Full Tech Ahead

In this episode of "Full Tech Ahead," host Amanda Razani interviews Stefaan Vervaet, co-founder and CEO of Akave. They discuss the major shifts in AI infrastructure, specifically the rise of "Neo Clouds"—specialized GPU-as-a-service providers and compute marketplaces that decentralize compute away from traditional hyperscalers. 

Vervaet highlights that acute GPU shortages and soaring compute/storage costs are forcing enterprises to navigate tight contract commitments (1 to 3 years) for high-end chips like NVIDIA B200s and B300s. 

A primary bottleneck in this multi-cloud environment is data lock-in and steep egress fees imposed by legacy hyperscalers. Akave addresses this by offering a neutral, persistent data storage layer with zero egress fees situated close to GPU clusters. 

This architecture eliminates vendor lock-in, solves the costly "cold boot restart" problem by rapidly restoring snapshots and weights, and integrates with emerging confidential computing frameworks to guarantee sovereign enterprise data security.

Key Quotes

• "Neo clouds are specialized service providers that specifically zoom in on GPUs... compute has now fully... decentralized or disaggregated or distributed."
• "The hyperscalers made it really difficult to move out."
• "A snapshot is worth minimum three hundred hours of engineering hours... The time between your purchasing access to GPUs and the time that you actually can use it, that's extremely expensive."
• "I've never seen such high demand for infrastructure... as of September 2026, it's getting harder and harder to get access to capital and equipment. Start now to lock in your hardware."

Takeaways

• Navigate the Neo Cloud GPU Shortage: Compute is no longer confined to hyperscalers; specialized Neo Clouds and decentralized compute brokers are essential for sourcing GPUs. With pricing doubling and hardware scarce, companies must define whether their workload is inference or fine-tuning early to negotiate long-term hardware allocations before capacity vanishes.
• Keep Data in a Neutral Layer to Prevent Lock-In: Traditional hyperscalers hold enterprise data "hostage" through exorbitant outbound egress fees. Storing proprietary datasets and model weights in a neutral, egress-free storage provider like Akave maintains strategic leverage, enabling frictionless migration across multiple GPU providers and regional sovereign clouds.
• Solve the "Cold Boot Restart" Bottleneck: High-performance local NVMe storage attached directly to GPUs is too expensive for multi-terabyte model persistence. Utilizing a nearby edge object repository to quickly pull snapshots, checkpoints, and model weights saves hundreds of wasted engineering hours and prevents paying for idle GPU rental time.
• Enforce Confidential Computing for AI Sovereignty: Moving proprietary data to third-party Neo Clouds or international co-locations raises valid security concerns. Enterprises should require hardware-level confidential compute (enforced chip decryption only during runtime execution) and maintain dedicated, isolated single-tenant environments ("AI factories") governed by external key management systems.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Federico Ramallo, Founder and CEO of Density Labs. They examine the realities of AI engineering, focusing on business outcomes and managing total cost of ownership (TCO) as token consumption surges. 

Ramallo addresses why roughly 95% of enterprise AI pilots fail to reach production, citing over-reliance on idealized demo scenarios, uncontrolled complexity, and treating AI purely as a software purchase rather than an organizational transformation. 

To deploy non-deterministic AI agents reliably, Density Labs utilizes Eval LLMs, employing an independent secondary model to evaluate the primary model's execution decisions as a "second opinion." 

Paradoxically, Ramallo advocates using as little AI as possible: writing deterministic, traditional code for rule-based workflows and reserving costly LLM calls only as a fallback for subjective, intuitive tasks. 

Finally, he outlines new cybersecurity threats, including prompt-based phishing designed specifically to deceive AI agents.

Key Quotes

• "Density Labs... what we're doing is AI engineering... We take all the hype away and we focus on business impact outcome... We track the total cost of ownership."
• "Roughly ninety-five percent of enterprise AI pilots never reach production... The main reason is that they test the demo with the best case scenario, and then they don't consider all the use cases."
• "Even though I am advocating for the use of AI, I believe that using as little AI as possible is the best approach."
• "Think of AI as a new hire that doesn't have accountability... no agent, no model can be accountable for anything. Only humans can."

Takeaways

• Code the Deterministic, Model the Intuitive: Do not use AI agents to automate entire business workflows blindly. Build standard, deterministic software code for rule-based mathematical steps (which are cheaper, faster, and easier to test), and restrict non-deterministic AI models to subjective tasks like sentiment analysis as a fallback to optimize token costs.
• Implement "Eval LLMs" for Second Opinions: AI models executing operational actions produce non-deterministic outputs. To prevent autonomous errors from propagating, companies should deploy a separate evaluation model to audit decisions before execution, avoiding the echo-chamber risk of a single model evaluating its own work.
• Code Craftsmanship vs. Disposable Software: AI models struggle with complex architectural abstractions, often generating lower-quality code than senior human engineers. However, software development is shifting: long-term code craftsmanship is becoming less critical as agentic tooling makes regenerating and replacing code faster and cheaper, provided specifications are hyper-detailed.
• Defend Against "Agent Phishing": Threat actors now use autonomous agents capable of bypassing traditional CAPTCHAs and behavioral heuristics. Furthermore, attack vectors have evolved from human-targeted phishing to malicious payloads, links, and emails engineered specifically to trick autonomous AI agents into executing unauthorized actions.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Israel Ortiz, VP and General Manager at Accruent. They discuss the major operational crisis currently facing the manufacturing sector: the massive retirement wave of seasoned workers with 20 to 30 years of experience, leaving behind a novice workforce facing a steep knowledge gap. 

Ortiz explains how combining asset management history with modern Internet of Things (IoT) sensor telemetry (measuring vibrations, temperature drops, and power fluctuations) enables AI-driven predictive maintenance. However, he cautions against blind trust in automated signals, emphasizing that senior domain experts must remain in the loop to validate findings. 

By meticulously documenting work orders, cleaning historical data, and embedding intelligent IoT sensors into newer production machinery, organizations can bridge the generational experience gap and achieve significant reductions in unplanned downtime.

Key Quotes

• "The great experienced twenty, thirty-year workforce is deciding to move on to a better life... and that's leaving behind the novices, the newer folks to try to figure out how to close that gap as soon as possible."
• "The smartest technicians out there... always say, 'Oh, I just have to put my hand on that and feel the vibration and I know what to do.' Well, you take someone brand new, there's no way they know... That's what connected IoT does."
• "The future will be us supervising that automation... let technology work for us, but let's always stay in the loop to ensure it's producing what we want."

Takeaways

• Bridge the Knowledge Drain via Asset Data: Seasoned technicians rely on unwritten, tactile intuition. To prevent this tribal knowledge from vanishing upon retirement, companies must rigorously document detailed work orders (annotating problem, cause, and remedy) to build rich historical datasets for AI models.
• Transition to Predictive Maintenance: Integrating AI with IoT sensor streams (vibration, heat, power) shifts operations from reactive fixes or static preventive schedules to dynamic predictive maintenance. In one automotive manufacturing use case, this approach slashed maintenance overhead by 30% and dramatically boosted facility uptime.
• Maintain Human Supervisory Control: Never trust predictive IoT signals blindly. Organizations should implement dedicated review cadences (e.g., weekly telemetry triage sessions) where human operators filter out false alarms and validate AI recommendations before taking equipment offline.
• Enable the Novice Field Technician: The ultimate goal of modernizing manufacturing plants is field empowerment. By scanning a machine's digital tag, a novice technician should immediately receive an AI-synthesized dossier containing past repair history, current sensor telemetry, and step-by-step diagnostic actions.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Kurt Muehmel, Head of AI Strategy at Dataiku. They discuss the recently published "2026 CEO Confessions Report," which gathers anonymous feedback from around 900 global enterprise CEOs. 

Muehmel reveals a notable structural disconnect: while 70% of CEOs state they officially "own" their company's AI strategy, only 60% actually participate in the core AI decision-making processes, which are typically delegated down to CIOs and Heads of AI. 

A major highlight of the report is that 76% of CEOs regret their initial AI vendor choices, feeling dangerously over-dependent on a few select providers. Following recent geopolitical export controls and government bans in June and July 2026 that suddenly restricted model availability, Muehmel stresses the absolute necessity of maintaining "strategic independence." 

He urges leaders to design flexible architectures that allow rapid model switching, reduce token expenditures by shifting optimized base loads to open-source models, and establish robust governance before operational expectations collide with board-level accountability.

Key Quotes

• "Seventy percent of the CEOs that we surveyed say that they own the AI strategy... But then only sixty percent are saying that they participate in a lot of or most of the AI related decisions."
• "Seventy-six percent of CEOs said that they regretted one of the choices, one of the vendor choices that they had made, and were feeling overly dependent on too few AI vendors."
• "AI is increasingly becoming a geopolitical topic... which means that it's critically important for enterprises to be able to choose a model... but then be ready to test and switch quickly."
• "Boards are asking CEOs to defend the AI outcomes faster than companies can actually explain them."

Takeaways

• Bridge the Executive-AI Disconnect: CEOs are forced to be the public and investor face defending AI initiatives to boards, yet they lack granular involvement in implementation choices. Successful organizations close this gap by ensuring top executives are hands-on, everyday users of AI tools to fully comprehend their operational limitations and strengths.
• Maintain Strict Strategic Independence: Geopolitical interventions and export controls make tight coupling with a single proprietary AI vendor a massive enterprise liability. Companies must construct their computing frameworks to remain model-agnostic, allowing seamless backend transitions from one provider to another without rebuilding the entire application stack.
• Optimize via Smaller, Open-Source Models: While token consumption across the global economy will continue to skyrocket, enterprise spending must mature from experimentation to cost optimization. Organizations should use premium frontier models solely for initial prototyping, then shift production workloads to specialized or local open-source models to permanently secure access and slash token costs.
• Prioritize Real-Time Explanability Over Shovel-Ready Slop: Employees will naturally bring unapproved tools past corporate firewalls to clear mundane work debt. Leaders must quickly provide centralized, secure internal agents and copilots that go beyond basic text processing to automate advanced workflows with clear logging, observability, and data-privacy safeguards.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of “Full Tech Ahead,” host Amanda Razani interviews Michele Bavitz, Vice President of Growth at Theorem. They discuss the vital role of automation in digital media ad operations (ad ops) and how it addresses operational complexities across publishers, media owners and streaming platforms.

Bavitz explains that ad campaigns touch multiple internal teams throughout the end-to-end “order-to-cash” lifecycle—including sales, client success, ad ops, IT and finance. This high-volume, multi-platform environment creates massive friction and costly manual errors.

By adopting a hybrid automation model that integrates both tech solutions and human oversight, organizations can eliminate repetitive toil, reduce financial make-goods, cut operational overhead, and allow teams to upskill toward strategic growth, media prospecting and performance analytics.

Key Quotes

• “Theorem... is a digital media organization... Our model is a hybrid where with clients, we are integrating the human element as well as the tech side to bring greater automation.”
• “The end to end process in delivering an ad campaign touches so many teams... It brings a lot of opportunity for things like error, friction with internal teams as well as client teams.”
• “What I’m seeing... is less about job loss and more about upskilling... taking those resources and upskilling them to work on the tech side.”
• “You want to pick the least complex process with the highest impact... Within our experience, we’ve found that the trafficking process within ad operations is the one that has the highest impact.”

Takeaways

Address the Order-to-Cash Friction: Ad operations isn’t an isolated department; ad campaigns flow through sales, account management, finance, and IT. Automating manual handoffs within this complete order-to-cash lifecycle drastically reduces campaign errors, avoids costly client make-goods, and resolves cross-departmental friction.

• Prioritize High-Impact, Low-Complexity Targets: When initiating ad ops automation, gather all departmental leaders to map collective pain points. Start the roadmap with highly repeatable, high-volume operational bottlenecks—such as creative asset trafficking—to yield immediate cost efficiency and team relief.
• Upskill Teams from Trafficking to QA: Automation in ad ops is not a single “switch flip” that eliminates human workers. As automated pipelines take over routine uploads, ad ops roles shift naturally from manual trafficking to strategic Quality Assurance (QA), client prospecting, and data-driven pitch positioning.
• Culture and Leadership Alignment Come First: The biggest obstacle to successful automation isn’t software capability or API integration; it is organizational culture and executive alignment. All leaders across finance, sales, IT, and operations must agree on unified automation goals before deployment.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Ward Balcerzak, Field CISO at Sentra. They explore how AI is fundamentally transforming data security posture management (DSPM) and exposing long-neglected operational hygiene, such as outdated access rights and forgotten data repositories. 

Balcerzak highlights the evolution from basic Large Language Models (LLMs) to fully Agentic AI systems capable of autonomous reasoning. He warns that threat actors are using advanced frontier models to chain together low-and-medium severity vulnerabilities to breach perimeters and exfiltrate IP. 

To counter these AI-driven external attacks and internal data overexposure, Balcerzak advises business leaders to move past "opening the floodgates," establish strict data provisioning based on specific use cases, build trust-based partnerships across departments (Legal, HR, Privacy), and master basic data and identity security fundamentals.

Key Quotes

• "Sentra... we are a data security posture management software vendor. And what that really is, is we're finding where your sensitive data is at, what it is, how it's exposed."
• "AI is exposing things that we forgot about for the last twenty years or we ignored... hygiene, data hygiene, access rights."
• "Frontier models are able to chain exploits together in a way that humans really didn't think about... You need to focus on the mediums and lows, first and foremost."
• "Security leaders... you need to find your champions out there in the organization... Start reaching out... make them your best friends."

Takeaways

• Address Medium and Low Vulnerabilities: Traditional vulnerability management focuses exclusively on high and critical risks. However, threat actors now leverage AI models to string together multiple minor, unpatched exploits into sophisticated breach pathways, making low-and-medium vulnerability remediation mandatory.
• Avoid Opening Data Floodgates: Deploying copilots or agentic AI across an entire corporate dataset by default creates severe overexposure. Companies must restrict training and input data to a minimal, highly specific subset tailored strictly to defined business outputs and permissioned user roles.
• Bridge the Security-Business Communication Gap: Security professionals cannot protect an organization without understanding operational goals. CISOs should establish non-transactional, human relationships with non-technical leaders in Legal, HR, Privacy, and specific business units to identify champions and align security posture with actual daily usage.
• Master Identity and Data Fundamentals: A strong AI defense relies on basic digital hygiene. Organizations must clean up authentication infrastructure (such as Active Directory) to enforce need-to-know access, tokenize or encrypt sensitive data in databases, and run continuous discovery to locate forgotten corporate data assets.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Joe Sykora, CEO of Coro. They discuss the massive cybersecurity challenges facing small to medium-sized enterprises (SMEs) and Managed Service Providers (MSPs) who struggle with limited resources and complex "Franken-stacks"—a fragmented mix of disconnected, multi-vendor security tools stitched together via APIs. 

Sykora outlines how Coro eliminates this complexity by providing a unified platform powered by a single agent and a clean, consolidated dataset running roughly 14 integrated modules. This architecture enables an automated cyber threat remediation rate of 92% to 96%. Sykora notes that cyberattacks have surged 3 to 4 times per client compared to last year due to malicious AI use. 

To address this, he champions operational simplicity, a 100% channel-partner model, and urges security leaders to embrace rapid product cycles and platform consolidation to maximize MSP profit margins and eliminate costly misconfigurations.

Key Quotes

• "Coro is a complete cybersecurity platform... We have a single agent that we provide roughly fourteen different modules... we cover all aspects of cyber."
• "The number one risk is people... but number two is misconfiguration. I've seen a lot of clients through the years have issues with something was misconfigured because they had that 'Franken-stack'."
• "As a provider who gets to see things happen, we've already seen almost four X the number of attacks so far this year than all of last year."
• "If you're not using an AI system, I don't see how you keep up... This is kind of hyperscale when it comes to that."

Takeaways

• Ditch the "Franken-stack" for Data Cleanliness: Relying on separate security tools connected via APIs fails to provide a true "single pane of glass." A unified platform ensures a clean, singular dataset, which is the foundational prerequisite for defensive AI to effectively stop and remediate threats automatically.
• Consolidation Cuts Operational Overhead: For MSPs, the primary cost is not software licensing but backend operational labor. Switching from fragmented tools to a unified architecture allows a single security analyst to manage 100 to 200 clients seamlessly, compared to just 20 or 30 under an enterprise legacy setup, heavily expanding profit margins.
• Defensive AI is Mandatory: With AI-driven phishing and social engineering attacks scaling up to 4X per client in 2026, cyber threats no longer contain obvious grammar mistakes or broken logos. Keeping up with this hyper-scale execution requires immediate adoption of automated, defensive AI ecosystems.
• Embrace Hyper-Fast Change Cycles: The landscape is shifting so quickly that traditional long-term product roadmaps are being superseded by immediate, rolling update cycles. Cybersecurity leaders and developers must remain adaptable and open to constant system refactoring to survive market disruption.

Coro website and social handles:

https://www.coro.net/

https://www.linkedin.com/company/corocyber/

https://x.com/coro_cyber?s=20

https://www.youtube.com/@coro-cybersecurity

Many Thanks to Coro for sponsoring this episode!

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews DJ Spry, Head of Product for Aria Networks. The discussion focuses on the rapidly shifting AI landscape and the critical infrastructure bottlenecks facing organizations building out AI factories and computing clusters. 

Spry introduces the pioneered concept of "Deep Networking," a vertically integrated hardware and software solution that embeds telemetry down to the lowest ASIC level while surfacing insights through natural language agentic AI interfaces. He highlights the stark architectural contrast between the cloud era and the AI era: while cloud applications are loosely coupled and resilient to localized infrastructure failures, AI networking is highly tightly coupled, meaning processing speeds automatically drag down to the slowest component. 

To achieve "time to first token" efficiency and secure a competitive advantage, Spry urges CIOs and CTOs to steer away from costly, prolonged in-house builds and instead leverage specialized commercial expertise and advanced "Neo Clouds" to optimize hardware investments.

Key Quotes

• "Aria Networks... pioneered this concept of what we like to call as deep networking, and that is this vertically integrated solution that has like deep technology all the way down into the... ASICs, the lowest level of the hardware."
• "In cloud, the applications were loosely coupled to the infrastructure underneath them... In AI networking, that is very much the pendulum has swung the other way. These are very tightly coupled systems."
• "I think that there's going to be more consumers of our solutions and our products that don't have heartbeats... products are going to be consumed more and more by agents."
• "A two percent gain [in networking] can give you outsized impact, you know, like ten to twenty percent more token efficiency. So I think that it's worth steel-manning the counter [instead of driving cost to the lowest component]."

Takeaways

• AI Architecture Demands Tight Coupling: Unlike cloud environments where infrastructure failures easily spin up in alternative VPC regions without user disruption, AI training and inference loops are highly tightly coupled. System performance and model completion rates are dictated by the slowest link, making deep, low-latency networking non-negotiable.
• Optimize via Outsized Technical Gains: When calculating infrastructure ROI, looking strictly for the lowest-priced hardware component is counterintuitive. Investing slightly more in hyper-speed networking can generate a tiny 2% infrastructure optimization that yields a massive 10% to 20% surge in enterprise token efficiency.
• Prepare for Non-Human Users: The traditional SaaS metrics of Daily Active Users (DAU) and Monthly Active Users (MAU) must be refactored to account for AI agents. The industry is moving toward a reality where digital workflows run autonomously 24/7 while human teams sleep, shifting software consumption primarily toward agentic workloads.
• Leverage Forward-Deployed Expertise: The specialized engineering skill set required to stand up GPU data centers is severely lacking in the broader enterprise market. Organizations should avoid the trap of prolonged internal tool builds that delay time-to-market and instead utilize forward-deployed commercial specialists to bootstrap systems rapidly.
  
  Speaker Bio: DJ Spry is Head of Product at Aria Networks. He previously served as Senior Director of Product Management at Juniper, leading the product team following the acquisition of Apstra, where he was an early employee. His commercial networking career includes driving GTM for Open Networking at Dell EMC and serving as a consulting engineer at Juniper. He began his career in the US Air Force and later served as a network engineer and architect for the US Intelligence Community.
  
  
  
• Aria website and social handles:
• arianetworks.com
• https://www.linkedin.com/company/aria-networks-inc/
• https://x.com/AriaNetworks
• https://www.youtube.com/@Aria_Networks

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Nidhi Aggarwal, Chief Product Officer (CPO) of HackerOne. They discuss the paradigm shift in cybersecurity risks caused by AI-accelerated software development. Aggarwal introduces HackerOne’s new continuous threat exposure management platform, H1, designed to bridge the "find-to-fix" lifecycle gap. 

She reveals that following the release of advanced AI models, vulnerability report volumes surged by over 90% in April 2026 alone. This influx has dramatically shortened the "zero-day clock", the time between vulnerability discovery and adversary exploitation, from an average of one month down to mere hours or minutes. 

To combat the resulting 25X spike in critical vulnerability backlogs and build up "exposure debt," Aggarwal emphasizes that organizations must abandon seasonal compliance checks in favor of continuous, AI-driven adversarial pen testing combined with human discernment.

Key Quotes

• "Remediation has not kept pace... most CISOs are not looking for more vulnerabilities. Everybody's inundated with vulnerabilities."
• "The zero day clock... has steadily gone down from it used to be about a month last year to a matter of a few hours now in this year with AI."
• "Defense has to operate at that AI offensive scale... We have a concept called exposure debt... you have to think of it like technical debt or something sitting on your balance sheet."
• "The big advice would be offense is defense. So you have to think offensively."

Takeaways

• Automate Defense at Machine Scale: Since generative AI has driven the marginal cost of cyberattacks close to zero, adversaries can now launch massive, automated exploits in under ten minutes. Security defense can no longer operate at human speed; prioritization and remediation must scale up to match offensive AI.
• Manage Your "Exposure Debt": Unremediated high-risk vulnerabilities function like technical debt on an enterprise balance sheet. Organizations must treat this exposure as a board-level risk conversation and design a continuous drawing-down plan rather than letting critical backlogs accumulate.
• Filter out "AI Slop" via Bifurcation: The explosion of automated AI scanning has altered risk distribution. Security teams are experiencing a bifurcation: they are flooded either with informational "AI slop" (false positives that existing controls block) or hyper-critical zero-days. Rapid automated validation is mandatory to isolate true exposure.
• Shift to Continuous Risk-Based Pen Testing: Move away from compliance-driven, checkbox security architectures. True defensive resilience requires automated, 24/7 white-box and black-box pen testing, paired with the creative adversarial judgment of ethical human researchers using AI.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Matthew Shaxted, CEO of Parallel Works. The conversation centers on a major obstacle facing enterprises today: skyrocketing token consumption and the ballooning costs of using frontier AI models. Shaxted explains that opening up unrestricted API access to hundreds or thousands of users leads to rapid budget depletion, citing recent industry examples like Uber. Drawing a parallel to the high-performance computing (HPC) and cloud migration trends over the past decade, Shaxted predicts a cyclical shift: while firms currently rely heavily on public cloud endpoints, economic pressures and massive utilization rates will drive them to bring data workloads back on-premise using increasingly powerful open-weight models (like the recently released GLM 5.2). To combat initial adoption chaos, Parallel Works offers a computing control plane called Activate, providing a single pane of glass to enforce visibility, tracking, and strict "token budgets" that automatically deny requests once expenditure thresholds are met.

Key Quotes

• "Unless [token usage] is thought about in the very beginning in terms of how are you going to control and monitor token usage... it really becomes a big problem. We've seen the Uber story recently, where you burn through the entire budget in a few months."
• "Having strong visibility in where the tokens are going... make sure from the very beginning you have visibility into who's doing what, because that's going to start growing very quickly."
• "As soon as it basically is out of budget, it will deny the request until you get more allotted. That's exactly what's happening."
• "As the open weights get better and better—which I think we're starting to see with GLM 5.2 coming out recently—you can start running open weight models for certain classes of things with much more predictable cost."

Takeaways

• Establish Financial Guardrails Early: Unrestricted enterprise AI access creates a cash burn. Implement central governance and a "computing control plane" from day one to enforce hard spending limits (token budgets) at the user or team level, preventing unexpected tech invoicing.
• Prepare for the On-Premise AI Hybrid Shift: Much like cloud computing evolved, enterprise AI will hit a baseline utilization rate (e.g., 8k/80% load) where renting per-token public APIs becomes economically unsustainable. Companies should plan a hybrid stack that shifts routine tasks to dedicated on-premise infrastructure running open-weight models to cut costs up to 6X.
• Incentivize Token Awareness: End-users rarely optimize resource usage unless faced with explicit constraints. Providing visible token limits encourages developers and practitioners to delegate simpler, mundane prompts to lighter, less expensive internal models rather than burning resources on premium frontier labs.
• Architect for Agentic Workloads: With the industry shifting toward massive agentic systems where hundreds of autonomous agents execute tasks 24/7, compute demands are projected to scale up to 1,000X. Managing this scale without breaking corporate cost structures requires unified virtualization and gateway gateways across cloud and hardware assets.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/