networks around the world. This model of decentralized fuel production has implications far beyond defense. As nations and corporations look to secure energy independence, modular reactors like AIRCO’s could serve as on-demand energy assets, disconnected from global supply chains and pipelines.

The modular innovation marks a broader movement in climate and energy tech—from Bloom Energy’s solid oxide fuel cells to companies like Oklo and Radiant building small-scale nuclear reactors for military and industrial use. .

Carbon Will Become a Platform for New Products

While many carbon capture companies are focused on burying CO2 underground, AIRCO is focused on using it. Their approach is to use CO2 as a feedstock to create entirely new categories of products—from fuels to consumer-grade ethanol used in fragrances, spirits, and industrial solvents.

This shift from carbon storage to carbon utilization could redefine how we think about emissions. Instead of being a cost center, CO2 becomes an input into the supply chain—a feedstock for products that already exist and entirely new ones still to be imagined. For AIRCO, that has meant monetizing their R&D in creative ways, such as producing vodka and hand sanitizer with early prototypes while scaling toward industrial markets.

AIRCO is not alone. Companies like Twelve, LanzaTech, and Carbon Upcycling are all pioneering the idea that CO2 can be used as a platform to create everything from plastics to building materials. As green hydrogen becomes more available and AI accelerates material discovery, this approach could unlock entire new industries. It’s a future where emissions are no longer buried, but transformed—where carbon becomes the backbone of a new industrial era.

Time to power up!

Future Of Selling: AI Revenue Intelligence

Hi {{ first name | there }},

Happy release day! Today we dive into the future of enterprise sales. Tune in here 🎧

AI isn’t just streamlining the sales process — it’s reshaping who sells, how they sell, and when selling even begins.

In this episode of The 5 Year Frontier, I spoke with Andy Byrne, CEO of Clari, a platform managing over $4 trillion in pipeline for the world’s largest companies. We covered the collapse of manual sales ops, the rise of procurement platforms that match buyers and sellers before contact is made, and how the CRO of the future will orchestrate revenue with a predictive, AI-powered command center.

Take a glimpse into how enterprise sales will work in 2030.

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My 5 Year Outlook:

• AI Co-Pilots Will Bring the Human Back to Enterprise Sales

  Bringing listening, problem solving, and human connection back into the fold.

• Procurement As A Matchmaking Layer

  From AI agents to private procurement marketplaces, no sale will start cold.

• Revenue Will Flow Will Be Holistic Data System

  Money flowing like data and orchestrated by the CRO.

Curious? Read on as I unpack each below 👇🏼

AI Co-Pilots Will Bring the Human Back to Enterprise Sales

In a world of noisy outbound and overloaded CRMs, the best sales reps will stand out not for their admin skills, but their human touch. As selling becomes more complex, the best reps won’t be the ones who master data entry — but those who double down on the human connection. AI co-pilots will quietly handle CRM updates, pipeline tagging, and deal-stage forecasting in the background, freeing sellers to focus entirely on the buyer relationship.

Clari’s early customers have already cut down manual workflows by automating deal inspection and forecast hygiene, letting reps reclaim time to actually sell. One Fortune 500 customer used AI to flag stale deals in real-time, then automatically route coaching to reps — resulting in a 28% increase in pipeline conversion.

Gartner predicts that by 2026, 65% of B2B sales organizations will transition from intuition-based to data-driven selling, powered by sales tech like Clari’s co-pilots. But ironically, that shift will re-humanize the sales motion, not strip it down.

5YF #36: Sales Co-Pilots, AI Revenue Intelligence, Procurement Agents, CRO in 2030, and The Future of Selling w/ Clari CEO Andy Byrne

Procurement As A Matchmaking Layer

In the near future, sales cycles won’t begin with a cold email — they’ll begin with mutual intent.

Buyers will proactively opt into platforms like Clari Align or vertical-specific marketplaces, privately registering their tech stack, desired capabilities, KPIs, and timelines — not for public broadcast, but to enable intelligent vendor matching. At the same time, vendors tag their solutions with architecture compatibility, core functionality, and pricing thresholds. Should marketplaces be seen as too direct then AI agents can build such understanding in real time and use their predictive matching as a way to pre-qualify conversations.

Basic alignment — such as “Do we integrate with your ERP?” or “Can we help you hit this retention KPI?” — is handled upfront. From there, AI agents surface qualified matches, vet timing, budget fit, and compliance alignment. What emerges isn’t a pipeline of cold leads — it’s a curated stream of ready-to-engage meetings where both sides already agree on the problem space.

Imagine: instead of reps wasting cycles on misaligned accounts, they walk into buyer conversations focused on co-designing solutions and long-term partnerships. This flips the funnel — from chase-and-convince to match-and-collaborate.

Revenue Will Flow Will Be Holistic Data System

The modern CRO is evolving from pipeline overseer to AI-powered strategist. In the next five years, revenue leadership will move from spreadsheets and dashboards to dynamic, self-optimizing systems that surface risk and opportunity in real time.

Clari’s 1,500 customers are no longer thinking about sales, CS, and marketing in silos. The CRO of the future will command a full-spectrum view of every revenue stream, cadence, and risk point. Today’s CRO might run a forecast meeting with lagging indicators. But tomorrow’s CRO will run a revenue control room — powered by predictive models that synthesize data from emails, calls, pipeline shifts, and buyer behavior. They’ll ask: Which regions are slipping? Which deals are stalling? Where should our top reps focus next week? And the system will answer.

Clari, for example, already processes over 10 million closed opportunities to surface insights about win patterns and conversion triggers. In five years, this kind of data exhaust won’t just inform strategy — it will run the system. The CRO becomes the conductor of an instrumented, AI-powered revenue engine.

The future of enterprise revenue isn’t just faster — it’s smarter, more connected, and built around alignment from the first touch to final close.

Let the money flow!

Future Of Employees: Agent Workforce

Hi {{ first name | there }},

Happy release day! Today we dive into the future of AI employees. Tune in here 🎧

The rise of AI agents in the enterprise isn’t just about streamlining workflows — it’s about rewiring how companies operate from the inside out. In this episode of the 5 Year Frontier, I sat down with Surojit Chatterjee, CEO of Ema, to explore a future where AI employees work alongside humans, collaborate with each other in real time, and increasingly take on the day-to-day tasks that once required entire departments.

Surojit brings one of the most accomplished product backgrounds in tech. He served as Chief Product Officer at Coinbase, led product at Flipkart to scale India’s e-commerce giant, and spent nearly a decade at Google leading product for mobile ads, commerce, and enterprise tools. Now, at Ema, he’s building a new category: universal AI employees — fully agentic systems that can be trained, configured, and deployed to operate across sales, HR, legal, support, and beyond. The goal? Not just to assist humans — but to meaningfully amplify what organizations are capable of.

The conversation surfaced three transformative trends that will define how we work in collaboration with AI over the next five years:

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My 5 Year Outlook:

• Agents introduce the 24/7/365 workday

  Company output will skyrocket with always on agents.

• Tokenized micropayments power the agent economy

  High-frequency agent interactions will require a new layer of low-cost, automated transactions.

• Developing economies will accelerate not be threatened by AI workforce

  AI talent will fill critical skill gaps in healthcare, education, and services — unlocking leapfrog growth.

Curious? Read on as I unpack each below 👇🏼

Agents introduce the 24/7/365 workday

As every function in the enterprise begins to be supported by AI employees, we’re moving toward a world where departments continue working long after humans have clocked off. These agents aren’t bound by office hours, energy levels, or the limits of human attention. They reason, act, and collaborate in real time — creating a foundation for organizations that run continuously, 24 hours a day, 365 days a year.

In this model, the organization begins to resemble an iceberg: the visible tip is human output, but beneath the surface lies an enormous agentic workforce doing the heavy lifting. To quantify this shift: a typical employee works around 2,000 hours per year — but an AI agent running continuously can deliver 8,760 hours annually, a 4.4x uplift in potential output simply by being always on. Now multiply that across an entire company — and then across the global economy — and the productivity gains become staggering.

And when those agents begin collaborating across companies, executing tasks between systems in real time, we won’t just see more output — we’ll need to build new infrastructure for how they coordinate, transact, and exchange value.

5YF #35: AI Employees, Managing Agent Workforces, Crypto Micropayments, End Of SaaS, Rewiring Developing Nations and The Future of Agents w/ Ema CEO Surojit Chatterjee

Tokenized micropayments power the agent economy

When agents start collaborating across company lines — negotiating, sourcing, executing tasks — they’ll need the autonomy to move money. While large transactions will continue to require human approval chains what is more interesting is the rise of potential micro-payments between agents.

Traditional invoicing and approvals won’t cut it in a machine-speed and high-volume agentic world. Instead, we’ll see the rise of tokenized micropayments: a new economic infrastructure purpose-built for AI. These could be the sourcing of a 3rd party data source or the run of an external agent to complete the objective of your own AI employee.

This requires programmable, traceable, low-cost payment rails — and likely, a convergence between AI systems and crypto primitives like stablecoins or blockchain-based clearing.

One early signal: OpenAgents, an open-source project from AutoGPT contributors, has begun integrating crypto wallets into autonomous agent workflows. Imagine an agent coordinating a marketing campaign and paying a third-party AI designer for assets — all autonomously, in fractions of a cent. This isn’t theoretical — it’s already being prototyped.

As the economy of bots emerges, we must ask: who benefits the most from intelligent labor that’s affordable, scalable, and instantly deployable?

Developing economies will accelerate not be threatened by AI workforce

In many developing nations, the fear isn’t over-automation or losing offshore jobs to AI — it’s the persistent shortage of skilled labor needed to drive economic growth. From doctors and teachers to civil servants and administrators, the demand for quality services dramatically outstrips supply. AI employees offer a fast-track solution, giving these countries a chance to leapfrog traditional development pathways — not by replacing humans, but by filling critical gaps while the human workforce scales.

Surojit points to examples like a rural clinic gaining an AI medical assistant or a remote village student learning from a personalized AI tutor. These agents don’t replace people — they buy time, build capacity, and expand access. We’ve seen this story before: mobile phones leapfrogged landline infrastructure and unlocked new economic activity in the developing world. Now, the same pattern is playing out in labor. Startups across Latin America and Africa are already deploying AI-powered legal assistants, call centers, and virtual teachers. One Nigerian edtech company even launched a generative AI tutor capable of coaching students in native dialects — with no onboarding required.

And that brings us to the big picture. AI employees aren’t here to sit quietly in the corner — they’re here to work. As Surojit makes clear, they’ll operate across time zones, payment rails, and economic systems. The question is no longer whether enterprises will adopt agents — it’s whether they’re prepared for what happens when the agents start running the show.

Time to work.

Future Of Work: Context And Memory

Hi {{ first name | there }},

Happy release day! Today we dive into the future of AI in the enterprise. Tune in here 🎧

I sat down with Aman Singh, CTO and Co-founder of Contextual AI, to explore how enterprise AI is evolving from demos to production-grade systems. Aman’s insights reveal a future where language models are orchestrators, enterprise systems are safeguarded against hallucination, and the enterprise AI ecosystem diverges from its consumer counterpart.

The conversation surfaced three transformative trends that will define enterprise AI over the next five years:

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My 5 Year Outlook:

• LLMs Are The Universal Interface For Orchestrating Specialized Models And Agents

  Humans use language to communicate making LLMs our natural first engagement layer for AI.

• Grounded Language Models (GLMs) and RAG Will Eliminate AI Hallucinations in the Enterprise

  The enterprise won’t accept misguided answers, luckily new technology is grounded AI replies in their data.

• Consumer AI Consolidates Vs Enterprise AI Proliferates

  Enterprise AI is going down a very different path to consumer.

Curious? Read on as I unpack each below 👇🏼

LLMs Are The Universal Interface For Orchestrating Specialized Models And Agents

Large Language Models (LLMs) are evolving into the universal interface through which humans interact with complex AI systems. Because natural language is the most intuitive, accessible medium for human interaction, LLMs are perfectly positioned to become the primary UX layer — enabling users to instruct, query, and coordinate a diverse array of specialized models and agents.

In this vision, LLMs are not just processing language; they are interpreting human intent and delegating tasks to other systems, including retrieval agents, world models, and domain-specific AIs. As Aman Singh explained, the power of LLMs lies in their ability to act as “the planner” — understanding user requests, breaking them down into sub-tasks, and routing those tasks to the right agents.

Companies like Adept are already building agent ecosystems that respond to natural language instructions. Meanwhile, Contextual AI is developing systems where LLMs act as orchestration layers, retrieving contextual data, coordinating agents, and optimizing performance through feedback loops. This paradigm is especially critical for enterprise environments where context, accuracy, and specialization are essential.

The future enterprise stack will increasingly rely on LLMs to serve as the connective tissue between human users and a diverse array of AI tools. Whether it’s accessing a specialized world model, deploying a retrieval system, or orchestrating a workflow across multiple agents, the LLM will be the central interface managing it all.

In short, LLMs are becoming the conductor of the AI orchestra — where agents and models are the instruments, and language is the baton.

5YF #34: Agents In The Enterprise, Cloning Experts, Organizational Memory, Grounded Language Models, Thin AI Orgs, and The Future of Work w/ Contextual AI CTO Aman Singh

Grounded Language Models (GLMs) and RAG Will Eliminate AI Hallucinations in the Enterprise

One of the biggest pain points for enterprises deploying AI is the problem of hallucinations. In consumer AI, minor inaccuracies are acceptable; a chatbot that suggests the wrong movie or provides a slightly incorrect fact can be forgiven. But for enterprises making financial decisions, legal evaluations, or critical diagnostics, mistakes are catastrophic.

Contextual AI’s approach is radically different from generic LLMs like GPT-4. By implementing Grounded Language Models (GLMs) — systems that only respond when the necessary context is available — Aman’s team is creating AI that understands its own limitations. The RAG 2.0 framework provides a retrieval system that is not only context-aware but continually optimized through feedback loops.

The value of these safeguards is already evident in Contextual’s work with Qualcomm, where their models are tuned to respect privacy rules, document attribution, and domain-specific expertise. Aman explained that this architecture doesn’t just prevent hallucinations; it offers a clear audit trail, allowing enterprises to trace an answer back to its source.

Going one step further is then prioritizing the top expertise in the organization so that their knowledge, often tribal in nature, is surfaced ahead of others. For Contextual AI this is their re-ranker system but it can be seen as a wider movement across the enterprise as they model their data to better represent their organizational structure.

Consumer AI Consolidates Vs Enterprise AI Proliferates

What is becoming clear is that AI in the enterprise can no longer be viewed via the same lens as consumer-facing AI like Chat-GPT. Over the coming years there is a divergent path that points to consumer AI being increasingly consolidated amongst the dominant players who have distribution, data access, compute, and brand: OpenAI, Google, LLaMa. As all of the internet data corpus is leveraged data doesn’t become the clear differentiator rather product and distribution is.

But in the enterprise, the opposite is happening. Each company has unique processes, proprietary data, and specialized needs. According to Aman, Contextual AI is building custom agents designed to work within these specific contexts, allowing enterprises to deploy AI models that fit their workflows, rather than forcing their workflows to fit a general-purpose model.

The proliferation of verticalized AI solutions is already visible. Companies like Harvey are building LLMs specifically for legal workflows, while others like Glean AI are developing accounting-specific agents. This specialization will continue to drive fragmentation in the enterprise AI landscape.

The consequence? While consumer AI consolidates under a few massive brands, enterprise AI will diversify into a rich ecosystem of tailored, purpose-built solutions designed for specific industries and companies.

Overall what surprises me most is how practical and near-term these changes are. We’re not talking about far-off AGI scenarios — we’re seeing concrete shifts in how enterprises design their AI systems today. Those that embrace specialization, context, and precision will emerge as the leaders of this new era.

Time to shine!

Future Of Autonomy: Modeling The Real World

Hi {{ first name | there }},

Happy release day! Today we dive into the future of autonomous cars, robots and systems. Tune in here 🎧

Autonomous driving has long been stuck in the “just around the corner” phase, but recent breakthroughs in AI-driven simulation, synthetic data, and real-world modeling are accelerating the industry at an unprecedented pace. Unlike traditional approaches that rely on massive fleets and brute-force data collection, Helm.ai is pioneering a more scalable way forward—one that could allow automakers to leapfrog Tesla in self-driving capabilities.

In our latest episode of 5 Year Frontier, I sat down with Vlad Voroninski, CEO of Helm.ai, to discuss the state of AI autonomy, how simulated environments are training real-world AI, and what’s coming next.

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My 5 Year Outlook:

• Synthetic Data Grows To Be The New Standard for Training AI

  Endless variation and edge cases built around small pools or real world data.

• World Models, Not LLM’s, Bring AI Into The Physical World

  An understanding of the world is what is needed for AI to leave its box.

• Cost Becomes The Last Hurdle To Self Driving Cars

  The technology is there, regulation is slowly moving, but business models remain to be developed.

Curious? Read on as I unpack each below 👇🏼

Synthetic Data Grows To Be The New Standard for Training AI

Traditionally, the training process for developing autonomous vehicles required collecting and annotating vast amounts of real-world driving data—a process that is both time-consuming and costly. Companies such as Tesla and Waymo collect sensor and camera data from each vehicle in their growing fleets. They have developed a clear lead over the automotive market due to the amount of data that can be used to train their self-driving systems. That lead, previously viewed as insurmountable, may be challenged over the coming years as simulation and synthetic data takes center stage for AI training.

Relying only on fleet captured data has diminishing returns. Capturing rare “edge cases,” such as unusual obstacles or extreme weather conditions, is challenging yet crucial for ensuring vehicle safety. Especially safety that passes the scrutiny of regulators.

To address these issues, companies are increasingly turning to synthetic data—artificially generated datasets that multiply the depth and dimensionality of data assets collected from cameras, sensors, and activities in the real world. Along with running simulations via gaming engines to deliver orders of magnitude more training runs to AI.

Helm.ai exemplifies this shift by utilizing AI-driven simulations to create synthetic datasets, effectively reducing the dependency on extensive real-world data collection. This approach not only accelerates the training process but also enhances the model’s ability to handle rare or hazardous scenarios that are difficult to capture in real life. Similarly, Applied Intuition employs log-based synthetic simulation to rigorously validate AV systems using real-world data within controlled environments, enhancing the reliability and safety of these technologies before deployment.

The advantages of synthetic data extend beyond autonomous vehicles. In mining and industrial robotics, synthetic datasets enable the simulation of hazardous conditions, allowing AI systems to learn appropriate responses without endangering human workers. This capability is particularly valuable in training robots to operate in unpredictable environments, thereby enhancing both safety and operational efficiency.

5YF #33: Autonomous Cars And Robots, Leapfrogging Tesla, Real World Models, AI Training Simulators, and the Future of Autonomy w/ Helm.ai CEO Vlad Voroninski

World Models, Not LLM’s, Bring AI Into The Physical World

For all the breakthroughs Large Language Models (LLMs) have delivered—from ChatGPT to AI-generated code—they lack an intuitive understanding of the real world. AI today can generate essays, but it struggles to anticipate how a pedestrian might behave at a busy intersection. This limitation is why Yann LeCun, Chief AI Scientist at Meta, believes the future of AI autonomy depends on “world models”—systems that don’t just process data but predict, simulate, and interact with the physical world.

Just as babies learn to navigate their environment before they learn to speak, world models allow AI to build an intuitive grasp of space, motion, and cause-and-effect, without relying on massive labeled datasets.

While world models are not yet in production, Helm.ai’s work points to a future where they could play a complementary role in AI autonomy. Helm.ai’s approach to self-driving AI is already built on the idea that real-world data collection is not the only way to train AI systems. By leveraging synthetic data and unsupervised learning, Helm.ai has found ways to train self-driving models without the massive fleet-based data collection approach used by Tesla. This mirrors the core promise of world models: reducing AI’s dependence on pre-programmed rules and real-world datasets by allowing it to generalize and adapt to new environments.

As world models continue to evolve, their impact could extend far beyond self-driving cars. Autonomous robots, industrial automation, and even AI-powered scientific research stand to benefit from AI that can predict and interact with the physical world, rather than just responding to data. Companies like Wayve and NVIDIA are already investing in AI architectures that allow autonomous systems to learn from experience, rather than rigid mapping. In the long term, world models could help bridge the gap between digital intelligence and real-world reasoning, enabling AI to function safely and efficiently in open-ended environments—from city streets to factory floors and beyond. If self-driving cars represent the first real test case, then world models could be the foundation for the next era of AI-driven autonomy.

Cost Becomes The Last Hurdle To Self Driving Cars

The biggest challenge standing between today’s autonomous vehicle technology and full Level 4 (L4) deployment isn’t capability—it’s cost. Self-driving systems have already proven they can handle complex environments, but scaling them to a point where they are commercially viable remains elusive. L4 autonomy works in limited deployments, but making it economically feasible for widespread adoption is the last major hurdle.

As Helm.ai CEO Vlad Voroninski explains, achieving full autonomy comes down to a trade-off between safety and cost. Adding more sensors, compute power, and redundancy layers can increase an autonomous vehicle’s safety, but at an exponentially higher price point. The challenge is finding the balance between cost-effective AI models and systems that meet safety thresholds acceptable to regulators. This is why most L4 deployments today are limited to controlled environments like robo-taxis in select cities or autonomous trucks on highways with fewer unpredictable variables. Expanding beyond these use cases requires significantly lowering the cost per vehicle.

Regulation also plays a major role in determining how much cost is necessary. If self-driving cars only need to be as safe as human drivers, then companies can deploy solutions sooner with existing technology. However, if regulators demand AVs to be 100 times safer than human drivers, it would require a massive increase in sensor and compute costs, pushing L4 autonomy even further out of reach. Without clear regulatory standards, companies must over-engineer for safety, further driving up costs. The technology is ready—but until cost barriers are lowered and a clear path to regulatory approval emerges, the road to L4 autonomy will remain blocked.

The convergence of synthetic data generation and world modeling is accelerating the development of autonomous vehicles. By enabling AI systems to learn from a vast array of simulated scenarios, these technologies address critical challenges such as data scarcity and the need to cover edge cases. As these advancements continue, we move closer to a future where autonomous vehicles operate safely and efficiently in complex real-world environments.

Autonomy isn’t just about better AI—it’s about making intelligence scalable, adaptable, and ready for the real world. The companies that solve this will define the future of transportation, robotics, and beyond.

Let’s get moving!

Future Of Data: Unlocking The Hidden 90%

Hi {{ first name | there }},

Happy release day! Today we dive into the future of turning data into value, at scale. Tune in here 🎧

Data infrastructure isn’t just about hardware—it’s about the intelligence built on top of it. Over the next five years, we will see a radical transformation in how data is processed, stored, and utilized, driven by AI, compute efficiency, and new architectures. But while the conversation often centers on hardware—GPUs, data centers, and even the $500 billion Stargate project—huge breakthroughs are happening already in the software and systems that sit on top of that infrastructure.

In my conversation with Craig Dunham, CEO of Voltron Data, we explored the hidden inefficiencies in today’s data systems and what companies are doing to unlock new levels of performance. From untapped petabytes of enterprise data to the rise of energy-efficient compute, we discuss the future of data infrastructure and how it will redefine business, AI, and the global economy.

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My 5 Year Outlook:

• Gains Delivered By The Invisible Processing Layer

  Step change in compute output and data processing thanks to advances in the ‘invisible’ processing layer.

• AI Models Will Be Hungry For Smarter Compute

  Software optimizations down to the bare metal of GPUs and data centers to unlock smarter compute, not just more compute.

• Agentic Insights Opens Door To Fully Autonomous Decision-Making

  The world will move from managing AI to trusting AI.

Curious? Read on as I unpack each below 👇🏼

Gains Delivered By The Invisible Processing Layer

For decades, companies have built massive compute infrastructures, yet 90% of enterprise data remains untapped. The problem isn’t storage—it’s processing bottlenecks that make using this data too slow, too expensive, and too fragmented.

The next five years will bring the rise of “invisible processing”—highly optimized, automated data pipelines that seamlessly process and serve insights in real time, without human intervention. Voltron Data is already proving this with claims that its customers can reduce their server usage from 200 to 2, cutting costs by orders of magnitude. Theseus, its GPU-accelerated query engine, is an early glimpse of this shift, proving that data processing can be done 100x faster and at a fraction of the cost.

This shift will help alleviate traditional infrastructure concerns and allow companies to focus entirely on real-time decision-making rather than managing data pipelines. Instead of moving vast amounts of data across different cloud environments, processing will happen where the data lives, reducing cost and complexity. AI models will no longer be trained solely on historical data; instead, they will continuously ingest and learn from live enterprise data streams, unlocking a new level of intelligence across industries.

For example banks that struggle with fraud detection at scale need to process billions of transactions per day, but traditional data pipelines couldn’t keep up. By optimizing data processing at the GPU level, they are able to run models in real time rather than waiting hours or days for batch processing. This is the future of compute—instant, automated, and embedded into every transaction.

🎧 Listen to our discussion

5YF #31: Unlocking 90% of Data, AI Bottlenecks, DeepSeek vs. Stargate, Precision Forecasting, and the Future of Data Processing w/ Voltron Data CEO Craig Dunham

AI Models Will Be Hungry For Smarter Compute

The AI boom has driven unprecedented demand for compute, pushing data centers to their limits in power, space, and cost. Jensen Huang of NVIDIA has warned that AI inference could require 10x more compute power than training—a trajectory that, if unchecked, would make today’s AI infrastructure completely unsustainable.

But the future of AI isn’t just about more GPUs—it’s about smarter compute. Instead of brute-force scaling, companies will focus on optimizing existing hardware for higher efficiency and output. DeepSeek’s GPU optimizations have shown what’s possible when software fully exploits hardware capabilities, and Groq’s LPUs are further proving that rethinking chip architecture can unlock orders of magnitude performance gains.

All eyes will be on Elon Musk’s Memphis data center and the next iteration of Grok as test cases for how tightly integrated hardware-software stacks can redefine AI compute. This signals a shift toward engineers working closer to the metal, where fine-tuning infrastructure at the lowest levels will become a competitive advantage.

In the next five years, we’ll see a new wave of AI-first databases, query engines, and processing frameworks that intelligently allocate compute based on workload needs, rather than the industry’s current “buy more GPUs” mentality. Governments and cloud providers may shift from building the biggest AI supercomputers to the most efficient ones, sparking a new arms race in compute optimization.

Craig Dunham highlighted a key reality: most enterprises are sitting on massive amounts of unused compute capacity. Their existing servers aren’t fully utilized, yet they keep buying more infrastructure. The companies that win the next phase of AI won’t be the ones with the most hardware—they’ll be the ones that unlock the full potential of what they already have.

Agentic Insights Opens Door To Fully Autonomous Decision-Making

The rise of AI agents is redefining how businesses operate, accelerating the demand for higher-speed data processing at lower economic cost. These agents aren’t just assisting humans; they are beginning to take over real-time decision-making, pulling insights from massive data sets and acting instantly.

Today, predictive analytics helps companies anticipate trends, but humans still make the final call. That’s about to change. By 2030, enterprises won’t just use AI to predict the future—they’ll use it to act on it automatically. AI-driven systems will optimize supply chains, financial trading, cybersecurity, and marketing without human oversight.

As businesses move from analyzing past data to continuously optimizing the present, AI will execute high-stakes decisions at a scale humans simply cannot match. Systems will autonomously adjust pricing, manage inventory, detect fraud, and trigger security responses in milliseconds. This shift will demand a new level of compute efficiency, as AI agents will require instant access to vast amounts of structured and unstructured data to operate effectively. Governments and regulators will be forced to rewrite the rules around AI-driven decision-making in finance, healthcare, and national security.

Consider a government agency using AI-powered geospatial analytics. Instead of waiting weeks for human analysts to review satellite imagery, AI will autonomously detect anomalies in real-time—flagging potential security threats, disasters, or climate risks. But it won’t stop there. AI agents will trigger automated workflows, such as deploying drones, adjusting emergency response plans, or rerouting resources before a crisis escalates.

To make this future a reality, AI agents must be powered by dramatically faster and more efficient compute. Running these systems at scale will require an order-of-magnitude reduction in data processing costs—something that cannot be achieved with today’s brute-force hardware approach. The future of AI decision-making hinges on smarter compute, optimized infrastructure, and real-time data pipelines.

This is the next frontier of data processing—where AI shifts from analyzing the past to actively shaping the future. With data volumes exploding and AI models becoming more sophisticated, the benefits of giving up manual control to autonomous systems will soon outweigh the risks. The world will move from managing AI to trusting AI.

Time to get crunching on that data!

Future AI: An Open Source Tsunami

Hi {{ first name | there }}!

Never a dull moment in tech!

Welcome back to the 5 Year Frontier and happy release day. Today’s episode comes perfectly timed with the news on DeepSeek and open source AI models. Tune in here 🎧

Our guest couldn’t be better placed to comment on AI’s current events and, importantly for our purpose, what it all means for the future — Lin Qiao built open source infrastructure at Meta, leading a team of 300 AI engineers. Her Sequoia-backed startup Fireworks AI, is barely 3 years old and already valued at $550M thanks to having built infrastructure to deploy over 100 AI models. Fireworks AI was one of the first to make DeepSeek deployable for US enterprises.

Join us as we dive into the future of AI models and infrastructure, fast forwarding this battle for AI supremacy.

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My 5 Year Outlook:

• Open Source Tsunami Comes For AI

  DeepSeek has (briefly) put Open Source ahead of proprietary AI performance. This is just the beginning of the community movement that will see it win the model wars.

• Shift To Small Expert Models As LLMs Commoditize

  Low cost, expert small models unlock the important last mile of value in enterprises and consumer.

• Enterprises Transform Their Private Data In AI Assets

  Private data eclipses public internet data, and it will steadily processed to be high value AI assets.

Curious? Read on as I unpack each below 👇🏼

Open Source Tsunami Comes For AI

Over the next five years, open source AI will outcompete proprietary models, reshaping how AI is built and deployed. The rise of DeepSeek and LLaMA proves open models are no longer mere alternatives—they’re now outperforming closed systems in logical reasoning, adaptability, and efficiency. For instance, DeepSeek already beats GPT-4 on reasoning benchmarks. While OpenAI’s latest reasoning model, released this week, set new records for reasoning, it’s an endurance race: given enough time, the collective innovation of open source will overrun elite labs.

The open source community is global, moves fast, shares best practices, and iterates in public. GitHub reported over 10,000 new AI code commits in January alone—a pace that proprietary labs can’t match. The industry is shifting toward an open, modular ecosystem much like Linux did for operating systems or Kubernetes for cloud computing. In five years, the biggest breakthroughs won’t come from closed labs but from the collective intelligence of the open-source movement.

Enterprise adoption is set to drive this trend. Facing high API fees and restrictive models from providers like OpenAI and Anthropic, companies are increasingly turning to self-hosted AI solutions for full customization and control. A recent survey of Fortune 500 companies revealed a 20% surge in self-hosted AI deployments in the past month, signaling that as open models improve, enterprise demand will only accelerate the shift.

🎧 Listen to our discussion

5YF #30: DeepSeek Deployed, How Open-Source Wins, The Small Model Revolution, AI Infrastructure Wars, and the Future of AI w/ Fireworks AI CEO Lin Qiao

Shift To Small Expert Models As LLMs Commoditize

The next major shift in AI is evolving from monolithic, general-purpose models toward small, specialized expert models. Enterprises don’t need a single massive AI that performs everything at a mediocre level—they require highly optimized models that excel in specific domains like healthcare, law, and finance. Large general-purpose models and small expert models are intertwined but to date the focus has been on developing large scale LLMs, going forward there will be shift towards the proliferation of light weight specialized models.

This evolution mirrors the software shift from monolithic architectures to microservices, where agility, speed, and efficiency became paramount. Open-weight models like DeepSeek and fine-tuned variations of LLaMA are already demonstrating superior performance in niche applications while significantly lowering inference costs.

Ultimately, it all comes down to the unit economics of individual AI runs—critical for interactive applications where even a slight increase in latency can have a huge impact. For example, a mobile banking app with an AI fraud detection system that adds 100ms per call can quickly slow down during peak times, frustrating users and driving up server costs. A streamlined, small expert model cutting latency by 20% can dramatically enhance performance and reduce expenses.

This trend is also evident in the race for on-device AI. About a year ago, there was immense excitement over on-device models—those designed to run directly on devices rather than in the cloud. Apple led the charge by launching an on-device model for iPhones in June, shortly after Google’s Gemma and Microsoft’s Phi-2 small models. While the hype around on-device AI subsided as large language models like GPT-4 began dominating cloud-based reasoning tasks, DeepSeek’s emergence has reinvigorated interest in smaller, cost-effective models that can operate efficiently on limited hardware.

This is particularly relevant for Apple, which has long championed on-device processing for privacy and performance. By leveraging small expert models, Apple could offer more powerful, tailored AI experiences on its devices without relying solely on cloud infrastructure—further differentiating its ecosystem from traditional LLM-dependent platforms.

Zyphra, an open-source small model developer that is in talks to raise $100 million at a $1 billion valuation, say on-device models are built to work with limited storage and processing power compared to models running in the cloud. They are trained on fewer parameters and are cheaper to run than larger models. As Steve Jurvetson, founder of Future Ventures and investor in Zyphra, explains, “On-edge developers’ constraints are very different. Your entire development pipeline looks different, and the products are different.”

Together, these shifts point to a future where hundreds of small expert models power a diverse array of applications, fundamentally transforming how AI is integrated into everyday technology and business processes.

Enterprises Transform Their Private Data In AI Assets

The biggest untapped opportunity in AI is converting vast, underutilized enterprise data into actionable intelligence. Every major company sits on enormous datasets—from customer interactions to supply chain logistics—but much of this data remains unstructured and siloed. Over the next five years, businesses will shift from simply storing data to activating it through AI. Recent surveys show nearly 70% of enterprise executives view leveraging proprietary data as a key differentiator by 2025, echoing the transformative impact of the cloud computing revolution.

The transformation is already underway. For example:

• Walmart has integrated AI-driven forecasting to optimize inventory management, boosting efficiency by around 20%.

• UPS uses machine learning for route optimization, achieving significant fuel savings and reduced delivery times.

• GE leverages sensor data for predictive maintenance, cutting maintenance costs by up to 30%.

Automation and seamless integration are critical to this change, enabling companies to fine-tune AI models without extensive in-house expertise. Emerging platforms like DeepSeek are lowering barriers for data infusion, bridging the gap between general-purpose models and company-specific applications.

As Lin emphasizes, enterprises must adapt their models to internal data to fully capitalize on this opportunity. The winners will be those who can automate the conversion of raw data into dynamic, self-improving AI systems, fundamentally redefining competitive advantage.

Time to turn data into intelligence. Happy building!

Future Materials: AI Designed Atoms

Hi {{ first name | there }}!

Happy 2025! I hope your year is off to a great start.

Welcome back to the 5 Year Frontier and happy release day! Tune in to our first episode of the year here 🎧.

Today, we dive into the future of discovering, designing, and deploying new breakthrough materials leveraging AI.

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Material science is on the brink of a revolution, fueled by AI and computational breakthroughs. At the helm of this transformation is Orbital Materials and its CEO, Jonathan Godwin, who are pioneering new ways to design and deploy advanced materials that could reshape industries from energy to aerospace. Their bold approach combines AI-driven design with real-world scalability, unlocking innovations that were once unimaginable.

In the next five years, the future will be defined by AI-designed materials that grant humanity unprecedented control over the physical world, data centers that resemble biological systems, and hybrid materials inspired by nature. These advancements promise not only to transform industries but to fundamentally change how we build, power, and interact with the world around us.

My 5 Year Outlook:

• Foundation Models That Can Manipulate The Physical World

  AI that understands the periodic table and simulates the world of atoms.

• Data Centers Start Resembling Biological Systems

  Chips submerged in liquid vats and vast array of connective tissues.

• Hybrid Materials Will Merge Biology And Material Science

  Elegance in natures design merge the biological with the physical.

Curious? Read on as I unpack each below 👇🏼

Foundation Models That Can Manipulate The Physical World

Foundation models for materials science, like Orbital Materials’ Orb, will redefine innovation across industries. These models, trained on the entire periodic table and vast datasets of material interactions, have a valuable compounding capability: the more experiments and simulations that are run, the smarter and more accurate they become. This feedback loop will revolutionize humanity’s ability to predict, simulate, and design entirely new materials in silico, enabling scientists to manipulate atoms and molecules with extraordinary precision. Instead of focusing on single-use materials, these tools will empower the creation of versatile, purpose-optimized variants—from hyper-efficient solar panels to medical implants tailored to individual patients.

By simulating atomic interactions digitally, material development will become as agile and iterative as software engineering. Imagine crafting virtual prototypes of materials that don’t yet exist, testing their performance under varying conditions, and optimizing them—all without entering a lab. This unprecedented control will not only revolutionize industries such as energy, transportation, and electronics but also give humanity a greater command over the physical world. The potential applications are boundless, from self-cleaning surfaces to climate-resilient infrastructure, paving the way for solutions to challenges we can only dream of today.

By 2030, this AI-driven approach could reduce material development timelines by 80-90%, unlocking transformative breakthroughs in semiconductors, batteries, and carbon capture. Moreover, open-source models like Orb will democratize access to these innovations, empowering a global community of researchers to tackle the world’s most pressing challenges collaboratively.

🎧 Listen to our discussion

5YF #29: Floating Data Centers, Decarbonized Compute, New AGI Infrastructure, Foundation Models for Molecules, and the Future of Materials w/ Orbital Materials CEO Jonathan Godwin.

Data Centers Start Resembling Biological Systems

Future data centers will look dramatically different to day’s mass of electronics. If built for Earth, large-scale data center campuses may integrate small modular nuclear reactors, providing abundant, reliable energy tailored to the growing demands of AGI workloads. These reactors will address energy scarcity while aligning with global decarbonization goals. Meanwhile, the first orbital data centers could emerge in space, leveraging the vacuum environment for efficient cooling and solar power for sustainable energy. These extraterrestrial facilities will drive innovation in material science, requiring cutting-edge thermal management systems and radiation-resistant materials to thrive in extreme conditions.

Visually, data centers will evolve from mechanical to biological-inspired designs. Traditional racks and fans will be replaced by transparent vats filled with viscous, bubbling liquid, engineered for exceptional thermal conductivity. Chips will be fully immersed in this liquid, with phase-change cooling fluids efficiently dissipating heat. Walking through these facilities might feel like entering a futuristic lab, with interconnected liquid baths and intricate wiring networks resembling the neural pathways of a brain. These advancements won’t just reduce costs—they’ll enable AGI to scale dramatically, allowing for larger, more energy-efficient training simulations and unlocking the next wave of computational breakthroughs.

Hybrid Materials Will Merge Biology And Material Science

The convergence of biology and material science will emerge as one of the most groundbreaking frontiers over the next five years. Advances in AI and computational tools are unraveling problems once deemed unsolvable, such as modeling interactions between metals and proteins. Orbital Materials’ pioneering work with hybrid materials highlights the transformative potential of combining biological precision with the robustness of traditional materials. This fusion could unlock innovations like bio-inspired electronics, self-healing infrastructure, and bio-friendly sensors that seamlessly integrate into our lives.

Imagine a building material that mimics the regenerative properties of bone—able to detect micro-cracks and autonomously repair itself before structural integrity is threatened. Such materials could revolutionize infrastructure, drastically reducing maintenance costs while improving safety in critical environments like skyscrapers, bridges, and space habitats. These intelligent materials would represent a monumental leap in how we build and sustain the physical world.

Another compelling application is wearable technology that feels alive. Envision a flexible, skin-like fabric embedded with sensors capable of monitoring vital signs, adapting its temperature for comfort, and even self-healing small tears. This innovation would transform industries such as healthcare, athletics, and defense. Enabled by AI-driven design, these hybrid materials will be developed faster than ever, as computational models decode the molecular intricacies of biological systems. This fusion of biology and material science promises to blur the boundaries between the synthetic and the organic, redefining the products we use and the world we inhabit.

The AI-led era of material science is enabling the physical world to not only keep pace with but also power the revolutionary transformations of our digital age.

Here’s to building the impossible! 

28 Visions For The Future: Prepare Your Mind For 2025 And Beyond

Hi {{ first name | there }}!

2025 is set to be a year of mind-bending innovation. As we stand on the edge of accelerating change, the ability to anticipate what’s next has never been more critical—or more thrilling.

To help you see ahead, we’ve compiled 28 bold visions from the CEOs and founders featured on the 5 Year Frontier podcast.

These leaders—who’ve collectively raised over $4 billion—are reshaping industries, rewriting the rules, and building the technologies that will define our future.

Scroll down, explore the frontier, and prepare your mind for the breakthroughs and opportunities that 2025 and beyond will bring 👇🏼

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Future of human performance 5YF #21 - Superhumans, doped Olympics, enhancement clinics, pharma’s trillionaires, Peter Thiel’s moonshot, and the future of human potential w/ Enhanced Games President Aron D’Souza

Future of code 5YF #20 - AI eats software, specialized models win, billions of coders, synthetic data, NVIDIA and the Hyperscalers, and the future of code w/ Poolside CEO Jason Warner

Future of gaming 5YF #12 - Living games, endless worlds, AI characters, accelerating human evolution, and the future of gaming w/ Inworld Co-founder Kylan Gibbs

Future of drones 5YF #2 - Drone networks, Earth’s autonomous logistics system, disrupting eCommerce giants, and the future of delivery w/ Zipline CEO Keller Rinaudo Cliffton

Future of industrial AI 5YF #23 - Industrial AI, Generative Engineering, data moats, Saudi Aramco the tech giant, and the future of the industrial economy w/ Geminus CEO Greg Fallon

Future of cybersecurity 5YF #25 - Cyber Attacks, AI Criminals, Deep Fakes, Data Shields, and the Future of Cybersecurity w/ Arctic Wolf CEO Nick Schneider

Future of Housing 5YF #22 - Housing reimagined, the construction cloud, data-driven designs, virtual home buying, automatic permits, and the future of home building w/ Higharc CEO Marc Minor

Future of biomanufacturing 5YF #24 - The $4T Bioeconomy, Nature’s Factory, AI Scientists, Global Bio Supremacy, and the Future of Biomanufacturing w/ Synonym CEO Edward Shenderovich

Future of biology 5YF #26 - Machines Controlling Biology, Light Reprogramming Cells, Algorithms for Drug Development, Commoditizing Pharma, and the Future of Biotechnology w/ Prolific Machines CEO Deniz Kent

Future of genetic engineering 5YF #17 - Defeating death, programming life, resurrecting the Woolly Mammoth, AI’s blueprint for biotech, and the future of genetic engineering w/ Colossal Biosciences CEO Ben Lamm

Future of industrial robotics 5YF #27 - Industrial Robots, Smart Infrastructure, Predictive Maintenance, Reinforcing America’s $5T Backbone, and the Future of Robotics w/ Gecko Robotics Co-founder Troy Demmer

Future of space operations 5YF #16 - Space command reimagined, automating mission control, new deep tech hubs, interstellar missions, and the future of space operations w/ Epsilon3 CEO Laura Crabtree

Future of 3D Printing 5YF #14 - 3D printing breakthroughs, AI designed products, mass customization, spare body parts, dismantling global trade, IP piracy, and the future of production w/ Carbon Co-founder Philip DeSimone

Future of manufacturing 5YF #15 - Factory automation, humanoid robots, digital twins, building the backbone of AI and the future of manufacturing w/ Bright Machines CEO / Eclipse VC Lior Susan

Future of flight 5YF #13 - Flying cars, electrifying the skies, re-drawing our city limits, mountain lairs and the future of flight w/ Archer Aviation CEO Adam Goldstein

Future of the brain 5YF #19 - Turbocharging the mind, digital drugs, psychedelics, autonomous therapies, the neuroscience frontier, and the future of the brain w/ Neuroscape Founder Dr Adam Gazzaley

Future of online advertising 5YF #28 - Advertising On Autopilot, End of Cookies, Privacy-First Marketing, Next Gen Agencies, and the Future of Online Advertising w/ Prescient AI CEO Mike True

Future of ideas and creativity 5YF #18 - Redefining intellectual property, programmable art, AI’s trust layer, South Korea’s rise, and the future of creativity w/ Story Protocol Co-founder Jason Zhao

Future of biosecurity YF #9 - Fighting the next pandemic, masks made of light, germ free buildings, viruses in space and the future of biosecurity w/ R-Zero Systems co-founder Ben Boyer

Future of healthcare 5YF #5 - AI diagnostics, monitoring our voice for disease, the mental health pandemic, and the future of healthcare w/ Kintsugi CEO Grace Chang

Future of fitness 5YF #7 - AI entrenches vertical SaaS, predicting customer behavior, global wellness in 2024, and the future of fitness w/ Mindbody CEO Fritz Lanman

Future of sports and entertainment 5YF #11 - Fans take on billionaire sports owners, athletes as eCommerce companies, 100 Kentucky Derby winners, and the future of sports and entertainment w/ Commonwealth CEO Brian Doxtator

Future of safety and security 5YF #10 - Crime fighting cameras, threat prediction, Large Vision Models, and the future of multi-sensory Ai w/ Ambient Ai CEO Shikhar Shrestha

Future of construction 5YF #8 - Automating profitability in construction, software powered job sites, predicting $15T of spend, and the future of building w/ Briq CEO Bassem Hamdy

Future of real estate 5YF #6 - Sustainable cities, $300M transformation of the Empire State, open source buildings, and the future of real estate w/ Empire State Realty Trust CEO Anthony Malkin

Future of money 5YF #4 - Personalized finance, what's brewing in crypto winter, new game economies, and the future of money w/ Finder co-founder Fred Schebesta

Future of delighting customers 5YF #3 - 100x worker performance, predictive call centers, and the future of delighting customers w/ ASAPP Founder Gustavo Sapoznik

Future of food 5YF #1 - Robotic restaurants, vertical farming, drone delivery, and the future of healthy eating w/ Sweetgreen CEO Jonathan Neman

A HUGE THANK YOU to our visionary guests and to YOU, our listeners.

Here’s to a monumental 2025 and beyond!

Future of Advertising: Predictive Modeling

Hi {{ first name | there }}!

It’s release day! Tune in here 🎧.

Today, we dive into an advertising industry rapidly adjusting to a privacy-centric internet.

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Online advertising has long been the engine driving the free, accessible internet we know today, powering its adoption by billions worldwide. With $700 billion spent annually, digital marketing has reached unparalleled levels of sophistication, leveraging user targeting and personalization to achieve remarkable effectiveness. Yet, the rapidly approaching era of data privacy threatens to upend this model, leaving advertisers without the tools to navigate the new landscape. Enter Prescient AI—a pioneering marketing technology company leveraging advanced data models to equip marketers for success in this evolving digital ecosystem.

Mike True, Prescient AI’s CEO, envisions a future where marketers seamlessly blend data science with creative intuition to deliver exceptional value in an increasingly anonymous internet. Let’s take a look forward to how the industry could shake out…

My 5 Year Outlook:

• Predictive Models Will Replace Cookies As Advertisers Primary Lens

  The arrival of a privacy-first and cookie free internet gives rise to statistical models.

• Autonomous Advertising Dominates

  An inevitable trend line of advanced automation from planning to creative to buying.

• Marketer As Data-Driven Visionary

  A hybrid of data-driven strategist and creative visionary, embedded deeply within the brand.

Curious? Read on as I unpack each below 👇🏼

Predictive Models Will Replace Cookies As Advertisers Primary Lens

Over the next five years, the shift to a privacy-first internet will fundamentally alter the digital advertising landscape. The demise of third-party cookies and stricter data regulations like GDPR and CCPA signal the end of traditional user tracking methods. Apple’s iOS 14.5 update, which introduced App Tracking Transparency (ATT), was a watershed moment in this transformation and points to how quick the ramifications can be felt. By requiring apps to seek user consent before tracking their activity across platforms, Apple effectively dismantled a key pillar of traditional digital advertising. The impact was immediate: Meta reported a $10 billion revenue loss in 2022 alone due to reduced ad targeting effectiveness. 

These developments will force marketers to abandon reliance on granular tracking and pivot to first-party data collection. Brands that embrace loyalty programs, direct-to-consumer relationships, and cohort-based targeting will be best positioned to thrive. For example, Coca-Cola’s use of loyalty apps to gather valuable customer insights has allowed it to deliver personalized campaigns while staying privacy-compliant.

To navigate this new reality, predictive models like advanced Marketing Mix Modeling (MMM) are emerging as critical tools for measuring return on ad spend (ROAS). Unlike traditional attribution models, which are undermined by privacy restrictions, predictive models use statistical techniques to analyze historical data and forecast campaign performance across channels. For instance, dynamic MMMs can now run daily, allowing marketers to reallocate budgets in real time based on expected outcomes. These tools not only provide a privacy-compliant way to measure effectiveness but also offer a means to synthesize vast amounts of tangential data into cohesive brand ecosystems. By creating data models that map a brand’s unique dynamics and user behavior, marketers can uncover actionable insights, optimize campaigns, and future-proof their strategies in an increasingly fragmented digital environment.

🎧 Listen to our discussion

5YF #28: Advertising On Autopilot, End of Cookies, Privacy-First Marketing, Next Gen Agencies, and the Future of Online Advertising w/ Prescient AI CEO Mike True.

Autonomous Advertising Dominates

The era of semi-manual media buying is fading as advanced data models revolutionize advertising. Tools like dynamic Marketing Mix Modeling (MMM) and probabilistic analytics are driving the shift toward fully automated campaigns. These systems enable brands to optimize ad spend at the campaign level in real time, delivering predictive accuracy of up to 90%. Such advancements promise not only efficiency but also precision, transforming how businesses allocate their marketing budgets.

In this automated future, media buyers will no longer be manually managing campaigns. Instead, intelligent models will confidently recommend and execute optimizations based on back-tested data and dynamic forecasting. This shift empowers brands to adopt in-house teams armed with cutting-edge tools to plan, activate, and measure campaigns, reducing the reliance on traditional ad agencies. Platforms like Shopify’s AI-driven advertising solutions are leveling the playing field for small businesses, enabling them to compete with industry giants through automated targeting and optimization.

An open question remains: how far can AI go in automating the creative aspects of advertising, such as ad design and copywriting? Adtech companies and startups are increasingly leveraging generative AI (GenAI) to adapt creative assets natively across different channels and develop entirely new variations with minimal human input. For instance, Jasper AI helps brands craft tailored ad copy by analyzing tone, audience, and messaging, while VidMob uses AI to provide insights on creative performance, enabling real-time design optimization. These advancements are pushing the boundaries of automation, but creativity often demands emotional resonance and cultural nuance—areas where human intuition still plays a critical role. Whether AI will fully replace creative teams or serve as an augmentation tool remains to be seen.

While automation offers unparalleled scale, the human marketer’s role remains vital. Marketers will need to act as strategic guides, ensuring that automated systems align with brand objectives and ethical practices. Success in this new era will hinge on a balance between machine efficiency and human oversight, leveraging automation to amplify impact while maintaining authentic connections with audiences.

Marketer As Data-Driven Visionary

The marketer’s role is set to transform into a hybrid of data-driven strategist and creative visionary, embedded deeply within the brand rather than outsourced to agencies. As automation and AI take over much of the media buying and operational workload, marketers will lead in orchestrating complex systems of data inputs and predictive models. Tools like dynamic Marketing Mix Modeling (MMM) and incrementality testing will become staples, enabling marketers to synthesize insights from fragmented datasets while intuitively guiding strategy. Brands like Netflix, which already use behavioral analytics to inform creative campaigns, offer a glimpse into this future, where marketers balance technical fluency with an overarching narrative focus. Privacy-first advertising and the rise of direct-to-consumer relationships will further push marketers to build first-party data ecosystems and craft campaigns grounded in authenticity.

Despite the rise of automation, creativity and emotional resonance will remain critical differentiators. Marketers will need to adapt to emerging channels—such as TikTok Shops, AI-powered conversational agents, and virtual commerce—while maintaining a brand-centric perspective. This evolution will also require marketers to act as cultural curators, staying attuned to societal shifts and emerging trends to shape messages that resonate with authenticity. The future marketer will be a connector of data, creativity, and culture, driving growth while keeping the human element at the core.

Keep it creative!

Future of Industrial Robots: A Built World Intelligence Layer

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It’s release day! Tune in here 🎧.

Today, we dive into how robots are revolutionizing the industrial sector and keeping our critical infrastructure safe.

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The built world—from bridges to power plants—is undergoing a digital revolution, driven by data and AI layered onto physical assets. Gecko Robotics leads this charge, monitoring over 500,000 assets with sensor-rich robots that crawl, swim, and fly to capture game-changing insights. With $5 trillion in U.S. infrastructure urgently needing repair, Gecko’s data models are unlocking a future of predictive maintenance and autonomous management.

Troy Demmer, Co-founder and Chief Product Officer, envisions a future where AI not only maintains but transforms infrastructure, enabling industries to design and operate assets with unprecedented agility and intelligence—shaping a smarter, more resilient world.

My 5 Year Outlook:

• Autonomous Infrastructure Management

  Data rich asset models and digital twins across physical infrastructure.

• $5T Infrastructure Design Upgrade Using Software

  Data-led improvements from new materials to completely novel designs.

• AI Models Advance To Replace The Need For Data Collection

  After reaching a certain scale of data and prediction quality, AI will simulate with unmatched accuracy.

Curious? Read on as I unpack each below 👇🏼

Autonomous Infrastructure Management

In the near future, our infrastructure will be managed more autonomously, blending robotics, digital twins, and AI to make data-driven decisions in real time. Every part of our built environment—from bridges and tunnels to power plants and pipelines—will have a digital twin. These virtual models allow real-time monitoring and diagnostics, capturing every detail of physical assets.

For example, Gecko Robotics is pioneering ultra-detailed digital twins of complex infrastructure, fusing data from multiple sensors from its robotic inspections. Imagine a bridge that, instead of undergoing sporadic manual checks, continuously updates its digital twin with data on wear, strain, and potential weak points. Drones scan its exterior while robots crawl over its frame complimented by embedded IoT sensors. In this future, roadways could predict and mitigate traffic wear based on vehicle loads, while water systems could adjust for peak usage, all autonomously.

This shift transforms physical infrastructure into a living, adaptable ecosystem powered by software. A digital layer that makes proactive maintenance feasible, preventing failures before they even surface. As we scale these data models across cities and states, digital twins will become essential tools for infrastructure management, fundamentally transforming how we maintain and improve our physical world.

🎧 Listen to our discussion

#27 - Industrial Robots, Smart Infrastructure, Predictive Maintenance, Reinforcing America’s $5T Backbone, and the Future of Robotics w/ Gecko Robotics Co-founder Troy Demmer

$5T Infrastructure Design Upgrade Using Software

Increased monitoring and data-collection of assets unlocks an ability to not just maintain asset health but improve its design and function. The most important evolution of this process will be the speed and timeframes to design upgrades. Previously, assets form water tanks to pumps to ships were manually observed over years if not decades before engineers suggested improvements. The future points to a collapsing of that timeframe and effort thanks to a constantly flow of data, digitization of the asset in software, and the simulation of different upgrades using AI.

Over the next five years, software-driven insights will transform infrastructure design, creating a continuous improvement loop where data leads directly to innovations in materials and architecture. As companies like Gecko Robotics feed massive amounts of real-world data into digital models, we’ll uncover patterns that inform smarter designs and longer-lasting materials.

For instance, a water tank digital twin could analyze stress distribution and environmental wear, revealing that a specific composite material, after countless simulations of variations, outperforms traditional steel in durability and cost-effectiveness. This isn’t hypothetical—real-world data can already indicate where to reduce material use without sacrificing safety, or how to design structures that better withstand natural disasters.

We’re moving toward an era where infrastructure not only responds to current conditions but also anticipates future needs. By making every infrastructure project an evolving prototype, data-led design will ensure that the next generation of infrastructure is more resilient, adaptive, and sustainable.

AI Models Advance To Depart From The Need For Data Collection

In five years, AI models will have progressed to a point where they can operate beyond the need for extensive, on-the-ground data collection. Trained on an enormous corpus of historical and real-time data, these models will become sophisticated enough to predict asset conditions, risks, and even maintenance needs with greater accuracy than human observation.

Imagine an AI model trained on the behavior of thousands of miles of pipeline. By recognizing patterns in temperature fluctuations, pressure changes, and material fatigue across assets, it can project the performance of future builds and pinpoint failure risks in existing infrastructure—often more reliably than if an inspector were physically present.

For instance, Gecko Robotics has data models covering over 500,000 assets. If they’ve gathered data on 1,000 identical water tanks, their model will be able to predict with precision how the 1,001st tank will perform. This level of predictability means that the need for fresh data collection diminishes as the dataset scales and model accuracy grows. Instead of continuous inspections, Gecko’s model leverages its vast, high-fidelity dataset to monitor new tanks remotely, setting a new standard for proactive asset management.

This shift doesn’t just improve efficiency; it redefines what’s possible. Inspections for remote, high-risk areas like isolated pipelines and offshore rigs can now be simulated by advanced AI models, enabling continuous monitoring in locations where human access is challenging or costly.

In effect, AI will provide a sharper, more consistent lens on the built world, transforming how we monitor, repair, and even design resilient infrastructure for the future. 

Let’s build!

Future of Biotech: Machines controlling biology

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It’s release day! Tune in here 🎧.

Today, we dive into biotech’s increasingly successful quest to read and write natures code.

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Prolific Machines is the first biotech company to harness light to manipulate cells. Developing a breakthrough approach to efficiently produce lab grown food, life saving drugs, and novel biosolutions. It has opened the door to machine and AI controlled cell interface and a new chapter of accelerating biotech progress.

Prolific Machines CEO Dr Deniz Kent envisions a biotech renaissance fueled by merging advances in biology and AI. Through rapid cycles of software-driven innovation and light-based bioreactors, Prolific is setting the stage for a more efficient, scalable, and accessible approach to harnessing the fundamental elements of life.

My 5 Year Outlook:

• A Programming Language To Read/Write Biology

  Machines and software will be able to control cells.

• Virtual Cells Becomes The Next Human Genome Project

  Simulating a cell in software will be biology’s next moonshot.

• Pharmaceutical Industry Produces Everyday Commodities

  From high value high cost to mass produced goods.

Curious? Read on as I unpack each below 👇🏼

A Programming Language To Read/Write Biology

Today, scientists design experiments and manipulate cells using chemicals—a lengthy, imperfect process, reflected by the 90% failure rate for drugs in clinical trials. AI has accelerated drug discovery, but a missing link has been the ability for software to directly interface with cells. A language between machines and cells can shift research from being scientist-led to machine-driven, enabling rapid, iterative testing and a higher success rate of new drugs to market.

Prolific Machines’ breakthrough is using light, rather than chemicals, as an input to guide cellular behavior. Light is easily manipulated by machines, making it ideal for efficient, cost-effective experimentation. In a bioreactor, sensors monitor cellular changes, providing real-time feedback to the software. This creates a powerful feedback loop that iterates until the desired outcome in the cell is achieved.

This innovation transforms scientists from hands-on experimenters to architects, directing changes they want to see while machines handle the tests.

The implications of a machine-to-cell language are transformative. With machines driving experimentation, biotech development could become exponentially faster, more precise, and less costly. Drugs, treatments, and even lab-grown foods could reach consumers far more quickly, responding to demand with the agility of software updates. This shift could democratize biotech, making advanced treatments and products accessible to more people while reducing reliance on human labor for high-stakes testing. Ultimately, this innovation sets the foundation for a future where biological research adapts in real time to new insights, needs, and discoveries.

🎧 Listen to our discussion

#26 - Machines Controlling Biology, Light Reprogramming Cells, Algorithms for Drug Development, Commoditizing Pharma, and the Future of Biotechnology w/ Prolific Machines CEO Deniz Kent

Virtual Cells Becomes The Next Human Genome Project

Although scientists now have tools to control cells, they don’t fully understand how cells work on a fundamental level—they’re still largely a “black box.” Deniz points out that knowing every detail isn’t always necessary however. If we can make a cell behave as needed, we can create useful products without fully understanding every mechanism.

Yet, if we could digitally map the cell and understand its workings then it would revolutionize science. The Human Genome Project mapped DNA and unlocked CRISPR and genetic therapies, showing us that decoding biological blueprints leads to scientific leaps.

A digital representation of a cell would allow scientists to run experiments entirely in software, skipping expensive lab procedures and dramatically advance the research process. Imagine testing cancer treatments or anti-aging solutions on virtual cells, running countless high-speed experiments 24/7 instead of lab researchers painstaking lab work over months or years. Overcoming the current intractable questions in biology would just be a matter of time and compute power. This approach would make complex biology scalable and fast, allowing biotech breakthroughs to happen at the speed of software development.

A global effort to create this virtual cell map could transform biotech, putting life-altering discoveries within reach faster than ever imagined.

Pharmaceutical Industry Produces Everyday Commodities

Advances in cell science and production efficiency are reshaping the possibilities for biologically-produced products beyond today’s complex, high-cost pharmaceuticals. According to Deniz, the industry is moving toward a future where biotech and pharmaceutical companies produce a broader array of low-cost, everyday items. This shift is happening because designing and manufacturing these products is becoming faster, cheaper, and more efficient.

A prominent example is lab-grown food. Alternative proteins, meat, eggs, and milk have already been produced, yet current versions remain costly and prone to variability, which limits their viability for large-scale, reliable production. Precision-guided light bioreactors and machine learning could steadily improve consistency, transforming these products into accessible options for consumers. This could eventually allow pharmaceutical companies to venture further into the food industry, producing an increasingly large share of food products.

Similarly, this approach extends to other areas, such as livestock feed, agricultural inputs, biofuels, and biological plastics. We discussed this trend in greater depth on episode 5YF#24 with Synonym and the arrival of the $4T bioeconomy (listen to the episode).

The biotech and pharmaceutical sectors are experiencing rapid change, expanding from high-cost, illness-focused drugs to wellness-enhancing products for everyone—Ozempic and GLP-1 drugs are prime examples of this shift. Could expanding markets and product lines position biotech and pharma companies at the center of economic production? With the rapid trajectory of scientific innovation, this outcome seems more possible than ever.

Keep exploring!

Future of Cyber Attacks: AI as Weapon and Shield.

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It’s release day! Tune in here 🎧.

Today, we dive into the rapidly evolving cyber threat landscape and why we’re more under siege than ever.

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Our infrastructure, businesses, and people are increasingly within reach of bad actors. The connected nature of our operations makes this an inevitability. What follows is an arms race—defense versus attack—fueled not by oil or bullets, but by data. Arctic Wolf scans over 5 trillion security events each week to safeguard its 3,000 customers, constantly evolving its technology to both harness its protective power and counter its potential for harm.

Arctic Wolf CEO Nick Schneider views cybersecurity as fundamentally a data challenge. Scale matters, and leveraging AI is key to identifying patterns, predicting threats, and responding faster than ever before. The challenge is, cybercriminals share his belief—turning data and AI into tools of attack, making the race for dominance over the next 5 years ever more urgent.

My 5 Year Outlook:

• There Will Be Might In Scale

  The network effects propelling cybersecurity giants.

• Cities And Nations Will Be Protected By Data Shields

  A threat data pool and national security graph will be our first line of defense.

• A New AI-Automated Cybersecurity Giant To Serve SMBs

  Fully automated end-to-end without need for management.

Curious? Read on as I unpack each below 👇🏼

There Will Be Might In Scale

In cybersecurity, scale is a critical advantage. The more data a company can access, the better prepared it is to identify threats and vulnerabilities, creating an information asymmetry that gives defenders the upper hand. As cyber threats become more sophisticated, this ability to rapidly analyze vast amounts of data is essential to staying ahead of attackers.

Arctic Wolf is a prime example of scale in action. By monitoring security incidents across 3,000 customers, an attack on one becomes an alert for all, creating a shared defense network. With 5 trillion security events analyzed weekly, Arctic Wolf’s AI-driven models are constantly learning, improving their ability to detect threats and respond quickly. The more data they have, the smarter and faster they become.

While network effects are usually associated with social media or marketplaces, they will be increasingly relevant in cybersecurity. AI models thrive on data, and the more a company processes, the better equipped it is to defend its customer base. This, in turn, attracts even more customers, accelerating the flywheel of growth. Over the next five years, this dynamic will intensify, with the largest cybersecurity firms pulling ahead by using their scale to evolve defenses faster than smaller competitors can keep pace.

In my view, this compounding advantage of data will define the future of cybersecurity, especially as attacks become increasingly AI-driven. Immediate response times and almost predictive defense capabilities will be key, and AI agents will need to be trained on vast datasets to effectively combat these evolving threats. The companies with the most data will lead the way in this new era of cyber warfare.

🎧 Listen to our discussion

#25 - Cyber Attacks, AI Criminals, Deep Fakes, Data Shields, and the Future of Cybersecurity w/ Arctic Wolf CEO Nick Schneider

Cities And Nations Will Be Protected By Data Shields

The concept of network effects in cybersecurity could redefine how cities and nations defend against cyber attacks and cyber warfare, offering an unparalleled first line of defense. Imagine governments harnessing the collective power of data from businesses, industries, and critical infrastructure to create a unified defense system, where threats are detected and neutralized before they cause damage. Although this would require a massive, coordinated effort to integrate data and collaborate across sectors, the potential benefits are staggering. Cities that adopt such a cutting-edge approach could become hubs of innovation and security, attracting businesses eager to operate in environments with advanced cyber defense capabilities.

By centralizing data into a city- or nation-wide pool, a graph-based AI system could map out relationships between users, devices, and networks in real-time, constantly scanning for suspicious activities or patterns that suggest emerging cyber threats. Phishing campaigns, ransomware attacks, and unauthorized access attempts could be detected and responded to before they escalate. This AI-driven network would act as a proactive and automated defense system, identifying anomalies and stopping cybercriminals in their tracks.

There is mutual benefit to both large and small organizations within this network, as cybercriminals often exploit weaker links in supply chains to gain entry into high-value targets.

The concept of using data shields and AI-driven defenses to combat cybercrime is already being explored in several cities and nations worldwide. One notable example is Singapore, which has taken significant strides toward integrating AI and data analytics into its national cybersecurity framework. Singapore’s Cyber Security Agency launched the Cybersecurity Operations Centre 2.0, a centralized hub that uses AI-powered tools to monitor cyber threats in real-time across the government and critical industries. By analyzing data from multiple sectors and using AI to predict and respond to emerging threats, Singapore has created a system where vast amounts of data from various sources are pooled to detect and mitigate cyber-attacks quickly.

In the United States, the Department of Homeland Security is exploring AI-based cyber defenses through initiatives like CISA’s (Cybersecurity and Infrastructure Security Agency) Artificial Intelligence for Cybersecurity Operations program. This initiative aims to use AI to analyze massive volumes of cyber data from critical infrastructure sectors—such as energy, water, and financial services—to predict and defend against cyber threats. While still in early stages, these efforts demonstrate the move towards using unified data pools and AI-driven systems as a primary defense mechanism against cybercrime at a national level.

A New AI-Automated Cybersecurity Giant To Serve SMBs

Advanced automation is the catalyst for a new era in cybersecurity, one that can finally serve Small and medium-sized businesses (SMBs) with the same efficiency and protection previously reserved for large enterprises. Until now, cybersecurity has primarily focused on big businesses, which can dedicate headcount and justify the high costs associated with managing complex security systems. Large enterprises have been able to invest in these solutions due to their massive annual contract values, but SMBs, with limited resources, have been left without adequate protection.

Now, thanks to advances in AI and machine learning, cybersecurity technology has evolved to the point where automation can replace the need for constant human oversight. AI-driven threat detection systems can learn and adapt on their own, while automated response tools can neutralize threats in real time. This means that cybersecurity can become a plug-and-play solution—one that is always on, always learning, and requires no manual intervention, making it an ideal fit for SMBs who don’t have the resources to manage complex security tools.

This shift opens the door for the creation of a cybersecurity giant with the technology, product, and brand identity dedicated to serve the SMB market. By providing turn-key, hands-off solutions, such a company can offer the same level of protection that previously required dedicated teams. What’s more, they can leverage the network effects I have described: as more businesses use the platform, the system gets smarter and more effective at detecting and preventing attacks.

We have never been more empowered and more vulnerable at the same time, and this dynamic will only intensify as technology innovation accelerates at an unprecedented pace

Stay safe!

Future of the $4T Bioeconomy: Nature as builder.

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It’s release day! Tune in here 🎧.

Today, we explore recreating the supply chain of products we consume using biological substitutes.

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Nature is the ultimate creator—an unparalleled designer, engineer, and operator. By comparison, our machines and factories feel crude while nature builds with elegance, efficiency, and precision. The bioeconomy, set to become a $4 trillion force, is reshaping how we make products by partnering with nature itself. Synonym sees this as the future and is funding and developing the infrastructure to power biomanufacturing.

Synonym CEO Edward Shenderovich envisions a world where energy, plastics, chemicals, and medicine aren’t built—they’re grown. This future is now within reach, thanks to breakthroughs in AI that allow us to decode, design, and collaborate with biology. A new era, where human ingenuity fuses with the power of nature, is on the horizon.

My 5 Year Outlook:

• One bio factory, countless products

  Biomanufacturing infrastructure is extremely versatile to what can be produced.

• Nature is coming for the petrochemical industry

  From fuel to plastics, the biological versions are more elegant and effective.

• Scientific AI models are giving us design control over biological organisms

  Alphafold revolutionized protein folding, other models are helping to create and modify existing organisms at speed.

Curious? Read on as I unpack each below 👇🏼

One bio factory, countless products

The magic of biomanufacturing lies in its incredible flexibility, especially in fermentation-based production systems. Unlike traditional factories that are locked into single-use processes, fermentation factories act like multi-purpose bio-kitchens. These facilities can take all kinds of inputs—plants, microbes, even waste—and process them into a wide variety of outputs. The same infrastructure can be used to brew biofuels, grow biodegradable plastics, or synthesize specialty chemicals. It’s as if you had a high-tech kitchen that could take different ingredients and create a whole range of new products, all in the same pot. This adaptability is why biomanufacturing is a game-changer, and we’re just starting to explore its vast potential.

The fun part is how fermentation technology is evolving. Companies are setting up “fermentation factories,” where engineered microbes are grown in bioreactors, fed various sugars or waste materials, and then produce products like bio-based textiles, eco-friendly cosmetics, or even lab-grown meat. These bioreactors are essentially huge, controlled vessels where microbes act as mini factories, breaking down inputs and assembling outputs at the molecular level.

While we still need to build and scale these fermentation factories, a process that will take capital and expertise, their versatility means we can innovate fast as they come online, adjust processes quickly, and reduce waste in ways traditional manufacturing just can’t. As these fermentation factories scale, biomanufacturing is poised to revolutionize industries with cleaner, smarter, and more adaptable production.

🎧 Listen to our discussion

5YF #24 - The $4T Bioeconomy, Nature’s Factory, AI Scientists, Global Bio Supremacy, and the Future of Biomanufacturing w/ Synonym CEO Edward Shenderovich

Nature is coming for the petrochemical industry

A notable first target for biomanufacturing is the transformation of the multi-trillion-dollar petrochemical industry, offering eco-friendly, bio-based alternatives to traditional oil-based products like fuels and plastics. As consumer (and therefore business) demand for sustainable solutions grows, the technology is advancing rapidly, offering practical, scalable ways to produce everything from materials to energy in a more environmentally friendly manner.

Plastics are everywhere—about 300 million tons are produced globally each year, much of it ending up in our oceans. Now, breakthroughs in bioengineering allow us to use plants, microbes, and algae to create renewable materials, drastically cutting greenhouse gas emissions and reducing plastic waste. The potential to revolutionize industries that rely on petrochemicals is enormous.

Exciting innovations are already here. LanzaTech is turning carbon emissions into ethanol for bio-based plastics and fuels, while NatureWorks produces compostable plastics from corn used in packaging and coffee pods. In cars, bio-plastics are replacing petroleum-based materials, and startups are even developing durable, biodegradable packaging from fungi. As these technologies scale, they’ll reshape industries from automotive to consumer goods, paving the way for a cleaner, greener future.

Scientific AI models are giving us design control over biological organisms

AI has been a key accelerant to ushering in the new bioeocnomy. Alphabet’s Alphafold, for example, has been a game-changer for biology, revolutionizing how we understand protein structures by predicting how millions of proteins fold. What once took years of painstaking lab work, AlphaFold accomplished in days, unlocking massive potential for drug discovery, synthetic biology, and more. 

Proteins are fundamental to the bioeconomy because they are the building blocks of life. Virtually every process in a living organism—growth, metabolism, repair—depends on proteins. From enzymes that speed up chemical reactions to antibodies that protect us from disease, proteins play a key role in everything from health to industrial applications. Understanding how proteins fold allows us to design custom proteins for biomanufacturing, creating new medicines, sustainable biofuels, and even biodegradable plastics. Beyond protein folding, models like CRISPR-AI for gene editing, and Generative Adversarial Networks (GANs) for metabolic pathway design are opening up new frontiers.

Imagine not only predicting protein folds but designing proteins and enzymes for specific tasks—like creating bacteria that break down plastic or crops that resist climate change. AI models can simulate how biological organisms will behave in real environments, giving us unparalleled control over bioengineering.

In the next five years, we’ll see rapid advances in custom-built organisms, personalized medicine, and bio-based products, all powered by AI that continuously learns and optimizes biological systems. What AlphaFold did for proteins, the next generation of AI models will do for the entire bioeconomy.

Let’s get growing!

Future of Industrial AI: Simulating The Real World

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It’s release day! Tune in here 🎧.

Today, we explore heavy industry’s embrace of AI to reimagine the physical economy.

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The amount of data within an industrial conglomerate is equivalent to the amount of data on the internet. This realization is sparking a new wave of awareness among industrial leaders: in an era dominated by data-hungry AI models, they are sitting on vast, untapped digital oil fields. The convergence of proprietary operational data, AI models capable of becoming expert systems, and a renewed drive for modernization is fueling a revolutionary shift among industrial giants.

In this transformative landscape, we focus on one of the leading AI startups, Geminus, whose specialized models are poised to revolutionize industries ranging from energy to semiconductors. At the helm is CEO Greg Fallon, a veteran who has shaped the product strategies of major industrial software companies like Autodesk and ANSYS. Now, he aims to disrupt the very industry he helped build.

My 5 Year Outlook:

• Industrial giants will monetize their digital infrastructure

  Following Amazon’s AWS playbook for immense profit.

• Generative Engineering will deliver heavy industry’s ChatGPT moment

  Physics-constrained expert AI models trained on immense volumes of proprietary data.

• Our physical world will be precisely simulated

  Digital twins grounded in the laws of physics.

Curious? Read on as I unpack each below 👇🏼

Industrial giants will monetize their digital infrastructure

Industrial giants are beginning to recognize the untapped value of the digital infrastructure they’ve developed in-house, much like Amazon did with AWS, the cloud infrastructure powering its eCommerce website. Initially built to support their own complex operations, these digital tools—ranging from advanced AI models to digital twin platforms and massive data systems— will be repurposed over the coming years and monetized as external offerings. By selling access to this infrastructure, these companies are creating entirely new revenue streams and marking a new chapter for the industrial economy.

Following Amazon’s playbook, where internal systems evolved into AWS and now contributes to $1T of the company’s market capitalization, industrial giants are poised to commercialize their proprietary tech and become pivotal players in industrial software. This shift transforms them from pure industrial manufacturers into platform providers, capable of selling cutting-edge digital solutions to other industrial companies seeking modernization.

Several factors uniquely position industrial giants for this digital renaissance: (1) complex and repetitive technical operations, (2) a workforce of highly trained and well-paid experts, (3) vast amounts of relevant data, (4) significant capital reserves, and (5) a pressing need to evolve beyond resource extraction and manual labor.

While it may seem obvious that a standalone industrial software company could emerge in this space, industrial companies are reluctant to share their data, viewing it as proprietary IP. Instead, this creates an opportunity for them to share a software product itself, safeguarding their data while still capitalizing on technological advancements.

Saudi Aramco, the world’s largest industrial company, offers a compelling first case study. Through Aramco Digital, they aim to transform their operations and modernize the broader Saudi industrial economy, before expanding globally. Backed by billions from their oil revenues, Aramco Digital is building a robust digital infrastructure focused on four key pillars: cloud, AI, connectivity, and security. Already, they have an ecosystem of 200 companies as both customers and solution providers. Saudi Aramco’s visionary strategy leverages its capital, talent, data, and deep expertise to create a digital future for its oil-dependent economy.

The next five years will see the rise of first movers looking to capitalize on exporting their digital expertise to peers across sectors. Mining giants like Rio Tinto and BHP, who already manage increasingly autonomous mines from hundreds of miles away, exemplify highly advanced organizations with the potential to modernize their entire industry. It’s not far-fetched to imagine these companies remotely operating mines in other countries. From mining and energy to agriculture and manufacturing, the wave of digitization is rapidly approaching in what could market a new industrial revolution.

🎧 Listen to our discussion

5YF #23 - Industrial AI, Generative Engineering, data moats, Saudi Aramco the tech giant, and the future of the industrial economy w/ Geminus CEO Greg Fallon

Generative Engineering will deliver heavy industry’s ChatGPT moment

While ChatGPT has captured the imagination of knowledge workers across various sectors, its value in the industrial world has been limited. ChatGPT is a general-purpose model, trained on a vast but generalized dataset—the entirety of internet data. However, what it lacks is the specialized expertise and precision required for deployment in industrial settings, where the stakes for accuracy are far higher.

Fortunately, industrial companies possess the specialized data and expert talent necessary to train highly accurate models. This is where companies like Geminus come into play, developing AI systems that can be rapidly scaled across organizations—often in weeks, not months or years.

The key to these models’ reliability lies in grounding them in the laws of physics, an approach Geminus calls AI-Augmented Computational Physics. By incorporating real-world constraints, these models can provide highly accurate simulations, such as predicting how a drill or pump will perform in specific geological conditions.

In addition to physical accuracy, these specialized models must excel in multi-step reasoning and problem-solving, a focus area for the AI industry. Recent advances, like ChatGPT-01, deploy AI agents that break down complex tasks into smaller, more manageable segments, collaborating to solve intricate problems with greater precision.

Geminus combines these two approaches—physics-constrained AI models and agent-based orchestration—to pioneer what it calls Generative Engineering, a nod to OpenAI’s Generative AI. The goal is to automate complex engineering tasks and provide an expert system that assists engineers and scientists. This ambitious vision could propel the industrial sector into a new era, but first, it must earn the trust of the industry’s key players.

Our physical world will be precisely simulated

Grounding AI models in the laws of physics will revolutionize not only the industrial economy but also how artificial intelligence interacts with and simulates the physical world. By embedding real-world constraints into AI, we create a more accurate digital representation of the environments in which these systems operate. This approach enables the creation of advanced simulations and digital twins—precise virtual replicas of physical systems—paving the way for AI to make decisions based on reliable, real-world context. With these foundations, AI systems can not only simulate physical processes but also predict outcomes with unprecedented accuracy.

This real-world context is critical in fields like autonomous driving and robotics, where AI must navigate and respond to complex physical environments in real time. Equally, it plays a crucial role in less obvious applications, such as space exploration and weather forecasting, where precise environmental modeling is essential. By ensuring AI understands how physical forces and laws impact operations—from friction and inertia to gravitational fields—we significantly improve the performance and safety of AI systems deployed in these demanding environments.

As AI continues to evolve, grounding it in first principles will become the key to unlocking more reliable and capable systems across industries. Whether orchestrating a constellation of satellites in deep space or forecasting increasingly extreme weather patterns, these physics-aware models will offer greater accuracy and trustworthiness.

The ability to simulate the real world with such precision is not just a technological leap—it’s a fundamental shift in how we apply AI to the physical challenges of the future.

Onwards!

Future of Housing: Digital Homes

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It’s release day! Tune in here 🎧.

Today, we explore the future of building new and affordable housing.

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Higharc is on a mission to revolutionize the $100 trillion U.S. residential real estate market, and cutting-edge technology is paving the way. Already thousands of homes are being delivered through a unified, data-rich platform that streamlines everything from sales to supply. This approach reimagines one of the most complex and expensive supply chains, transforming it into a highly agile, AI-assisted process.

Could this be our best chance to address the 6 million home shortfall quickly and affordably? I sit down with Higharc’s CEO, Marc Minor, who brings the disruptive mindset of someone who spent his career pioneering 3D printing to now tackle our housing challenge.

My 5 Year Outlook:

• Your Next House Is A Data Model

  Simulating and optimizing from design to suppliers to change orders.

• On-Demand Material Science

  Dramatic lowering of the testing cost of new materials, structures, and processes.

• Cheers To Instant Permits!

  Automatic approvals via API connectivity to government and council.

Curious? Read on as I unpack each below 👇🏼

Your Next House Is A Data Model

Currently, home building is a siloed process, with multiple stakeholders working independently. But the future lies in developing a unified digital workspace that breaks down these silos and creates a central source of truth. Traditional blueprints and CAD drawings have only brought the industry so far. The next evolution is a “digital thread” that connects everyone, from the customer and sales team to the worksite and back again.

Higharc’s innovation goes beyond conventional drawing sets by creating a spatial data model of a home. This data model serves as the central source of truth, allowing for dynamic 3D visualizations for salespeople, customizable material options, seamless sourcing of estimations from suppliers, and precise delivery of takeoff sheets onsite. Crucially, this data model automatically updates in response to changes from any stakeholder, whether they come from the job site or the customer, dynamically readjusting building plans, costs, and materials.

This approach extends beyond just the home itself; it also models the environment in which the home is being built. Factors like land topography, sun orientation, and surrounding aesthetic features can all be encoded into the model, providing rich contextual information. The result is a faster, more accurate, end-to-end process that remains resilient to the inevitable changes that occur throughout a project’s lifecycle.

Looking ahead, such a data model lays the groundwork for increased automation and AI-assisted construction. Each step of the process could soon have its own AI agent to offer recommendations and optimize decision-making. Home building will be fully simulated and refined in software before actual construction begins, with real-time monitoring to ensure projects stay on track and aligned with the owners vision.

🎧 Listen to our discussion

5YF #22 - Housing reimagined, the construction cloud, data-driven designs, virtual home buying, automatic permits, and the future of home building w/ Higharc CEO Marc Minor

On-Demand Material Science

A potential benefactor of the digital simulation of home building is the adoption of advanced, sustainable materials. From innovative support beams to eco-friendly insulation and cutting-edge 3D printing techniques, a growing library of materials offers exciting possibilities for home buyers and builders. However, shifting from conventional supplies to novel materials carries high risks, and mistakes can be costly.

Digital simulation allows for precise testing without the real-world risks, time, or expense. It enables builders to swap out any building material and run simulations to evaluate its performance, impact, and cost. This virtual testing environment can be done within the precise context of where the home is being built and its environmental influences.

Take, for example, recycled plastic composites, which are both durable and environmentally friendly. Digital simulations could highlight their strength and resistance to weathering, making them ideal for coastal or flood-prone areas, thus promoting their use in sustainable construction.

By minimizing risks and highlighting the advantages of new green materials, digital simulations and AI-driven tools empower builders and homeowners to confidently embrace sustainable and innovative options. This, in turn, fuels greater demand and adoption from emerging material suppliers, driving further innovation in the industry.

Cheers To Instant Permits!

Innovations are poised to extend beyond the design and construction process into the regulatory realm. With housing data models containing all the necessary information and the ability to be thoroughly analyzed, there is a clear path toward automating the permitting review and approval cycles conducted by local councils and state officials. Permitting is one of the most opaque and slowest components of construction, often plagued by backlogs and inconsistencies, adding months to project timelines.

To start, local laws and permit requirements for a specific project can be encoded into algorithms that run against the housing data model. This would enable developers to undergo a pre-approval review process, scoring how likely their project is to be approved and allowing for necessary adjustments before submission. It could even involve analyzing past submissions to enhance their chances of a successful approval.

As government officials build trust in this process and begin to adopt it, they could empower their staff to develop and refine the algorithms that govern approvals. Eventually, permits could be reviewed and issued almost instantly through an API. While maintaining transparency and control over decision-making will be a challenge, the potential benefits of reducing bureaucratic red tape and significantly shortening the time needed to bring new homes online are immense.

While none of these innovations alone can solve the 6 million home shortfall, together they represent a significant step forward in the overall efficiency and modernization of the housing industry.

Let’s get building!

Future of Human Potential: Showcasing Superhumans To The World

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It’s release day! Tune in here 🎧.

Today, we explore science and technology’s ability to break through humanity’s biological limits. 

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As one Olympics ends, another begins: The Enhanced Games, set to debut in 2025. These games place performance-enhancing drugs and genetic modifications at the forefront, aiming to showcase humanity’s untapped potential through cutting-edge science. Though controversial, they signal a future where human optimization could redefine sports, injury recovery, and longevity, potentially creating one of the largest new markets in history.

Led by Aron D’Souza and his team of serial entrepreneurs, with backing from investors like Peter Thiel and scientific support from Harvard’s Dr. George Church, the Enhanced Games could be a pivotal moment in our journey toward reimagining human biology.

My 5 Year Outlook:

• Biology’s Sputnik Moment

  In 2025, a globally televised event aims to redefine human performance and ignite a race for superhumanity.

• The Enhancement Economy

  The largest potential market ever created.

• Retiring At 100 Years Old

  If an enhanced 65 year old breaks an Olympic record, how will that shift our perception of the working age?

Curious? Read on as I unpack each below 👇🏼

Biology’s Sputnik Moment

In 2025, a globally televised event aims to redefine human performance and ignite the race toward superhumanity. This showcase will highlight what athletes can achieve when they fully embrace performance enhancements, pushing their potential to new limits. If records are shattered through scientific intervention, it could reshape our acceptance of biological enhancements in sports and, ultimately, in society.

There is a future where pharmacological intervention, genetic modifiers, and material science improvements, are embraced in sports. Even if just within the theatre of The Enhanced Games. As investments in these technologies grow, the gap between natural human abilities and those augmented by science will become increasingly apparent. The desire to surpass our biological limits is already present, from steroids in gym locker rooms to cosmetic enhancements on Rodeo Drive. What could happen if this desire is brought out of the shadows and into the mainstream, backed by full scientific and economic power?

All it might take to shift public perception is a spark—an undeniable proof—a Sputnik moment.

The Enhanced Games are being meticulously designed, from the science behind them to the social media strategy, to deliver that defining moment. If successful, they could trigger a surge in demand for enhancements and alter the course of human evolution. It’s difficult to envision a future where elite athletes, pharmaceutical companies, or governments remain passive as records in weightlifting, running, combat, and gymnastics fall to a new breed of competition.

🎧 Listen to our discussion

5YF #21 - Superhumans, doped Olympics, enhancement clinics, pharma’s trillionaires, Peter Thiel’s moonshot, and the future of human potential w/ Enhanced Games President Aron D’Souza

The Enhancement Economy

Formula One is a relentless pursuit of motorsport perfection—a symbiotic relationship between athlete and engineer. An F1 car bears little resemblance to a production Mercedes or Renault, nor would anyone be captivated by a race between stock vehicles. Yet, innovations born in F1, from anti-lock brakes to carbon fiber and traction control, have profoundly impacted the cars we drive every day. Similarly, The Enhanced Games seeks to forge a partnership between athletes and scientists, but with a focus on enhancing our bodies, not our machines.

The soaring demand for biological upgrades is exemplified by the blockbuster success of Ozempic and GLP-1 drugs. Originally designed for diabetes, a staggering 80% of their use is now off-label, primarily for weight loss. Transitioning from treating the ill to optimizing the well has added over $1 trillion to the market capitalization of companies like Novo Nordisk and Eli Lilly. By comparison, the major generative AI startups, including OpenAI, collectively hold a valuation of around $200 billion. As Aron D’Souza, President of The Enhanced Games, aptly points out, one enhancement drug’s market value eclipses that of the entire AI sector by fivefold.

While this comparison is purposefully provocative and lacks the nuance of a sophisticated valuation, it underscores the seismic shift underway in the pharmaceutical and healthcare industries—where the focus is increasingly on enhancing the healthy, not just treating the sick. Viewed through this lens, The Enhanced Games emerges as a brilliant marketing showcase for its true purpose: to become the global leader in enhancement protocols. After witnessing a 45-year-old sprinter shatter the 100M world record, viewers could visit an Enhanced Clinic and opt into the same protocol as that athlete. While regulators will face significant challenges, this marks the dawn of a new sector in the healthcare economy—one with an addressable market that includes not just the ill, but everyone else as well.

Importantly, The Enhanced Games is not alone in pushing the boundaries of human biology. Variant Bio, backed by Lux Capital, is assembling a comprehensive library of human genetic mutations. This effort could uncover new pathways for drug development or genetic therapies. Performance-enhancing mutations in one individual could be mapped, refined, and transferred to another, offering health and optimization benefits.

Though we are in the early stages today, the next five years could see the emergence of a vibrant and sophisticated biological optimization industry.

Retiring At 100 Years Old

Perhaps the first cohort of benefactors to these trends over the next 5 years will be our aging baby boomer population. As the most health-conscious and financially prepared aging cohort in history, their pursuit of a healthier, longer life may well fuel the burgeoning Enhanced Economy.

This movement isn’t just about living longer—it’s about redefining the future of humanity, where aging becomes a choice and our biological limits can be transcended.

Imagine a 65-year-old enhanced sprinter breaking the 100M Olympic record. Such a feat would shatter our current perceptions of what it means to be ‘senior’ and could trigger a societal shift in how we view retirement. As Aron D’Souza suggests, this could lead us to reconsider the very notion of retirement, emphasizing the potential of enhancing our aging populations rather than merely caring for them.

Science already demonstrates the profound benefits of these enhancements: accelerated injury recovery, preserved muscle mass, and fortified skeletal structures. The evidence is mounting, yet societal acceptance lags behind. A similar trajectory can be observed in the realm of longevity science, once considered fringe but now steadily gaining recognition in mainstream healthcare. This critical shift is drawing in the funding and talent necessary to propel these innovations forward.

It’s clear to me that we are on the cusp of a global initiative, akin to a Manhattan Project for human biology. Now is the time to let science thrive, to ask the hard questions, experiment boldly, and validate our findings—unhindered by outdated constraints.

The future is calling. Onwards!

20 Visions For The Future: Celebrating our 20th Podcast Episode

Hi {{ first name | there }}!

Our world is in flux, making it more critical than ever to look ahead. In our 20th episode of the 5 Year Frontier podcast, we recap insights from 20 tech leaders who are reshaping their industries for the future.

These leaders have collectively raised over $3B to drive disruption in their industries and redfine our path forward.

Scroll down to embark on your journey to the near frontier 👇🏼

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Future of code 5YF #20 - AI eats software, specialized models win, billions of coders, synthetic data, NVIDIA and the Hyperscalers, and the future of code w/ Poolside CEO Jason Warner

Future of genetic engineering 5YF #17 - Defeating death, programming life, resurrecting the Woolly Mammoth, AI’s blueprint for biotech, and the future of genetic engineering w/ Colossal Biosciences CEO Ben Lamm

Future of drones 5YF #2 - Drone networks, Earth’s autonomous logistics system, disrupting eCommerce giants, and the future of delivery w/ Zipline CEO Keller Rinaudo Cliffton

Future of space operations 5YF #16 - Space command reimagined, automating mission control, new deep tech hubs, interstellar missions, and the future of space operations w/ Epsilon3 CEO Laura Crabtree

Future of gaming 5YF #12 - Living games, endless worlds, AI characters, accelerating human evolution, and the future of gaming w/ Inworld Co-founder Kylan Gibbs

Future of 3D Printing 5YF #14 - 3D printing breakthroughs, AI designed products, mass customization, spare body parts, dismantling global trade, IP piracy, and the future of production w/ Carbon Co-founder Philip DeSimone

Future of manufacturing 5YF #15 - Factory automation, humanoid robots, digital twins, building the backbone of AI and the future of manufacturing w/ Bright Machines CEO / Eclipse VC Lior Susan

Future of flight 5YF #13 - Flying cars, electrifying the skies, re-drawing our city limits, mountain lairs and the future of flight w/ Archer Aviation CEO Adam Goldstein

Future of the brain 5YF #19 - Turbocharging the mind, digital drugs, psychedelics, autonomous therapies, the neuroscience frontier, and the future of the brain w/ Neuroscape Founder Dr Adam Gazzaley

Future of ideas and creativity 5YF #18 - Redefining intellectual property, programmable art, AI’s trust layer, South Korea’s rise, and the future of creativity w/ Story Protocol Co-founder Jason Zhao

Future of biosecurity YF #9 - Fighting the next pandemic, masks made of light, germ free buildings, viruses in space and the future of biosecurity w/ R-Zero Systems co-founder Ben Boyer

Future of healthcare 5YF #5 - AI diagnostics, monitoring our voice for disease, the mental health pandemic, and the future of healthcare w/ Kintsugi CEO Grace Chang

Future of fitness 5YF #7 - AI entrenches vertical SaaS, predicting customer behavior, global wellness in 2024, and the future of fitness w/ Mindbody CEO Fritz Lanman

Future of sports and entertainment 5YF #11 - Fans take on billionaire sports owners, athletes as eCommerce companies, 100 Kentucky Derby winners, and the future of sports and entertainment w/ Commonwealth CEO Brian Doxtator

Future of safety and security 5YF #10 - Crime fighting cameras, threat prediction, Large Vision Models, and the future of multi-sensory Ai w/ Ambient Ai CEO Shikhar Shrestha

Future of construction 5YF #8 - Automating profitability in construction, software powered job sites, predicting $15T of spend, and the future of building w/ Briq CEO Bassem Hamdy

Future of real estate 5YF #6 - Sustainable cities, $300M transformation of the Empire State, open source buildings, and the future of real estate w/ Empire State Realty Trust CEO Anthony Malkin

Future of money 5YF #4 - Personalized finance, what's brewing in crypto winter, new game economies, and the future of money w/ Finder co-founder Fred Schebesta

Future of delighting customers 5YF #3 - 100x worker performance, predictive call centers, and the future of delighting customers w/ ASAPP CEO Gustavo Sapoznik

Future of food 5YF #1 - Robotic restaurants, vertical farming, drone delivery, and the future of healthy eating w/ Sweetgreen CEO Jonathan Neman

A HUGE THANK YOU to our visionary guests and to YOU, our listeners. If there are any CEOs you’d like to hear on the podcast, hit reply with your suggestions i’d love to hear from you.

Onwards and upwards!

Future of Code: AI-led Software Development

Hi {{ first name | there }}!

It’s release day! Tune in here 🎧.

Today, we explore the future of developing software in an AI-led world.

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Our world is increasingly written and run in code. Software has created the digital infrastructure powering every industry and has enabled human capital to reach ever increasing productivity. Yet all of the software in the world is created by just a small cohort of developers that represent 0.3% of the global population. What would progress look like if that number grew to 100%?

Jason Warner is one of the most qualified people to talk about software development and how it is transforming with Artificial Intelligence. He believes we have barely seen the start of ‘software eating the world’ and that our ability to produce software is about to explode.

My 5 Year Outlook:

• Everyone will code

• Search replaced by Action

• AI will generate its way out of data scarcity

• Foundation models to become cloud companies

Curious? Read on as I unpack each below 👇🏼

Everyone will code

GitHub’s Co-Pilot, pioneered during Jason’s tenure as CTO, revolutionized how developers interact with code by introducing AI-assisted coding. Today, AI contributes to 40% of code production. While this trend continues, the next significant shift will be moving from developer-led, AI-assisted programming to AI-led, developer-assisted programming. This transition will elevate junior developers and significantly boost software productivity.

However, this advancement benefits only those proficient in coding. More exciting is the prospect of enabling non-developers to code. Enabling the other 99% of the population to build and execute software is a thrilling possibility. With advancements translating code into plain English for documentation, reversing this process to convert English into code is a logical future step.

This shift will democratize software development, dismantling barriers that have historically required specialized training. Imagine a small business owner creating applications to best run their business, a teacher designing new educational tools, an entrepreneur shipping a new marketplace — all without hiring developers. This revolution will accelerate innovation, empower a wide spectrum of individuals, and foster a more inclusive and dynamic technological landscape.

🎧 Listen to our discussion

5YF #20 - AI eats software, specialized models win, billions of coders, synthetic data, NVIDIA and the Hyperscalers, and the future of code w/ Poolside CEO Jason Warner

Search replaced by Action

Beyond democratizing coding, AI’s capabilities can redefine how we interact with technology by replacing traditional search methods with action-oriented requests. Instead of searching for information or tools for us to leverage towards achieving our ultimate goal, we could simply state our end objective, and AI would generate the necessary software on the fly.

For instance, someone needing to analyze sales data could request a specific analysis, and the AI would create a customized program to perform that task instantly. This shift from search to action would streamline workflows, enhance productivity, and provide tailored solutions in real-time.

Jason calls it “full program synthesis”, where a simple prompt in plain english is enough to kick off the software development lifecycle that can be completed end-to-end by the AI model.

It would represent a paradigm shift that will lay down a challenge to search engines like Google as well as traditional software players like Salesforce, which rely on lock-ins with high switching costs. With AI enabling on-demand custom software, the need for expensive, one-size-fits-all solutions diminishes. Users can generate tailored applications, reducing reliance on proprietary systems and long-term contracts. 

AI will generate its way out of data scarcity

Lange Language Models (LLMs) like OpenAI have amazed the world due to the immense amount of data they have been trained on — about 150 trillion gigabytes from the internet. However, improving model performance continuously requires more data, and the quality of the data is crucial.

To address this, OpenAI has partnered with content providers like Reddit, while Elon Musk’s xAI leverages Twitter. These specialized data partnerships are essential and something we will see a lot more of going forward, yet they may not be sufficient alone.

Jason points to synthetic data as a potential solution.

Synthetic data involves generating new data through algorithms that mimic real-world data, overcoming privacy and access issues. This method not only augments existing datasets but also creates diverse, large-scale data for model training. While concerns about diminishing data quality persist, companies like Poolside, which generate substantial original data as their model runs different code executions, demonstrate the potential of synthetic data to unlock new advancements in AI, providing a scalable solution to the data bottleneck.

Foundation models to become cloud companies

As software collapses into the AI model layer and users increasingly come to model companies like Poolside with requests to create software, it opens the question of where will this software be hosted and run? Will the foundation models, who are soliciting the demand for new programs, be best placed to then run those programs for their users and take up the role of cloud providers as well as model companies?

This evolution will likely involve partnerships with existing cloud providers, such as OpenAI and Microsoft Azure or Anthropic and Amazon AWS. This indicates an increasing integration of models and cloud services, pointing to a future where outcomes are requested and delivered end-to-end by a single provider.

This concept isn’t entirely new. We’ve seen it with open-source software like Elastic and MongoDB, where the software itself is open but the hosting, access, and execution are monetized. This business model aligns well with users’ willingness to pay for comprehensive solutions and outcomes.

The future is arriving fast, and with it, an exciting era of integrated AI and cloud services.

Onwards!