Why the Physical World Is Now the Biggest Cybersecurity Vulnerability in Most Organizations - and Why AI Changed Everything

Guest: John Gallagher, VP Viakoo Labs at Viakoo

Host: Seth Earley, CEO at Earley Information Science

Published on: September 28, 2026

In this episode, Seth Earley speaks with John Gallagher, VP of Viakoo Labs at Viakoo, a company that has spent 12 years building automated cyber hygiene for IoT and OT systems across millions of devices. They explore why the physical world - cameras, building automation, water infrastructure, manufacturing equipment - is now the most underprotected attack surface in most organizations, how AI has eliminated the obscurity that passively protected these systems for decades, why ransomware has shifted from stealing data to shutting down operations, why human-in-the-loop is a hard line that can never be crossed in OT remediation, and what it actually takes to build a digital twin of every device in an enterprise environment and use it to match the speed of AI-driven threats.

Key Takeaways:

• IT security investments do not transfer to OT and IoT environments - the 150,000 operating systems, device types, and tightly coupled application relationships require a completely separate approach.
• AI has eliminated security through obscurity - threat actors who previously could not justify the effort to attack niche OT systems can now use AI to read manuals, understand device architectures, and find vulnerabilities at scale.
• Ransomware has shifted from stealing data to shutting down operations - holding a manufacturing line or energy facility hostage is a fundamentally different and more consequential threat than data exfiltration.
• Organizations typically have 5 to 20 times more OT and IoT devices than IT systems - the attack surface is orders of magnitude larger than most executives realize.
• Threat detection in OT is now a solved problem - the unsolved problem is remediation at scale, which requires AI-assisted preparation and autonomous execution with a human in the loop for every deployment decision.
• A digital twin of every device - tracking make, model, firmware, memory state, configuration, and change over time - is the foundational knowledge layer before any security action can be taken.
• The defense is not a smarter model - it is knowing what you own, what connects to what, what breaks when you touch it, and who is accountable. That is an inventory problem, a normalization problem, and an information architecture problem.

Insightful Quotes:

"AI has brought what we call the inversion. It's taken things like remediation - firmware, password, certificate updates - and said, if you were okay updating even a few hundred devices manually a few times a year, those days are gone. It's a daily occurrence now, and you have to match AI speed." - John Gallagher

"Will we ever put AI in charge of deploying that firmware update? Never. That is a hard line. There is a strong argument that AI never should be put in a decision-making process in this environment without a human in the loop." - John Gallagher

"For decades, devices running on the physical side of business were protected by the fact that they were too obscure to be worth an attacker's time. That protection was never a strategy, and AI has erased it. The defense is not a smarter model - it is the knowledge of what you own, the firmware, what connects to it, what breaks when you touch it, who is accountable." - Seth Earley

Tune in to discover why IoT and OT security is the most consequential and most underestimated cybersecurity challenge in the enterprise today - and what the path from awareness to remediation at scale requires.

Links

LinkedIn: https://www.linkedin.com/in/b2bpipelinebuilder/

Website: https://www.viakoo.com/company/

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

Why Making AI More Biological May Be the Most Consequential Development in the History of Computing

Guest: Alex Ksendzovsky, CEO and Co-Founder at The Biological Computing Company

Host: Seth Earley, CEO at Earley Information Science

Published on: August 14, 2026

In this episode, Seth Earley speaks with Alex Ksendzovsky, CEO and Co-Founder of The Biological Computing Company, a neurosurgeon and neuroscientist who spent nearly two decades studying how the brain processes information - including implanting electrodes into human brains to understand epilepsy and growing neurons in a dish to study them at the molecular level. They explore why the AI field diverged sharply from biology in the 1980s and what was left behind, how TBC grows real brain cells on electrode arrays to derive mathematical principles that improve AI algorithms, what a 13-20% improvement in video generation quality and a 4-5x efficiency gain means against an industry where 1-2% counts as significant, and where biological computing is headed in the next decade and beyond. This is one of the most technically ambitious and genuinely novel conversations the podcast has had.

Key Takeaways:

• AI diverged sharply from biology in the 1980s when backpropagation was introduced - it produced highly performant systems but at the cost of massive energy inefficiency that the brain solved hundreds of millions of years ago.
• TBC grows hundreds of thousands of neurons on electrode arrays with 4,096 electrodes, encodes information as electrical patterns, and derives mathematical principles from how those neurons actually process and represent that information.
• The adapter products built from these biological principles plug into existing transformer architectures and produce 13-20% improvements in video quality metrics where a 1-2% improvement is considered publication-worthy.
• On efficiency, TBC's adapters currently produce a 4-5x improvement in frames per second - and when combined with existing optimization strategies, the two approaches are synergistic rather than conflicting.
• The catastrophic forgetting problem - AI's inability to learn continuously without losing what was previously learned - is one TBC is directly attacking by studying how biological synapses change during closed-loop learning and deriving new learning rules from that process.
• The brain is millions of times more efficient than silicon; even capturing a minuscule portion of that through biologically-derived principles has produced gains that suggest the ceiling for this approach is enormous.
• The ethical framework is clear: the cultured neurons used in TBC's experiments are fundamentally different from a brain - lacking the three-dimensional structure, scale, and emergent properties associated with sentience - and TBC actively works with bioethicists to maintain those guardrails.

Insightful Quotes:

"Moving forward past the 1980s into 2026, you have extremely performant AI systems, but they're being trained with brute force and they're extremely inefficient. At TBC, we think the reason for this is because they became extremely non-biological." - Alex Ksendzovsky

"Just making it a tiny, tiny bit more biological reached these massive gains. It's a testament to the complexity of how the brain operates, and the more of these principles and primitives we can derive and apply, the more improvements we'll get in terms of performance and efficiency." - Alex Ksendzovsky

"The gap is not a coincidence. It's a result of hundreds of millions of years of evolution solving the same problems that we are now trying to solve in silicon." - Seth Earley

Tune in to discover why biological computing may be the most consequential and least-understood frontier in AI infrastructure today - and what it means for the energy crisis that is already shaping every data center investment being made.

Links

LinkedIn: https://www.linkedin.com/in/alexander-ksendzovsky-31732711/

Website: https://www.tbc.co

Blog: https://www.tbc.co/blog

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

Why Applying AI to Drug Development Is One of the Most Technically Demanding Problems in the Industry - and What Is Finally Making It Solvable

Guest: Patrick Leung, Chief Technology Officer at Faro Health

Host: Seth Earley, CEO at Earley Information Science

Published on: August 4, 2026

In this episode, Seth Earley speaks with Patrick Leung, Chief Technology Officer at Faro Health, who spent over a decade at Google including working on Google Duplex before bringing that technical depth to one of the most regulated and high-stakes domains in medicine. They explore why generative AI is in the trough of disillusionment in pharma, what the vibe coding fallacy costs organizations that believe they can build clinical software by prompting, how classical machine learning models and modern LLMs are working together to forecast trial outcomes, and why every day of clinical trial delay can cost up to half a million dollars in lost revenue. Patrick shares candid and specific insights on prompt injection as the new SQL injection, why human experts cannot be removed from clinical AI workflows, and what bending Eroom's Law would mean for patients worldwide.

Key Takeaways:

• Generative AI is in the trough of disillusionment in pharma - the initial hype that AI could automate entire clinical processes has collided with the real complexity of the domain and the limits of the technology.
• Vibe coding hits an event horizon of complexity - demo apps are achievable by prompting, but real enterprise software requires proper engineering, security design, testing discipline, and architectural decision-making that AI cannot replace.
• Prompt injection is the new SQL injection - any tool that uses AI to process user input is now vulnerable to a class of attacks that did not exist before, and most organizations are not yet protecting against them.
• Classical machine learning models and modern LLMs are more powerful together than either is alone - survivor curve models from insurance analytics proved directly transferable to clinical trial forecasting with strong results.
• Every day of clinical trial delay can cost up to half a million dollars in lost revenue - and a typical amendment forcing a trial redesign and resubmission runs three to six months.
• Generic general-purpose models cannot replace domain-specific knowledge engineering in clinical contexts - the claim that they can is an easy sales pitch that does not survive contact with the actual complexity of the problem.
• The goal is not to automate clinical professionals out of existence but to remove the rote and repetitive work so they can focus on the judgment calls that only they can make.

Insightful Quotes:

"There's no escaping the fact that you need to test software. There's no escaping the fact that you need to have specs that are really well thought out. As you add more features to a codebase, it gets more complex and unwieldy and difficult to maintain. You can't vibe code your way out of those key design decisions." - Patrick Leung

"I found myself applying models I'd learned about in a completely different domain. Survivor curve models we used for predicting insurance policy claims worked pretty well when applied to clinical trials. Transferability is really a thing." - Patrick Leung

"Eroom's Law is not sustainable. Any exponential increase in cost is not sustainable by definition. So we want to bend Eroom's Law - and hopefully reverse it. Why not?" - Patrick Leung

Tune in to discover why AI in clinical drug development is one of the hardest and most consequential problems in the field - and what is finally making it tractable.

Links

LinkedIn: https://www.linkedin.com/in/puiwah/

Website: https://www.farohealth.com

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

Why Making Complex Revenue Simple at Scale Requires More Than Throwing Contracts Into a Chat Interface

Guest: Deepak Bapat, Co-Founder and CTO at Tabs

Host: Seth Earley, CEO at Earley Information Science

Published on: July 30, 2026

In this episode, Seth Earley speaks with Deepak Bapat, Co-Founder and CTO at Tabs, a revenue and accounts receivable management platform built for B2B companies. They explore why dropping contracts into a general-purpose AI tool is not a strategy for enterprise scale, what generative AI unlocked that OCR and legacy machine learning could never solve, why context engineering beat fine-tuning for contract extraction, and why newer and larger models are not always better for specialized tasks. Deepak shares candid and specific insights on building atomic AI pipelines, the provability requirement that financial compliance demands, and what finance and data leaders consistently underestimate before deploying AI on their contracts.

Key Takeaways:

• Dropping contracts into a chat interface is a reasonable experiment but not an enterprise strategy - doing things at scale requires specific tooling, specific expertise, and integration across systems.
• The SaaSpocalypse framing misses the point - the more interesting question is not whether chat replaces UI, but how platforms can understand intent and preempt the actions users would otherwise have to click through manually.
• Generative AI solved the contract problem by reasoning over ambiguous natural language at document level - something OCR and rules-based systems fundamentally could not do.
• Context engineering beat fine-tuning at Tabs because merchant preferences vary so significantly that fine-tuning per merchant became cost-prohibitive - a well-prompted generalized model proved faster and more elastic.
• Newer and larger models are not always better for specialized tasks - Deepak's eval sets show that models from six months ago outperform newer versions on certain contract extraction jobs, likely due to overfitting on coding.
• Provability is the non-negotiable requirement in financial AI - it is not enough to produce correct output, you must be able to prove the output is correct and traceable back to the source contract.
• Organizations that want to deploy AI on their contracts first need to standardize internally on what outcomes they actually want - two people on the same team asking the same question about the same contract should not produce two different answers.

Insightful Quotes:

"The misconception is that difficult problems can just be solved by throwing something into ChatGPT and having the answer come out the other side. In our case, the at-scale piece is everything. Those intelligence tools are still individualized tools - to do things at scale for an entire enterprise still takes specific tooling, specific thought, and specific expertise." - Deepak Bapat

"What we're trying to do is move from a place of unstructured data to provable and correct structured data. That is what Tabs is built around - and that is what most of these other systems simply cannot handle." - Deepak Bapat

"When you think about the legacy players that were more rigid SaaS tools with manual entry and brittle connectors - what was intractable about that model is exactly what generative AI made solvable. The ability to reason over the words in a document, understand what they meant, and understand what the output should be - that changed everything." - Seth Earley

Tune in to discover what it actually takes to build AI that is accurate enough, auditable enough, and elastic enough to handle enterprise revenue data at scale - and what most organizations underestimate before they start.

Links

LinkedIn: https://www.linkedin.com/in/deepakbapat/

Website: https://www.tabs.inc

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book