About this channel
VentureBeat explores what AI looks like inside businesses, products and engineering teams. Its coverage moves between applications in sectors such as travel, retail and pharmaceuticals and the less…
VentureBeat explores what AI looks like inside businesses, products and engineering teams. Its coverage moves between applications in sectors such as travel, retail and pharmaceuticals and the less visible systems needed to make agents useful. Questions about latency, evaluation and coordination give the material a practical focus: an impressive model is only one part of putting an AI system to work.
Company examples and conversations with technology leaders provide much of the framing. An Asana episode asks why MCP alone does not solve orchestration, while an Expedia discussion puts evaluations at the centre of defining product requirements. Another piece examines why an AI assistant can feel slow, connecting the pace of a model's reasoning with the infrastructure around it. Across these subjects, the channel highlights the gap between a capability demonstrated in isolation and an experience that works inside an organisation. Videos about how much engineering time is spent coding bring a similar question to software development.
The selection also includes stories about drug design, retail insights and autonomous driving, alongside an event-series preview. These broaden the view from engineering details to the ways organisations describe AI's impact. Technology managers, product teams and developers can use the channel to compare adoption questions across different settings: how success is evaluated, how tasks are coordinated and which parts of a workflow change. It is especially suited to viewers who want business context around technical discussions and concrete organisational examples around broad claims about AI. The recurring point of interest is the work required to turn a promising system into something a team can use.
Latest episode
19 Days Without Fable: The Enterprise Hedge Plan
66% of enterprise AI teams are actively engineering toward zero human oversight — but only 4.5% fully trust their automated evals. Half have already shipped an agent that passed evals and still…
66% of enterprise AI teams are actively engineering toward zero human oversight — but only 4.5% fully trust their automated evals. Half have already shipped an agent that passed evals and still failed a real customer.
VentureBeat's latest research exposes a dangerous gap between deployment ambition and evaluation maturity. The most-used eval platform is OpenAI's native tooling. The second most common answer: no dedicated eval platform at all. Meanwhile, 51% of enterprises are relying on a hyperscaler's built-in guardrails as their primary AI security layer — not a deliberate security architecture.
Fable's 19-day market absence changed the conversation. Enterprises that had built production workflows on frontier models got a wake-up call about single-vendor dependency. The result: GLM 5.2 is gaining serious developer traction as an open alternative competitive with Opus 4.8, and Tencent's new Hai 3 — less than half the size — is drawing attention for agentic workloads that don't require cutting-edge coding performance. Sam and Matt break down why companies like Shopify, LinkedIn, and Pinterest were already on open models, and why others are now catching up fast.
On infrastructure: neo-clouds are currently used by just 2% of respondents, but 45% name them as their top planned investment area over the next 12 months. The market hasn't calcified. Matt and Sam preview what they expect to hear at VentureBeat Transform from engineering leaders at Intuit, Brex, Instacart, and Mastercard — including how Intuit bootstrapped and pivoted its agent architecture mid-build, and why agent conversations are generating higher-quality behavioral data than traditional user research ever did.
🎙️ HOSTS: Matt Marshall · Sam Witteveen | VentureBeat
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If you enjoy these conversations, you need to be in Menlo Park this July.
VB Transform 2026 is VentureBeat's flagship enterprise AI event, built entirely around one question: How do you orchestrate AI autonomy at scale? July 14–15, Hotel Nia. Real projects, proprietary research, no fluff.
50% off for listeners with code BEYONDTHEPILOT: https://bit.ly/4fK4F6z
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Chapters:
00:00 Research data drop: eval gaps, zero-HITL targets, and hyperscaler security dependency
01:00 Chinese models breaking into enterprise — developer fad or structural shift?
02:00 GLM 5.2 and Tencent Hai 3: what they do well and where they fall short
03:20 How Shopify, LinkedIn, and Pinterest are already running open models in production
05:40 Fable's 19-day outage: why 19 days changed enterprise AI vendor strategy
07:00 Intuit's agent build: bootstrapping, mid-build pivots, and real-time behavioral data
08:40 Neo-cloud infrastructure: 2% current usage, 45% planned investment next 12 months
09:20 Is the enterprise AI market still wide open? Anthropic vs. OpenAI for enterprise lead
11:20 The routing question: when do you send workloads to smarter vs. cheaper models?
12:20 Transform preview: what to expect from Amazon, Databricks, Brex, Instacart, Mastercard
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