Price:

AI & TECH

Agent infrastructure startups enable autonomous AI by solving data and key access

Wednesday, June 10, 2026 · from 4 podcasts, 5 episodes
Greg IsenbergBanklessThe a16z ShowNo Priors
  • AI agents need direct data access and decentralized private keys to move from chatbots to autonomous operators.
  • Legacy SaaS vendors like SAP are blocking API access to protect their business models.
  • The competitive edge shifts from AI models to the private testing and orchestration layers companies build.

To function autonomously, AI needs hands and eyes. It requires the authority to sign transactions and the context to understand a business. A new layer of infrastructure startups is emerging to provide both, challenging incumbent gatekeepers and redefining how companies operate.

AI agents are treated as new employees, given email addresses and Slack accounts to navigate human-centric workflows. On The a16z Show, George Frazier noted that this “intermediate form” allows agents to interact with legacy systems lacking APIs, bypassing security protocols designed for humans.

“He argues that using a model to remember the capital of France is a waste of parameters. The future belongs to 'unknowledgeable but hyper-intelligent' models.”

- Will Bryk, The a16z Show

Agents need exhaustive data, not curated search results. Exa CEO Will Bryk argues that traditional search, built for human clicks, fails agents who demand comprehensive, high-fidelity information. The architectural shift treats the web as a queryable database, a necessity for complex tasks like competitive analysis or chemical modeling.

However, data access is being weaponized. Frazier pointed to SAP’s recent policy banning unauthorized AI agents from its APIs as the opening salvo in a defensive war. Incumbent vendors fear a “SaaSpocalypse” where AI bypasses their user interfaces to treat software as headless databases. Frazier calls this a doomed strategy that forces a “war with customers.”

“Companies are increasingly treating agents like new hires, giving them dedicated identities, phone numbers, and HR onboarding.”

- George Frazier, The a16z Show

The real value accrues to those who build the orchestration layer. On No Priors, Microsoft CEO Satya Nadella argued the frontier model is commoditizing. Lasting advantage comes from a company’s “harness” - the proprietary wrapper of data, tools, and private evaluation benchmarks that allows seamless switching between AI models.

For crypto-native actions, agents need decentralized keys. On Bankless, Will Price and Flip explained that giving an AI a seed phrase is a security disaster, while a centralized API key contradicts crypto’s ethos. Lit Protocol uses distributed key generation, splitting a private key across a network so agents can sign transactions only when pre-programmed conditions are met.

“Flip says this shifts the industry from asset storage to conditional logic. The primary blockchain users will soon be software, not people, requiring this secure middleware.”

- Will Price and Flip, Bankless

The race is on to build the plumbing for an agentic economy, where competitive speed comes from compressing human oversight into strategic direction and final judgment.

#Agents#Enterprise
Microsoft

Source Intelligence

- Deep dive into what was said in the episodes

Greg Isenberg
Greg Isenberg

Greg Isenberg

Become AI Native in less than 60 mins • Jun 9

  • Theo Taba defines an AI native organization as one where people manage agents, those agents can read and write to company data, and the company gets smarter over time.
  • The core AI native system comprises people, agents, and context. The people manage agents who interface with a shared context layer, which gives agents a comprehensive view of the company's data and operations.
  • In an AI native workflow, AI handles the middle execution work, freeing humans to focus on the strategic beginning and critical review stages. Theo Taba argues everyone essentially becomes a manager of AI agents.
  • Theo Taba outlines a progression for agent autonomy: from basic chat use to requiring manual approvals, and finally to full autonomy. He stresses autonomous agents need clear goals, skills, tools, and rich context to succeed without constant oversight.
  • Skills are markdown files that define specific capabilities for agents, similar to uploading knowledge. Skill chains are sequences of skills executed in order to produce complex, high-quality outputs and reduce AI hallucinations.
  • He states this automated proposal system has generated millions of dollars in revenue for LCA by enabling speed and deep personalization, giving them an edge over non-AI-native competitors in closing deals.
  • Theo Taba advises bootstrapping context by leveraging public resources like Mobbin for design patterns and a company's public design system, then creating skills around them to produce high-quality outputs even without internal data.
  • He posits that building AI-native service firms for specific niches is one of the hottest startup markets. The strategy is to niche down by industry, function, and company size, master those workflows, and use the AI-native system to deliver speed and insight.
  • Greg Isenberg and Theo Taba reference Demis Hassabis's quote at Google I/O: 'Running 100 miles an hour in the wrong direction is worse than standing still,' linking it to the AI-native principle that speed must be directed by customer signal.
Also from this episode: (4)

AI Infrastructure (3)

  • Theo Taba demonstrates a proposal workflow where a skill chain automatically builds a branded microsite, refines the copy, and conducts quality assurance, generating a complete proposal in under five minutes from a trigger.
  • The 'context layer' or 'brain' is a structured repository of company data that gives agents perfect vision of the organization. It involves capturing data from tools like Slack and email, curating it, storing it in a searchable format, and leveraging it for execution.
  • Greg Isenberg highlights that this context allows proposals to incorporate personalized details from past conversations, like a client's analogy about record stores, which would otherwise be forgotten.

Coding (1)

  • In a second demo, Theo Taba uses a voice command and a skill chain to build a functional Spotify feature prototype, complete with a usability test, in under ten minutes. The chain included building, testing, synthesizing feedback, and planning a V2.
#Agents#Startups#Labor#Enterprise
Bankless
Bankless

Is $LIT Cheap? | Will Price and Flip • Jun 9

  • Will Price and Flip argue that AI agents need decentralized keys to own property, not just converse. Centralized API keys defeat crypto's purpose, while seed phrases are a security nightmare.
  • Lit Protocol uses Distributed Key Generation to split a private key across a network. This allows any AI to sign transactions only when pre-programmed conditions are met.
  • Flip says this shifts the industry from asset storage to conditional logic. The primary blockchain users will soon be software, not people, requiring this secure middleware.
  • The custody model is moving from hidden seed phrases to programmable MPC, argues Flip. The next web requires a permissionless security stack, not gated institutional solutions.
  • Lit's architecture combines MPC with Trusted Execution Environments like Intel SGX. The hardware protects computation, while the MPC ensures no single node sees the secret.
  • Will Price says the $LIT token thesis is a 'backbone' play for an intent-centric future. As users express goals instead of manually bridging assets, they need solvers with secure signing authority.
Also from this episode: (3)

Coding (2)

  • This infrastructure reduces user friction. Users can log in with email or social accounts while maintaining self-custody through the network's background key management.
  • Lit is a cross-chain, chain-agnostic utility layer. It generates industry-standard ECDSA and EdDSA keys, letting it sign transactions on any network like Bitcoin, Ethereum, or Solana.

Agents (1)

  • The network captures value as demand for automated signing grows. The volume of signatures needed scales with the adoption of AI agents and smart accounts as dominant user interfaces.
#Agents#AI Infrastructure#Startups#Enterprise
The a16z Show
The a16z Show

Building Search for AI Agents with Exa CEO Will Bryk • Jun 6

  • Will Bryk co-founded Exa to build a perfect search engine, believing it would have profound positive downstream effects across all industries and human life, a mission he has pursued since childhood.
  • Bryk states Exa's small team built a superior search using LLMs, rendering Google's vast human click data advantage largely irrelevant for agentic search. He says this technological shift allowed them to bypass decades of incumbent optimization.
  • He claims that while it's easier to start an agentic search engine today, achieving perfection is far harder, as business use cases demand near-100% accuracy for high-stakes decisions like billion-dollar investments.
  • Bryk argues LLMs will commoditize faster than search. He asserts many knowledge work tasks are fundamentally search problems, and every extra 'nine' of search quality matters more than raw model intelligence for applications like go-to-market intelligence and recruiting.
  • He frames major societal issues like political polarization and loneliness as search problems, claiming accurate, comprehensive information would make reasonable people reasonable and help individuals find compatible communities.
  • For coding agents, Bryk states Exa provides fresher, higher-quality retrieval over technical documentation and SDKs, which reduces errors. He cites Devin by Cognition as a customer that found Exa's search made its agent more accurate.
  • Bryk says search retrieval can alleviate the 'token apocalypse' by letting smaller, cheaper LLMs use tools to act like larger models. Exa's research focuses on extracting only the most relevant document snippets to cut customer token costs.
  • He predicts the agentic search economy will be bigger than Google's ad business by the 2030s, based on extrapolating the number of LLM calls that will require search, which he believes will be orders of magnitude greater than human searches.
  • Bryk's leadership philosophy, influenced by his internship at SpaceX, emphasizes deep detail orientation, memetic mission naming, and ensuring employees work on projects they are passionate about to maintain excitement and high performance.
Also from this episode: (5)

Big Tech (1)

  • Bryk argues Google is optimized for consumer use cases and human clicks, excelling at delivering quick answers but failing at deep, complex queries. He cites his own difficulty using Google to research the lived experience in the Roman Empire.

AI Infrastructure (4)

  • Exa's search engine was designed from day one to serve AI agents, which Bryk claims have fundamentally different needs than humans, including the ability to handle complex semantic queries and demand for comprehensive, not just top-10, results.
  • Bryk claims future bottlenecks will shift from model intelligence to infrastructure for handling massive query volumes and then to data access, as agents will need to query information not currently on the public web.
  • Exa operates like a research lab, applying LLM training techniques like pre-training, post-training, and reinforcement learning to search models. Bryk says serving over 5,000 diverse businesses pushes their research in many directions.
  • Bryk criticizes the current retrieval evaluation ecosystem, noting public benchmarks are maxed and don't reflect agent needs. He says sophisticated customers rely on their own A/B tests, which he claims favor Exa.
#Agents#Models#Startups#Macro

AI Agents and the Fight for Customer Data • Jun 5

  • George Frazier argues AI agents need access to centralized business data to function effectively, creating a new imperative for data consolidation beyond traditional business intelligence.
  • Frazier notes some SaaS vendors like SAP are reacting to AI by restricting API access for agents, viewing them as a threat to their business models. He calls this a bad strategy that harms customers.
  • Frazier contends the fear that AI agents will reduce SaaS seat counts is overblown, as software costs are a minor part of business budgets and AI's primary use is to improve operations, not cut software spend.
  • FiveTran's open data infrastructure website scores vendors on data access policies, giving yellow ratings for charging egress fees and red for actively blocking access. Frazier says SAP has historically been bad while Salesforce was good but is getting 'squirrelly.'
  • Frazier believes the concept of 'data gravity' - that moving data is prohibitively expensive due to egress fees - is fake, arguing change data capture makes data movement manageable and that companies should centralize their data in a platform they control.
  • Frazier observes that early AI agent implementations often give agents human-like identities to slot into existing workflows, but sees this as an intermediate step before more integrated, closed-loop systems.
  • He argues most enterprise agent interactions will happen via APIs, not browser automation, because APIs are faster and consume fewer tokens. He notes Salesforce's CLI is comprehensive enough for agent use.
  • Frazier sees value in Model Context Protocol servers for agents, despite theoretical redundancy, because they solve practical problems like authentication and tool discoverability in real systems.
  • He disagrees with predictions of a 'SaaSpocalypse,' arguing the bigger threat is AI-native startups out-innovating incumbents. He notes established companies like FiveTran are accelerating, not slowing, despite market uncertainty.
  • Frazier says AI coding agents are an 'infinite supply of junior engineers' that help FiveTran troubleshoot its 750+ data connectors, leading to incremental quality improvements rather than replacing core engineering.
  • He states AI labs like OpenAI and Anthropic use FiveTran for typical data consolidation and analytics, building data foundations that look like any other modern company's, not exotic new systems.
  • Frazier argues AI is creating more demand for infrastructure, not commoditizing it, though it may threaten the highest 'consumption layer' of user-friendly platforms as agents can navigate more complex systems directly.
Also from this episode: (3)

Enterprise (1)

  • He advises companies to write data access guarantees into Master Service Agreements with large vendors, noting FiveTran provides model language for this. He says vendors often back down when challenged.

Coding (2)

  • He claims PostgreSQL is outdated technology with a poor storage engine, and that undergraduates in database courses write better storage engines. He believes the world should create a new operational database instead of endlessly repackaging Postgres.
  • Frazier's main existential worry for FiveTran is AI coding agents becoming so good at writing data connectors that customers shift to DIY. His biggest excitement is AI creating a massive new set of use cases for organized data.
#Enterprise#AI Infrastructure#Macro#Agents
No Priors: Artificial Intelligence | Technology | Startups
No Priors: Artificial Intelligence | Technology | Startups

The Rise of the Full-Stack Builder and Hyper-Leveraged Generalist with Microsoft CEO Satya Nadella • Jun 4

  • Satya Nadella defines a successful platform by its ability to create more value outside itself than it captures internally.
  • The Microsoft stack centers on a multi-model harness that combines models, tools, and a rich context layer, which they argue proves more performant in real-world tasks than isolated model training.
  • Nadella sees a durable role for SaaS vendors, but their data models and business logic must be unbundled and made available for composition into new agentic workflows. Inflexible vendors will struggle.
  • Nadella states Microsoft built more Azure data center capacity in the last 15 months than in its first 15 years. This forced a reconceptualization of infrastructure management toward agentic systems.
  • The industry must prove AI's broad economic benefits to earn societal permission, argues Nadella. This requires demonstrating real community impact through jobs, tax base, and responsible resource use.
  • Nadella believes true ambition with AI is making the impossible possible, not just making hard tasks easier. This requires organizations to develop new conceptual models of what work can be.
  • AI could drive a restructuring of engineering roles, elevating hyper-leveraged generalists who can translate knowledge work into building apps, while also creating deep needs in infrastructure and RLE design.
  • Nadella predicts the next major startup opportunity could be a new university or pedagogy that rethinks curriculum, credentials, and economic opportunity for an era where information access is transformed.
Also from this episode: (5)

Models (1)

  • Microsoft's MAI model strategy emphasizes a clean lineage from pre-training with high-quality data, disciplined ablations, and a strong reasoning core. This enables small models to effectively hill climb on specialized tasks.

Enterprise (1)

  • Nadella argues a company's private evaluation datasets and the traces from agentic workflows will become core IP, more critical than any single external model.

Coding (1)

  • Coding AI has proven so effective it is forcing a rebuild of the IDE to manage the cognitive load of multi-agent sessions, shifting from chat interfaces to canvases.

Agents (1)

  • Nadella predicts a near-term rise of long-running autopilot agents that handle delegated work overnight, requiring new tools for humans to review and supervise their activity.

Business (1)

  • He identifies per-user subscription and consumption-based pricing as the dominant near-term models, viewing pure outcome-based pricing as problematic because customers often balk at sharing revenue.
#Agents#Big Tech#Enterprise#AI Infrastructure#Labor

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