Cerebras Set to Report Earnings as AI Chips Enter a New Era

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August 12, 20261 min readAI

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Infrastructure and compute: Enterprises are buying AI compute for speed while flying blind on what it costs

Across 170 enterprises, AI infrastructure has moved decisively into production — two-thirds now run AI workloads live and three in 10 run them at scale — while the ability to account for what that infrastructure costs has not kept pace. Enterprises have quietly demoted cost in the buying decision: performance and GPU availability now outrank total cost of ownership, and reliability outranks price as the measure of success. That reordering is rational for teams under production pressure, but it lands on an uncomfortable fact — fewer than half can rigorously track what their AI compute costs, most GPUs still run at half capacity or less, and the next dollar is aimed at specialized clouds that fewer than one in twenty of them actually use. This wave of VentureBeat Pulse Research examines enterprise AI infrastructure and compute: where organizations are in their deployment journey, what they run AI on today, how they buy and measure it, where the next investment is aimed, and — most revealingly — how well they can see the economics of the compute underneath it all. This is an operational cohort. Two-thirds of enterprises (66%) have AI workloads running in production, and 29% describe AI in production at scale, with only 4% not yet running AI workloads at all. That maturity shows in the stack: the average enterprise runs three infrastructure platforms, with OpenAI (49%), Google Gemini (48%), Microsoft Azure (47%), and Google Cloud (42%) all present in roughly half of them. Asked to name one primary platform, Azure leads at 26%. The most consequential shift is in how enterprises decide. Integration with the existing cloud and data stack remains the top selection factor at 40%, but performance — latency and throughput — has climbed to second at 35%, and access to GPU availability to third at 24%, both ahead of total cost of ownership at 22%. The same ordering governs measurement: uptime and reliability is the primary success metric for 51% of enterprises and developer productivity for 39%, ahead of cost per million tokens at 31%. Enterprises under production pressure are buying and measuring for speed and availability, and have moved cost down the list. That would be unremarkable if the economics were under control, but they're not. Among the 155 enterprises that operate their own GPUs, 69% report utilization of 50% or less and only 23% clear the halfway mark; 12% do not measure utilization at all. Fewer than half (47%) rigorously track what their AI compute costs and returns, and even among enterprises running AI in production at scale that figure only reaches 56%. Value for money is the weakest of three satisfaction scores at 3.87, against 4.14 for overall satisfaction — the softness landing precisely on the dimension hardest to judge without measurement. The next round of spending points away from the current stack. AI-specialized clouds are the top planned evaluation area at 44% and carry the strongest net momentum of any infrastructure approach (+…

VentureBeat · 1 hour ago

Agentic security: Enterprises enforce agent permissions two-thirds of the time — and isolate high-risk agents less than one in five

Across 116 enterprises, agents are in production and so are the incidents: A majority have already had a confirmed agent security event or a near-miss. Two-thirds of enterprises enforce scoped permissions at runtime. Barely one in five isolates its highest-risk agents, making containment the weakest layer in the stack precisely as autonomy scales. Credential sharing persists across nearly two-thirds of agent fleets, and 53% have already had a confirmed agent security event or near-miss, contributing to a growing lack of confidence in agentic security. Security stacks remain overwhelmingly borrowed from model providers and hyperscalers, and confidence has slipped. Today, as many enterprises now believe AI-armed attackers are ahead of their defenses as believe the reverse. This wave of VentureBeat Pulse Research examines how enterprises secure their AI agents: what tooling they run, how they manage agent identity and isolation, what has already gone wrong, how much they spend, and whether they believe their defenses are keeping pace with AI-enabled attackers. Only 18% of enterprises isolate their highest-risk AI agents, even as 65% of enterprises enforce scoped permissions at runtime and 56% monitor and log agent activity. The gap between what enterprises watch and what they contain is the central finding of this wave of VentureBeat Pulse Research. More than half of enterprises (53%) have agentic AI systems in production today, and another 27% are piloting or running a limited rollout. The agentic security incidents are arriving with them: 53% of organizations have already had an agent security event, with 19% confirming an incident and 38% having identified a near-miss that was caught before it caused harm. The central finding is a containment gap. Enterprises have built the controls that watch and permission agents but not the one that bounds the damage when those fail. Among enterprises describing their security posture, 65% enforce scoped identities and permissions at runtime and 56% observe and log agent activity, yet only 18% isolate high-risk agents in sandboxes. Even among enterprises running agents in production, isolation is enforced just 21% of the time, and just 8% pair enforcement with isolation. That ordering is backward from a defense-in-depth standpoint. From SOC teams to CISOs, security teams know that observation tells you what happened and enforcement tries to prevent it, but isolation is what limits the blast radius when prevention fails. Identity has improved without being solved. 49% of enterprises say each of their agents has its own scoped, managed identity, but 63% report credential sharing somewhere in the agent fleet, and only 29% describe a fleet with scoped identities and no sharing anywhere. The security stack doing this work remains overwhelmingly hyperscaler or model provider-native: OpenAI’s guardrails (44%), Microsoft Azure (42%), Anthropic’s managed-agent controls (37%), and Google Cloud (31%) lead, and 92% of en…

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Agentic reliability and evaluations : Enterprises that got burned by a bad eval are the most likely to remove humans from the loop, not the least

Across 108 enterprises, trust in automated agent evaluation rose sharply in July — and the failure rate it is supposed to predict did not move at all. The share of organizations that fully trust automated evaluation nearly tripled, from 5% in June to 13%, and the complaint that evaluations don’t match real-world outcomes fell 10 points. Yet the same share as last month — just under half — shipped an agent that passed its evals and then failed a customer. The reason is visible in the cross-tabs: the new trust belongs almost entirely to enterprises that have not yet been burned. Among those that have, 4% trust automated evaluation; among those that haven’t, 24% do. And getting burned does not slow the march to autonomy — it speeds it up. This is the second wave of the VentureBeat Pulse Research agent reliability tracker, and the first fielded on an instrument identical to the month before it. That makes July the first read on direction rather than position: what moved, what held, and what the movement means. What moved is confidence. In June, only 5% of enterprises said they fully trusted automated evaluation, and the most-cited limitation was that evaluations align poorly with real-world outcomes (29%). In July, 13% fully trust automated evaluation and the alignment complaint has fallen to 19%, no longer the leading objection. Both shifts are large enough to read as real rather than noise. What held is the failure. Just under half of organizations (49%) deployed an agent or LLM feature in the past year that passed internal evaluations and then caused a customer-facing failure — statistically indistinguishable from June’s 50% — and a quarter (24%) have seen it happen more than once. Confidence improved; correctness did not. That is the July gap: not between autonomy and trust, as in June, but between trust and the evidence for it. The cross-tabs explain where the new confidence comes from, and it is not from better evaluations. Trust is concentrated almost entirely among enterprises that have not experienced a false-confidence failure: 24% of them fully trust automated evaluation, against 4% of those that have. The trust curve is being lifted by inexperience. Meanwhile the enterprises that have been burned are not retreating from autonomy — 85% of them already allow zero-human deployment or are engineering toward it, against 61% of those that have not been burned. Overall the autonomy trajectory is flat at 67%, but the population inside it has shifted toward the organizations with the most direct evidence that evaluations miss things. The vendor market, by contrast, is finally showing signs of settling. The share of enterprises running no dedicated evaluation tooling fell from 17% to 12%; specialist platforms gained, with Braintrust nearly doubling to 15% and DeepEval reaching 17%; and switching intent cooled, with those planning no change rising from 36% to 44%. Selection criteria moved with it: ease of integration overtook cost as the top factor…

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Agent context layers: Enterprises governing their AI data are catching twice as many bad answers as the ones who aren't

Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in the past six months, and the single most common answer is not "once" but "more than once." The counterintuitive part is which companies report it. Enterprises building or running a governed semantic layer (a layer of company-specific definitions and relationships) report recurring failures at more than twice the rate of those without one. The infrastructure built to fix bad context is, so far, mostly revealing how much bad context there is. Meanwhile the architecture meant to solve the problem commands no consensus at all: hybrid retrieval and outright pluralism finish one respondent apart, in a dead heat. This wave of VentureBeat Pulse Research examines the enterprise RAG and context layer: what feeds AI agents their business context, which retrieval systems enterprises run, how they buy and measure them, where the architecture is heading, and — most revealingly — how often that context is already failing them. The central finding is that the context failure is no longer an incident; it is a condition. Sixty-eight percent of enterprises say that in the past six months their AI agents produced confident but wrong answers they traced to missing or inconsistent business context rather than to model error. More striking than the total is its shape: 37% report the failure recurring, against 32% who saw it once. Among enterprises in a position to answer at all, the most prevalent experience of running agents on company data is being wrong repeatedly for reasons that have nothing to do with the model. The remedy the industry has settled on — a governed semantic or context layer giving agents and BI a shared understanding of the data — is being built at scale: 32% run one in production, another 31% are piloting or building one, and 20% more are evaluating. But the cross-tabs deliver an uncomfortable result: Enterprises that have built or are building a layer report recurring context failures at 50%, against 21% for those without one. The layer isn't causing the failures — it's catching them, which makes it the most useful finding in the wave. The semantic layer is what makes a context defect traceable. Organizations without one are not having fewer failures so much as attributing fewer failures. Underneath, the stack is unsettled in a way it was not expected to be. Retrieval remains the leading primary context source at 31%, and provider-native retrieval — OpenAI's file search (46%) and Google Vertex AI Search (41%) — still runs well ahead of every dedicated vector database. But the expected architecture has no majority behind it: hybrid retrieval (30%) and "multiple architectures, chosen by use case" (29%) are separated by a single respondent. And enterprises remain firmly unwilling to hand the context layer to a provider — just 12% intend to consolidate…

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