Price:

AI & TECH

Enterprises build AI tools to cement competitive moats

Wednesday, June 3, 2026 · from 4 podcasts, 5 episodes
AI Daily BriefThis Week in StartupsThe a16z ShowNo Priors
  • Leaders like Ramp build internal AI platforms for strategic control and workflow compounding, turning productivity into a competitive moat.
  • AI's value hinges on enterprise-wide coordination; isolated gains create organizational chaos without a central system.
  • Security and data control demands are driving companies away from vendor solutions and toward bespoke, independent oversight.

Enterprise AI is shifting from vendor procurement to in-house engineering. The winners are no longer just buying off the shelf; they're building proprietary platforms to lock in compounding advantages.

According to Nathaniel Whittemore on The AI Daily Brief, PwC data shows 20% of companies capture 75% of AI's economic gains. McKinsey found these leaders see a 20% EBITDA uplift and recoup investment within two years. Their edge isn't the technology itself but the enduring capabilities built around it. As Whittemore puts it, leading firms use AI for business model reinvention, not just efficiency.

"Internal AI infrastructure is now a core business need. You do not hand your competitive advantage to a vendor."

- Eric Glyman, The AI Daily Brief

Ramp exemplifies the model. Co-founder Eric Glyman says 99% of their employees use AI daily through an internal workspace called Glass, pre-loaded with 350+ reusable skills. The system turns one employee's breakthrough into the company's baseline. Seb Go argues internal productivity is a moat, ownership allows same-day fixes, and solving internal problems directly informs their external product development.

Coordination is the choke point. George Zarkadakis warns that while AI makes individuals 10x more productive, it hasn't made companies 10x more valuable. Without a system to align outputs, you get thousands of agents rowing in opposite directions. This creates a standstill.

Security pressures accelerate the in-house trend. Onyx Security CEO Maxim Bar Kogan argues legacy tools are blind to AI intent. A security system sees a 'delete' command as legitimate; it can't distinguish between a cleanup and a hallucinated catastrophe. Enterprises fear handing historical agent data back to labs like OpenAI for auditing, wary it will fuel future model training. Bar Kogan notes this structural distrust makes independent oversight crucial.

"Today’s security stack is effectively blind to the 'why' behind agentic actions."

- Maxim Bar Kogan, No Priors

The endgame is enterprise-wide coordination layers that operate like a nervous system. Happy Robot, which works with nine of the top ten US freight brokers, solves logistics by using AI as a “soft API” to bridge communication gaps across emails, calls, and fragmented systems. Co-founder Pablo Palafox says they’re now being pulled into telecom and insurance because the core problem - enterprise coordination - is universal. Luis Parag argues deploying agents to execute work is the best way to clean messy enterprise data, as agents consistently populate systems where humans fail.

The move is clear: competitive advantage now lies in owning the harness, not just renting the model.

#Agents#Enterprise#Big Tech
Nathaniel WhittemoreOpenAIPwC

Source Intelligence

- Deep dive into what was said in the episodes

The AI Daily Brief: Artificial Intelligence News and Analysis
The AI Daily Brief: Artificial Intelligence News and Analysis

Nathaniel Whittemore

Should Americans Get Shares in AI Companies? • Jun 2

  • PwC found that 75% of AI's economic gains are captured by just 20% of companies.
  • Nathaniel Whittemore says the AI-leading companies use AI for growth and business model reinvention, not just efficiency. They are 2 to 3 times more likely to pursue growth opportunities and 2.6 times more likely to report AI improves business model reinvention.
  • McKinsey's AI transformation manifesto argues that technology alone doesn't create advantage, but enduring capabilities built around AI do. They studied 20 AI leader companies and found AI transformations delivered a 20% EBITDA uplift on average.
  • McKinsey states AI leaders recoup their investment in 1 to 2 years and generate $3 of incremental EBITDA for every $1 invested.
  • McKinsey argues more than 70% of talent for AI should be in-house, as every tech transformation is ultimately a people transformation. They also state data is the constraining factor in most organizations.
  • George Zarkadakis argues institutional AI is not just aggregated individual AI but requires coordination layers to align outputs. He warns that without coordination, individual AI use creates organizational chaos and standstill.
  • Ramp co-founder Eric Glyman states 99% of the company uses AI daily but most were stuck due to painful setup. This led them to build an internal AI workspace called Glass, which comes pre-configured with 350+ reusable skills.
  • Seb Go to Jen's essay on Ramp's Glass argues the models are good enough, but the harness isn't. The system's design principles are to not limit anyone's upside, make one person's breakthrough everyone's baseline, and embed enablement into the product itself.
  • Ramp's Glass system includes a memory and context engine that synthesizes user data every 24 hours from Slack, Notion, and Calendar to pre-load context into AI sessions. It also features scheduled automations and an AI guide called Sensei to recommend skills.
  • Seb Go to Jen lists three reasons Ramp built Glass in-house: internal productivity is a competitive moat, owning the tool allows for faster fixes, and solving internal problems directly informs their external product development for finance teams.
  • Seb Go to Jen concludes that the biggest learning was that users who installed a skill on day one and got a result learned faster than from training sessions. Every feature in Glass is designed as a secret lesson, accelerating learning by doing.
  • Nathaniel Whittemore argues agentic engineering is becoming the work of everyone in leading organizations, not just a software domain. He cites Guillermo Rauch's announcement that Vercel is open-sourcing a reference platform for cloud coding agents.
#Agents#Enterprise#Startups

How to Use /Goal to Do More With AI • May 31

  • Nathaniel Whittemore cites a PwC study showing 75% of AI's economic gains are captured by only 20% of companies, marking a widening performance gap.
  • Whittemore reports AI-leading companies are 2-3x more likely to use AI for growth opportunities and 2.6x more likely to report AI-enabled business model reinvention, according to PwC.
  • McKinsey's AI transformation manifesto identified 12 themes separating leaders from laggards, arguing AI leaders focus on economic leverage points, not just efficiency.
  • McKinsey found AI transformations in leading companies delivered a 20% EBITDA uplift, broke even in 1-2 years, and generated $3 incremental EBITDA for every $1 invested.
  • George Zarkadakis argues institutional AI requires coordination layers to align individual AI use, as uncoordinated AI adoption creates organizational chaos.
  • McKinsey contends over 70% of AI talent should be in-house, as AI transformation is fundamentally a people transformation, not just a tech implementation.
  • Ramp built its internal AI workspace Glass to solve coordination problems, auto-configuring connections to all company tools via SSO so agents operate with full organizational context.
  • Seb Go argues the biggest failure mode in AI adoption is isolation, where one employee's discovered workflow doesn't help others, which Ramp solves with its Dojo marketplace for reusable agent skills.
  • Ramp's Dojo marketplace contains over 350 reusable AI skills, with an AI guide called Sensei surfacing the most relevant five skills for a new employee based on role and tools.
  • Seb Go states Ramp built Glass in-house because internal productivity is a moat, ownership allows same-day fixes, and solving internal problems directly informs their external AI products.
  • Whittemore notes OpenAI reported 50% of Codex usage is not about coding, highlighting AI's broader utility for knowledge work beyond software engineering.
  • Ryan Carson predicts complete end-to-end code factory solutions from major AI players by year-end, eliminating the need to duct-tape disparate tools together.
Also from this episode: (1)

AI & Tech (1)

  • Vercel CEO Guillermo Rauch announced the company is open-sourcing a reference platform for cloud coding agents, following patterns used by Stripe, Ramp, Spotify, and Block.
#Enterprise#Agents#Reasoning#Startups
This Week in Startups
This Week in Startups

This Startup Fused Human Brain Cells with Silicon Chips | E2295 • Jun 1

  • Cortical Labs is building a second data center in Singapore with Day One Data Centers, planning to deploy up to 1,000 units.
Also from this episode: (7)

AI Infrastructure (2)

  • Cortical Labs has deployed 120 CL1 biological computing units across six server racks in Melbourne, calling it the world's first biological data center.
  • The CL1 unit consumes about 30 watts of power, requires nutrient feeding, and uses filtration cartridges that need replacement every four to six months.

Biology (1)

  • The CL1 unit houses 200,000 to 2 million neurons, compared to approximately 100 billion in a human brain.

Models (1)

  • Cortical Labs' biological neurons demonstrated 5,000 times greater sample efficiency than GPU-based systems in reinforcement learning tasks.

Startups (2)

  • The company's first production run of 30 CL1 units sold out at a price of approximately $35,000 each, generating roughly $1 million in revenue.
  • Michael notes manufacturing components in the US costs roughly 2x more than in China for Pike Aerial, but owning the design mitigates cost.

Safety (1)

  • Han states the company has drawn an ethical red line at not creating conscious systems, as they have the capacity to suffer.
#AI Infrastructure#Biology#Robotics#Regulation#Energy
The a16z Show
The a16z Show

Building AI Agents for Enterprise Operations • Jun 1

  • Pablo Palafox says Happy Robot started by solving enterprise coordination problems in logistics, not just supply chain issues, after seeing a co-founder manually track olive oil shipments.
  • Luis Parag explains the company's early focus was the technical challenge of building a realistic voice agent for phone negotiations, which required fine-tuning models like Mistral and Llama for speed and building custom agent infrastructure.
  • Happy Robot's current enterprise customer base includes nine of the top ten US freight brokers, seven of the top ten tracking companies, and two of the largest ocean carriers.
  • Luis Parag argues the biggest problem for voice AI is conversation handling, not latency or realism. Agents must know when to talk, handle interruptions, filter background noise, and reason asynchronously.
  • Pablo Palafox states their agents now handle complex, cross-functional workflows like freight forwarding customer support, which requires coordinating actions across airlines, emails, and phone calls to track shipments.
  • Luis Parag says their platform uses a deterministic approach for tasks like rate negotiation, where an agent requests permission from an external tool instead of being exposed to maximum rates, to prevent hallucination.
  • The company runs collections campaigns for a large supply chain customer, making 20,000 to 50,000 daily outreach calls to recover duties on parcels.
  • Pablo Palafox describes a 'pyramid of work' where simple, high-volume tasks form the base, but the real economic value is in complex, strategic decisions at the top, which require shared context across functions.
  • Luis Parag argues that deploying agents to execute work is the best way to clean and connect enterprise data, as agents consistently populate systems, unlike humans who forget or make errors.
  • Happy Robot uses a forward-deployed engineering team to scope and build solutions, which Pablo Palafox says acts as an extension of the product team to capture customer context and accelerate implementation.
  • The company's work with DHL involves over 40 agents deployed across 80 countries, sharing context across regions and functions, which revealed they were solving a general enterprise coordination problem.
  • Pablo Palafox says they are now being pulled into telecommunications, utilities, and insurance because these industries face similar coordination problems between customers, partners, and employees.
  • Luis Parag states they focus on problems where communication is the interface to the external world, like voice, email, or web browsing, and where standard operating procedures are unclear or highly contextual.
  • Pablo Palafox emphasizes the importance of a human-like experience, noting that even when disclosed, end-users quickly forget they are talking to an AI if the conversation flows naturally and helpfully.
#Agents#Enterprise#AI Infrastructure
No Priors: Artificial Intelligence | Technology | Startups
No Priors: Artificial Intelligence | Technology | Startups

Building an AI Guardian for Enterprise with Onyx Security CEO Maxim Bar Kogan • May 28

  • Onyx Security trains models and builds agents to oversee other AI agents, aiming to detect illegitimate actions as enterprise AI adoption grows exponentially.
  • Maxim Bar Kogan states autonomous agents like coding assistants are the fastest-growing category in enterprises, outpacing low-code automation platforms.
  • Kogan argues existing security tools like identity management and API monitoring lack the context to understand the intent of flexible AI agents, creating new control gaps.
  • Onyx's approach uses small, specialized models to efficiently flag high-risk AI actions, reserving more powerful analysis only for critical moments to manage cost and latency.
  • Kogan sees Mithril-level AI models dramatically lowering the cost of vulnerability discovery, forcing security teams to implement foundational controls quickly.
  • He believes independent oversight is crucial because enterprises distrust vendors auditing their own products and need solutions adaptable to multiple AI providers.
  • Kogan notes enterprises are unwilling to share historical agent behavior data with Anthropic or OpenAI due to those companies' data-hungry training practices.
  • Auto-GPT's early demonstration of autonomous agents ignited market imagination, highlighting the potential and risks of AI performing complex computer tasks.
  • Kogan asserts the core challenge of AI oversight is interpreting agent intent, not just proxying data, which requires understanding what AI systems 'think'.
  • Onyx's founding insight was the need to control increasingly smart AI agents, especially as they begin managing critical infrastructure like power grids.
Also from this episode: (4)

Startups (1)

  • The Israeli tech ecosystem excels at understanding security team workflows and building products tailored to their daily operational needs, according to Kogan.

AI & Tech (3)

  • He predicts mechanistic interpretability of AI models will advance significantly as smarter AI systems emerge, aiding in understanding and controlling intelligence.
  • Financial institutions like JP Morgan adopt AI cautiously due to high risk profiles, contrasting with startups that aggressively deploy agents to gain competitive edge.
  • Kogan advocates for gradual, controlled release of Mithril-level AI models to allow enterprise security teams time to develop defenses and prevent catastrophic failures.
#Agents#Enterprise#Safety#Startups

Related Stories