SUCCESS

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 (+36), yet CoreWeave and Lambda each registers at 3.5% of current usage and the rest of the neocloud field sits below 3%. Non-Nvidia accelerators draw 39%. And 62% of enterprises intend to switch or add a provider within 12 months — though the consideration set is dominated by the same incumbents they already run.MethodologyVentureBeat fielded this survey as part of its ongoing Pulse Research series, this one focused on enterprise AI infrastructure, compute, and inference economics. Responses are filtered to organizations with more than 100 employees (n=170; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single July 2026 wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends; all figures are drawn from the July fielding only. Several questions were multiple-select, so those shares can sum to more than 100%.By organization size this wave reaches further up-market than the mid-market skew this series usually carries: 251–1,000 employees (28%) and 1,001–5,000 (25%) lead, with 10,001+ (19%), 101–250 (15%), and 5,001–10,000 (12%) filling out the rest — meaning 57% of respondents sit above 1,000 employees. By role it spans managers (48%), individual contributors (27%), the C-suite (12%), and VPs and directors (9%); on purchasing authority it is buyer-credible, with 39% final decision-makers and another 43% recommenders or influencers for AI solutions. Technology/Software is the largest industry at 35%, followed by Manufacturing (14%), Financial Services (12%), and Healthcare/Life Sciences (9%).At 170 respondents the sample is large enough to read directionally with reasonable confidence, but it should still be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively building and operating AI infrastructure rather than from the largest hyperscale operators.Finding 1: Two-thirds are past the pilotThree in 10 now run AI in production at scaleWe asked where organizations sit in their AI deployment journey. This cohort has largely moved beyond experimentation.Two-thirds of enterprises (66%) have AI workloads running in production, and 29% describe AI in production at scale. Only 30% remain in proofs of concept and just 4% have not started. This is a materially more operational sample than this series has typically drawn, consistent with its up-market composition — 57% of respondents sit above 1,000 employees.That maturity is the frame for everything that follows. The infrastructure decisions in this report are being made largely by organizations with production workloads and real bills, not by teams still sizing a pilot. It explains the reordering of buying criteria in Finding 5, where performance and availability displace cost — the priorities of teams running live systems. It also raises the stakes on Findings 6 and 7: an enterprise that cannot measure utilization or cost during experimentation has a planning problem, while one that cannot measure them in production at scale has an operating one.Finding 2: The stack is hyperscaler-and-API, three platforms deepThe specialized GPU clouds still barely registerWe asked which providers and platforms enterprises currently use to run their AI, and which one they treat as primary. The answer remains the incumbents — several of them at once.The current stack is hyperscaler-and-API, and it is plural: enterprises name three platforms on average. The general-purpose clouds and the major model APIs account for essentially all current deployment, with four platforms — OpenAI, Gemini, Azure, and Google Cloud — each presents in more than four of every 10 enterprises. Asked to pick one primary platform, Microsoft Azure leads at 26%, with Google Cloud second at 19%; the model providers together take 35% of primary status when OpenAI (14%), Gemini (14%), and Anthropic (8%) are combined.The specialized “neocloud” GPU providers that dominate AI-infrastructure headlines remain marginal in practice. CoreWeave and Lambda each appear in 3.5% of stacks, Baseten in 3%, and Crusoe, Nebius, Fireworks, Together, and Anyscale each at or below 2%. Combined, they are named as the primary platform by 1% of enterprises. Meanwhile 13% run a custom open-source self-managed stack and 9% operate their own GPU clusters — both larger footprints than the entire specialized-cloud category. That contrast is what makes the evaluation intentions in Finding 3 worth reading closely.A note on reading these shares: As described in the methodology section, this sample is self-selected and this question counted every provider a respondent uses — an average of 3.0 selections each — so the figures measure presence in the stack rather than spending or primary status. The separate primary-platform question is the better guide to where the center of gravity sits. A sample built this way will show a different provider mix than a spend-weighted census of the broader market; read these shares as a portrait of what this AI-active cohort runs today, and treat gaps against industry-wide market share estimates as a property of the sample rather than a contradiction of either.Finding 3: The next dollar goes to infrastructure they don’t yet runAI-specialized clouds top the evaluations list and carry the strongest momentumWe asked where enterprises plan to evaluate AI infrastructure over the next 12 months, and whether they expect to do more or less with each category of infrastructure. Both answers point away from the stack they run today.Here is the report’s sharpest tension, and it is the same one this series has now recorded across successive waves. The single most-cited planned evaluation area — AI-specialized clouds, at 44% — is the category that 3.5% of these enterprises actually use (Finding 2). Nearly four in 10 (39%) intend to evaluate non-Nvidia accelerators, a quarter next-generation Nvidia silicon, and even decentralized compute networks draw 18%.The direction-of-travel question corroborates it rather than merely repeating it. Asked whether they expect to do more, less, or about the same with each approach, enterprises put specialized AI clouds at the highest net momentum (+36, with 42% doing more against 6% doing less), ahead of inference APIs (+34) and hyperscalers (+30). On-prem and co-located infrastructure is the laggard at +5, the only category where a substantial share — 22% — report pulling back. Every off-premises approach is net-expanding; the specialized clouds are expanding fastest from the smallest base.Read against current usage, this is not incremental adjustment. It is the leading edge of a re-platforming that enterprises have been signaling for several waves and have not yet executed. The gap between a 44% evaluation rate and a 3.5% usage rate is the single widest intent-to-action spread in this dataset, and how it resolves — whether the neoclouds convert evaluation into deployment, or whether the hyperscalers absorb the demand with their own AI infrastructure — is the open question of the category.Finding 4: Six in 10 plan to move, mostly among the incumbentsHigh churn intent, but the consideration set is the stack they already runWe asked whether and when enterprises plan to switch or add an infrastructure provider, and which providers they are considering.For a category as foundational as compute, this is a substantial amount of intended movement: 62% of enterprises intend to switch or add a provider within 12 months, and 29% within the next quarter alone. Only 39% plan to stand still.Where that interest points is the more useful signal. The providers drawing the most switching consideration are the ones enterprises already run — OpenAI and Google Cloud (29% each), Microsoft Azure (28%), Gemini (25%), Anthropic (16%), Oracle Cloud (14%), and AWS (13%). The specialized clouds that top the evaluation list in Finding 3 draw far less concrete switching consideration: CoreWeave 4%, Lambda 3.5%, and the remainder at or below 2%. A further 8% are evaluating with no shortlist yet.The two findings are not in conflict; they operate on different clocks. The neocloud interest in Finding 3 is a 12-month evaluation thesis about where AI compute should eventually run. The switching in the next quarter is mostly incumbents trading share and enterprises consolidating spend among providers they already hold contracts with. Vendors reading the 44% evaluation figure as near-term pipeline should weigh it against a 4% consideration rate.Finding 5: Performance overtakes cost, in buying and in measurementTotal cost of ownership falls below latency and GPU availabilityWe asked what matters most when enterprises select an AI infrastructure provider, and what they treat as the primary measure of success once it is running. Both answers have moved away from price.Integration with the existing stack remains the top selection factor at 40%, which is consistent with a cohort running three platforms and unwilling to add a fourth that does not fit. What has changed is everything below it. Performance sits second at 35% and GPU access and availability third at 24%, both ahead of total cost of ownership at 22%. Fine-grained autoscaling draws 18% and cost per million tokens 16% — no longer the outlier it once was in this series, but still last.Measurement follows the same logic. Uptime and reliability is the primary success metric for 51% of enterprises, well ahead of developer productivity and deployment speed (39%), cost per million tokens (31%), latency (27%), and throughput (25%). Taken together, the operational metrics dominate the economic one by a wide margin.This is a coherent posture for the production cohort in Finding 1 — teams running live workloads care first about whether the system stays up and how fast they can ship on it. But it sits uneasily beside Finding 7. Total cost of ownership has been demoted to fourth as a buying criterion at exactly the moment when 53% of enterprises still cannot rigorously track what their compute costs. The uncomfortable reading is that cost has fallen down the list partly because it remains the hardest thing in the stack to see, and criteria that cannot be measured tend to lose to criteria that can.Finding 6: The GPUs run warmer, but most still run coldRoughly seven in 10 GPU operators report 50% utilization or lessWe asked what share of their GPU capacity enterprises actually utilize. Figures here are reported on the 155 enterprises that operate their own GPUs; 15 consume exclusively via API and run none.The compute already in place runs cold, though less so than this series has recorded before. Roughly seven in ten GPU-operating enterprises (69%) report utilization at or below half capacity, with the 26–50% band alone accounting for 46%. About a quarter (26%) run at 25% or below. Against that, 23% now clear the 50% mark — a meaningful efficient minority rather than a rounding error.The remaining 12% who do not measure utilization at all are the more troubling number, because they are invisible in both directions: they cannot claim efficiency and cannot detect waste. And utilization does not improve with maturity in the way one might expect — among enterprises running AI in production at scale, 22% clear the 50% mark, statistically indistinguishable from the 24% among everyone else. Scale is not, by itself, producing better-utilized fleets.Idle accelerators are expensive accelerators, and this remains the clearest single measure of the gap in this report: enterprises are planning to evaluate specialized clouds and next-generation silicon (Finding 3) while the capacity they already own sits substantially unused. The efficiency headroom in the current fleet is large, and for one in eight enterprises, entirely unmeasured.Finding 7: Fewer than half can account for what they spendRigorous cost tracking reaches only 56%, even among at-scale operatorsWe asked whether enterprises can quantify the cost and return of their AI infrastructure spend, and how satisfied they are with what they run. Confidence in the ledger still lags the spending.Measurement trails money. Fewer than half of enterprises (47%) rigorously track the cost and return of their AI compute; the majority track only partially (39%), cannot quantify it yet (15%), or have not prioritized it (6%). Maturity helps but does not solve it: among enterprises running AI in production at scale, rigorous tracking reaches 56%, against 43% for everyone else. Even in the most operationally advanced segment of this sample, more than four in ten cannot account precisely for what their AI compute costs or returns.Satisfaction with current infrastructure is moderately positive and tellingly uneven. On a five-point scale, overall satisfaction averages 4.14 and ease of implementation 4.04, while value for money trails at 3.87 — the softness landing on the one dimension that requires measurement to assess. Enterprises are, in effect, expressing dissatisfaction with an economic relationship most of them cannot yet quantify.Read with Finding 5, the picture is self-reinforcing rather than merely inconsistent. Cost has slipped to fourth among buying criteria while remaining the least visible property of the stack, and the least visible property is the one enterprises rate lowest. Better instrumentation would not necessarily change what enterprises buy — but it would let them know whether the trade they are making for performance and availability is a good one.Finding 8: The memory frontier is still unclaimedDell and Nvidia lead a scattered field, and one in five has no viewWe asked how enterprises would address the emerging constraint in large-scale inference — the shift from GPU compute to memory, specifically KV-cache capacity. The field remains early and fragmented.The memory frontier is real but barely governed. Dell leads at 24% and Nvidia follows at 21%, with the remainder scattering across open-source tooling (12%), model-level efficiency techniques such as MLA and quantization (11%), and a long tail of storage vendors each in low single digits. No approach commands anything close to a majority, and the two leaders together account for less than half the field.Most telling is that roughly one in five enterprises (19%) either do not recognize the constraint (7%) or have not begun to address it (12%). For a shift that will reshape inference cost and architecture, this is an early and unsettled market. It is also consistent with the measurement gap in Finding 7 — enterprises that cannot yet quantify what their current compute costs are in a poor position to anticipate which constraint will drive that cost next. The memory bottleneck is arriving while most of this cohort is still working to see the one in front of it.The bottom line: Buying for speed, blind on costOrganizations with more than 100 employees have moved AI infrastructure into production — two-thirds run live workloads, three in ten at scale — and their buying behavior has matured accordingly. They run three platforms on average, select on integration and performance, and measure success on uptime and developer velocity. For teams operating live systems, that is the right set of priorities.What has not matured is the accounting. Total cost of ownership has fallen to fourth among selection criteria and cost per million tokens sits last, at the same moment that 53% of enterprises cannot rigorously track what their compute costs, 69% of GPU operators run at half capacity or less, and 12% do not measure utilization at all. Value for money is the lowest-rated attribute of the infrastructure they run — a judgment most of them are making without the instrumentation to support it. Cost has not become unimportant; it has become invisible, and the buying criteria have quietly reorganized around what can actually be seen.Meanwhile the next round of spending points past the current stack. Specialized AI clouds are the top evaluation target at 44% and carry the strongest net momentum of any approach, against a 3.5% usage rate and a 4% near-term switching consideration — the widest intent-to-action spread in the data. Non-Nvidia accelerators draw 39%. And the constraint after this one, the shift from compute to memory in large-scale inference, is unrecognized or unaddressed by one enterprise in five.At 170 respondents in a single July wave, reaching further up-market than this series typically does, this is a directional read — but the direction is consistent. Enterprises have become good operators of AI infrastructure and have not yet become good accountants of it. The open question for later waves is whether the instrumentation catches up before the re-platforming arrives, or whether enterprises buy the next layer of compute as blind to its economics as the last.Based on survey responses from 170 qualified enterprise respondents (100+ employees), drawn from a single July 2026 wave. This sample is self-selected and directional rather than a precise measurement, and reads cross-sectionally with no month-over-month trend claims. Respondents include managers, individual contributors, C-suite, and VPs/directors, with purchasing authority weighted toward decision-makers and recommenders, across technology, manufacturing, financial services, healthcare, and other industries. Note: Figures for the switching-timeline, GPU-utilization, and cost-tracking questions are reported as a percentage of unique respondents rather than selections; individual categories for these three questions may sum to more than the reported total.

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 enterprises naming a primary security layer name a hyperscaler/model provider-native one.Two things have shifted against the comfortable picture. Confidence has slipped, with 30% now saying AI-armed attackers are ahead of their defenses, exactly as many as say their defenses are ahead. And churn intent is the highest this series has recorded, with 74% planning to adopt, add, or replace agent security tooling within twelve months, despite satisfaction scores at a series high of 4.29 out of 5. Enterprises are more satisfied than ever with a stack they are more determined than ever to replace.MethodologyVentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent security — the tooling, identity, isolation, and enforcement controls organizations use to secure autonomous AI agents. Responses are filtered to organizations with more than 100 employees (n=116; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single July 2026 wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends; all figures are drawn from the July fielding only. Several questions were multiple-select, so those shares can sum to more than 100%.By role the sample is senior and buyer-credible: 44% are final decision-makers for AI purchases and another 38% recommenders or influencers. Managers (36%), individual contributors (27%), VPs and directors (18%), and the C-suite (16%) make up the seniority mix. By organization size the sample is mid-market-weighted with a meaningful enterprise tail: 101–250 (34%) and 251–1,000 (23%) employees lead, with 1,001–5,000 (18%), 10,001+ (17%), and 5,001–10,000 (7%) above them. Technology/Software is the largest industry at 38%, followed by Healthcare/Life Sciences (11%) and Financial Services (10%).Three questions require a base note. Two questions were asked only of enterprises with agents live or piloting. Posture figures (observe / enforce / isolate) are reported on those 93 respondents, and primary-security-layer figures on the 92 of them who named a layer. The 23 respondents outside this base are those still evaluating, without plans, or unsure — organizations for which an agent security posture would not yet apply. And several multiple-select questions permitted overlapping answers where one was intended — identity (33 respondents selected more than one pattern), arms-race assessment (23), budget share (10), and incidents (9) — so those are computed at the respondent level and the overlap is described where it matters. Satisfaction ratings are computed on the respondents who answered each rating question; the overall satisfaction score reflects 76 of the 116 qualified respondents.At 116 respondents, the sample supports directional reads but not precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively standing up agent security rather than from the largest operators.Finding 1: Agents are in production, and so are the incidentsA majority have already had an agent security eventWe asked whether organizations run agentic AI in production, and whether they had experienced an agent security incident — a confirmed breach, or a near-miss caught before harm.Agents have moved into production for this cohort. More than half of enterprises (53%) run agentic AI systems live today, another 27% are piloting or running a limited rollout, and only 3% have no plans in the next twelve months. The security exposure has scaled with the deployment: 53% of organizations have already had an agent security event, 19% a confirmed incident and 38% a near-miss caught before it caused harm.That the near-misses outnumber confirmed incidents two to one is worth reading carefully. It means enterprises are catching problems, but catching them close to the edge — and a near-miss is a control that worked once, not a control that will work every time. The controls examined in the rest of this report, particularly the identity and isolation gaps in Findings 2 and 3, are what determine whether the next near-miss stays a near-miss.One pattern from earlier waves does not replicate here. Organization size makes no reliable difference to exposure: enterprises above 1,000 employees report an incident or near-miss at 47%, against 57% among those between 101 and 1,000 — a difference well inside sample noise, and pointing the opposite direction from the size gradient this series has previously recorded. In this wave, what separates the hit from the not hit is not headcount.Finding 2: Identity is improving — and still sharedHalf give agents scoped identities; two-thirds still share credentials somewhereWe asked how enterprises manage the identity of their AI agents — whether each agent has its own credentials, or agents share them. Respondents could describe more than one pattern across the fleet.Per-agent identity is now the most-cited pattern: 49% of enterprises say each agent carries its own scoped, managed identity, the precondition for least-privilege access and clean attribution. That is real progress on the control this series has repeatedly identified as the structural weakness beneath agent incidents.But the answers overlap, and the overlap is the finding. Thirty-three respondents described more than one identity pattern across their fleet, and rolled together at the respondent level, 63% of enterprises report credential sharing somewhere — either agents mostly running on shared API keys and borrowed human or service-account credentials (37%), or a mixed fleet where some agents are scoped and many are not (34%). Only 29% describe a fleet with scoped identities and no sharing anywhere at all. Among enterprises with agents in production, 60% report per-agent identity, so the improvement is concentrated where the agents actually are — but so is the residual sharing.The consequence is unchanged by the improvement. Where credentials are shared, an over-permissioned or compromised agent acts with far more reach than intended, and post-incident forensics cannot cleanly establish which agent did what. Half a fleet with scoped identities still has the blast radius of the half without. Non-human identity remains the largest unfinished piece of enterprise agent security, and as Finding 8 shows, it is still almost entirely absent from what enterprises are shopping for.Finding 3: Isolation is the control nobody buildsTwo-thirds enforce at runtime; fewer than one in five sandboxWe asked what an organization’s agent security posture looks like in practice — whether they observe, enforce, isolate, or some combination. The control that bounds damage is by far the least common. Figures are reported on the 93 respondents who described a posture.This is the containment gap, and it is the widest structural gap in the report. Enforcement and observation are now common — 65% enforce scoped permissions at runtime and 56% monitor and log agent activity — while isolation sits at 18%. Only 8% of enterprises run both enforcement and isolation together, the posture that both prevents and contains.Deployment maturity is a better predictor than the aggregate figures suggest. Isolation reaches 21% among enterprises with agents fully in production, compared with 13% among those still piloting — a meaningful gap that tracks maturity rather than exposure. Among enterprises that report credential sharing in the fleet, the group with the widest potential blast radius per Finding 2, isolation reaches 15%. The organizations with the most exposure are not meaningfully more likely to have built the control that bounds it.The ordering is backwards from a defense-in-depth standpoint. Observation tells you what happened after the fact. Enforcement tries to stop it. Isolation is what limits the damage when enforcement fails — and enforcement will sometimes fail, which is the entire premise of the near-misses in Finding 1. An agent fleet that is watched and permissioned but not boxed in is precisely the configuration in which a single control failure propagates across systems. Enterprises have built the first two layers of the model and largely skipped the third.Finding 4: Security still runs on borrowed, provider-native controlsNine in 10 name a model provider or hyperscaler as their primary layerWe asked which agent security tooling enterprises use, and which is their primary layer. The answer continues to favor the model providers and hyperscalers over the dedicated security vendors.Enterprises secure agents with tools that came bundled with their models and clouds. OpenAI’s guardrails lead at 44%, followed closely by Microsoft Azure (42%), Anthropic’s managed-agent controls (37%), and Google Cloud (31%). Asked to name a single primary security layer, 92% of those who answered named one of these provider-native offerings, with Azure (27% of answerers) and Anthropic (26%) leading.The purpose-built agent-security category is no longer at zero, but it remains marginal. Cloudflare (11%) and Cisco (9%) lead the specialists, with CrowdStrike, Palo Alto, Zenity, Check Point’s Lakera, HiddenLayer, F5, and SentinelOne each between 1% and 7%. The identity specialists most directly relevant to Finding 2 are the smallest of all: Microsoft Entra Agent ID at 7%, Okta for AI Agents at 3%, and non-human identity platforms at 3%. Dedicated runtime sandboxing tooling — the control missing in Finding 3 — is in place at 3%.A note on reading these shares: As described in the methodology section, the respondent sample is self-selected, and the usage question counted every vendor or approach a respondent has in place — so the figures measure presence in the security stack rather than spending or exclusivity. Individual vendor percentages therefore carry all the usual sample caveats. The structural pattern is the durable part: provider-native and hyperscaler controls lead by a wide margin, and dedicated agent-security specialists remain in single digits. Read the individual shares loosely and the pattern with confidence.Finding 5: Satisfaction is at a series high — and so is churn intentEnterprises rate their tooling 4.29 of 5 and three-quarters plan to replace itWe asked how satisfied enterprises are with their current agent security tooling, and whether they plan to adopt a new, additional, or replacement solution within twelve months. The two answers do not sit comfortably together.Satisfaction with agent security tooling is the highest this series has recorded — 4.29 out of 5 for both overall satisfaction and ease of implementation, with value for money close behind at 4.11. That is a striking set of scores for a stack that is mostly borrowed provider guardrails, given that a majority of the same enterprises have already had an incident or near-miss and fewer than one in five isolates high-risk agents.The purchase intentions tell the other half of the story. Three-quarters (74%) plan to adopt, add, or replace agent security tooling within 12 months, and 30% within the next quarter alone — higher churn intent than this series has previously seen in this category. Only 26% intend to stand pat. Enterprises are simultaneously more satisfied with their tooling and more determined to change it than at any prior reading, which suggests the satisfaction rests on the convenience and low friction of provider-native controls rather than on demonstrated containment. It is comfort with what is easy, not confidence in what is sufficient.Finding 6: Budgets are finally movingA third now spend more than a tenth of the security budget on agentsWe asked what share of the security budget enterprises allocate to securing AI agents. The allocation has grown, though it remains a modest slice.Agent security spending is still a slice rather than a pillar, but it is a growing one. The most common allocation remains 6–10% of the security budget (44%), and roughly a third of enterprises (35%) now devote more than a tenth — a meaningful funded minority. Just over a quarter (28%) spend 5% or less.Read against Findings 1 through 3, the budget looks like a lagging but responsive indicator. A majority of enterprises have had an incident or near-miss, credential sharing persists across two-thirds of fleets, and fewer than one in five isolates high-risk agents — gaps that a 6–10% allocation is unlikely to close quickly. The enterprises spending above a tenth are the ones with the resources to build scoped identity and isolation controls rather than adopt whatever their model provider ships, and whether that minority grows is a reasonable leading indicator for whether the containment gap narrows.Finding 7: The arms race has tiltedAs many say attackers are ahead as say their defenses areWe asked how enterprises assess the balance between their AI-enabled defenses and AI-enabled attackers. Confidence has slipped into an even split.
Enterprises are no longer net-optimistic about the contest. Exactly as many say AI-armed attackers are ahead of their defenses (30%) as say their defenses are ahead (30%), with another 33% calling it roughly even and 24% saying it is too early to tell. Taken together, 63% rate the balance as even or worse.Experience is what drives the pessimism, and the relationship is statistically clear. Among enterprises that have had a confirmed incident or near-miss, 39% say attackers are ahead; among those that have not, 20% do — a gap large enough to be unlikely to arise by chance in a sample this size. Getting hit does not just change what enterprises buy; it changes how they read the contest. The organizations closest to the actual threat are the least confident about it.That assessment sits uneasily beside the series-high satisfaction of Finding 5. Enterprises rate their tooling 4.29 out of 5 while a clear majority believe it is, at best, holding even against an adversary that is also compounding with AI. An even race is not a comfortable place to be, and the group that has actually been tested rates it worse than even.Finding 8: A reshuffle is coming — but identity still isn’t on the listIncidents drive urgency; the control they implicate draws 10% interestWe asked which agent security solutions enterprises are considering. The consideration set has broadened, but not in the direction the incident data points.Incidents start the buying cycle. Among organizations that have had a confirmed incident or near-miss, 38% plan to adopt, add, or replace agent security tooling within the next ninety days, against 22% of organizations with no incident; after a confirmed incident specifically the figure reaches 41%. Experience remains the strongest predictor of urgency in this data, as it is of pessimism in Finding 7.The consideration set still leans provider-native — OpenAI (38%), Microsoft Azure (37%), Anthropic (35%), and Google Cloud (28%) lead — though the dedicated security vendors now draw meaningful early interest: Cisco (10%), Cloudflare (9%), Zenity and CrowdStrike (8% each), and Palo Alto, Check Point’s Lakera, and open-source guardrails (6% each). For most of the specialists that is more forward interest than current footprint.What the shopping still does not include is the identity layer. Just 10% of enterprises include an agent-identity product — Okta for AI Agents, Microsoft Entra Agent ID, or a non-human identity platform — anywhere in their consideration set. Among the enterprises that both share credentials and have already been hit, the group with the most direct evidence that the control matters, identity consideration is no higher: roughly one in ten. Runtime sandboxing tooling draws 6%. The two controls most directly implicated by the incident data, identity and isolation, are the two least present in the purchase plans — the same blind spot this series recorded in the prior wave, unchanged despite a year of incidents.The bottom line: A security gap that prevention alone won’t closeOrganizations with more than 100 employees have put agents into production — 53% run them live today — and the incidents have arrived alongside them, with a majority already reporting a confirmed event or near-miss. On the controls, the picture is genuinely mixed rather than uniformly poor: nearly half now give each agent its own scoped identity, two-thirds enforce permissions at runtime, and a third devote more than a tenth of the security budget to agents. Enterprises are building agent security in earnest.What they are not building is containment. Fewer than one in five isolates high-risk agents, only 8% pair enforcement with isolation, and among enterprises running agents in production isolation reaches just 21%. Credential sharing persists across 63% of fleets, so the blast radius that isolation would bound remains wide. The stack doing this work is 92% provider-native by primary layer, and the specialists built for exactly these gaps sit in single digits. The result is an architecture optimized to prevent and observe, with almost nothing in place for the case where prevention fails — which is the case the near-misses in Finding 1 describe.The uncomfortable pairing is confidence with exposure, and it has sharpened. Satisfaction is at a series high of 4.29 out of 5, yet 63% rate the contest against AI-armed attackers as even or worse, 30% say attackers are ahead outright, and 74% plan to replace tooling they just rated highly. Enterprises that have actually been hit are markedly more pessimistic and markedly more urgent — and still not shopping for identity or isolation, the two controls their incidents most directly implicate.At 116 respondents in a single July wave this is a directional read, weighted toward the mid-market — but the direction is clear: agent deployment is running ahead of agent containment, and the gap is not in what enterprises watch or permission but in what happens when those controls fail. The containment gap will not be closed by a better provider guardrail. The open question for later waves is whether enterprises build isolation and governed identity deliberately, or whether a confirmed incident that propagates does it for them.Based on survey responses from 116 qualified enterprise respondents (100+ employees), drawn from a single July 2026 wave. This is a directional signal from a self-selected sample, not a probability sample. Respondents include managers, individual contributors, VPs/directors, and C-suite leaders, across technology, healthcare, financial services, and other industries.

HHS released a pledge to advance national mental healthcare. I add AI to the six stated principles, since AI will be integral to this. An AI Insider analysis and scoop.

Dick’s Sporting Goods stock (DKS) got a fresh vote of confidence from Wall Street this week, and the timing is unusual.The upgrade landed just two weeks before the company reports earnings that the same analyst expects to look weak.That mismatch is the whole point. Wells Fargo is telling investors to look past a soft quarter and focus on where the business is headed over the next two to three years.For anyone holding DKS after a strong run, the question is simple. Is the recovery real enough to buy before a shaky print, or is this a call to wait?How the Wells Fargo Dick’s Sporting Goods upgrade changes the setupOn Monday, August 10, Wells Fargo analyst Ike Boruchow upgraded Dick’s Sporting Goods to Overweight from Equal Weight and raised his price target to $240 from $220, CNBC reported.Overweight is the firm’s way of telling clients to own more of the stock than the market average. Equal Weight means treat it like the average.That $240 target sits about 12% above the August 10 close of $214.10. The stock had already climbed 7.2% over the prior five trading days, so buyers were moving in before the note.Boruchow’s message was direct. Near-term trends look soft, but the multi-year recovery led by Foot Locker is worth buying at current levels, CNBC noted. Structural profit improvements matter more than one quarter of low numbers.Why Wells Fargo says the Foot Locker turnaround drives the DKS bull caseDick’s acquired Foot Locker in 2025, and the integration has weighed on results since. Foot Locker’s operating margins fell to a thin 1% to 2% after the deal, dragged down by corporate changes and inventory cleanup, according to Investing.com.Wells Fargo expects those margins to recover to 7% to 8% over the next several years, helped by store remodels, better product allocation, and stronger merchandising.That climb from 2% to 8% is the biggest driver of the bank’s higher target. If Foot Locker earns its way back to normal margins, consolidated profit rises sharply.Boruchow also called the core Dick’s business the “New Star of US Sport,” pointing to its category leadership and its House of Sport and Field House store formats, Investing.com noted.He flagged the GameChanger youth sports app, paid loyalty tiers, and the DICK’s Media Network as an under-appreciated set of profit drivers working together.

Wells Fargo says Dick’s House of Sport and Foot Locker remodels are central to a multi-year margin recovery.jetcityimage / Getty Images

Dick’s as the cleanest way to bet on a Nike recoveryWells Fargo made a second argument that ties Dick’s to a much larger name in sportswear.Nike is working through its own turnaround, and the stock has struggled. Shares fell about 33% in 2026 as its “Win Now” reset dragged on. JPMorgan also recently downgraded the stock to Underweight.Boruchow’s view is that Dick’s gives investors a way to profit from an eventual Nike rebound without owning Nike directly, according to Investing.com. More Retail Coverage:JPMorgan cuts Nike stock to sell on longer turnaroundSportswear giant continues store closures nationwideAll eyes on Disney as ‘Toy Story 5’ massively validates new strategyHis channel checks point to encouraging early trends for Nike’s Spring 2027 product lines, which would flow through Dick’s as a major Nike seller.The logic is that Dick’s captures the benefit of healthier Nike demand in North America while avoiding Nike’s international resets.What the August 25 earnings report means for DKS shareholdersDick’s reports second-quarter results on August 25. Wells Fargo expects weak numbers, with earnings per share of $3.72, below the Wall Street estimate, largely because of Foot Locker, Investing.com noted.Earnings per share is the profit a company makes for each share of stock. A number below the estimate usually pressures the stock price.So the bank is upgrading the stock while forecasting a miss. The reasoning is that back-to-school trends and second-half profit levers will tell investors more than the Q2 figure.For shareholders, that means bracing for possible swings on August 25 while separating the quarter’s numbers from the long-term plan.How DKS stacks up on valuation and analyst supportValuation is a core part of the bank’s argument. Dick’s trades at about 14 to 15 times its expected 2027 earnings, according to CNBC. A price-to-earnings multiple shows how much investors pay for each dollar of profit, so a lower number can signal a cheaper stock.Wells Fargo says that price is reasonable if the company hits its longer-term earnings goals. The bank expects Dick’s to earn more than $20 per share by fiscal 2028.Related: Albertsons stock in hot water after sobering revealAgainst last year’s roughly $13 in earnings, that would be a large jump, and it explains why the bank is willing to look past a weak quarter.Boruchow is not alone. Dick’s carries a StrongBuy consensus rating from Wall Street, with an average target of $261.36. That broad support matters. It shows Boruchow isn’t the only bull on Dick’s. Most of Wall Street already agrees with him.What still has to happen before the $240 target pays offAn upgrade is a forecast, not a result. Several things still need to go right for the stock to reach $240. A few checkpoints worth tracking:Key checkpoints for the DKS bull caseFoot Locker margins: Investors need to see margins climb from 1% to 2% toward the 7% to 8% target the bank models, not just management promises.Nike product cycle: The Spring 2027 lines have to actually sell, since a stalled Nike recovery removes one of the bank’s main catalysts.Back-to-school demand: The August 25 report should show whether Dick’s core business is holding up after the summer World Cup boost faded.Profitability over sales: Wells Fargo is betting profit levers offset any sales shortfall, so margins matter more than headline revenue.The risks are real. Foot Locker’s recovery could take longer than expected, and Nike’s reset has already run longer than analysts expected. If either stalls, the fiscal 2028 earnings target slips, and the valuation case weakens with it.The bottom line for Dick’s Sporting Goods investorsWells Fargo is asking investors to buy a multi-year plan, not a single quarter.The upgrade rests on two clear bets: Foot Locker margins recover toward 8%, and Nike demand improves enough to lift Dick’s sales.For current holders, the near-term risk is a soft August 25 report that could push the stock lower before the longer recovery plays out.For new buyers, the setup offers about 12% to the $240 target, with more if the $20-plus earnings goal for 2028 comes through.The decision comes down to patience. If you believe Foot Locker and Nike both recover on schedule, the current price near $214 looks like a reasonable entry, though the payoff sits years out, not weeks.Related: Kroger stock slide reveals bigger grocery problem

TheStreet aims to feature only the best products and services. If you buy something via one of our links, we may earn a commission.Why we love this dealIt can be tough to find a pair of headphones that are secure enough for workouts and comfortable enough for everyday use. Noise-canceling headphones with over-the-ear designs can be pricey, reduce situational awareness, and cause ear pressure. On the other hand, wireless earbuds can cause discomfort with long-term wear, as well as fall out more easily. But if you want headphones that provide comfort and durability, even underwater while swimming, you’re going to want a pair of waterproof bone conduction headphones.The Tevese Bone Conduction Swimming Headphones at Amazon are a popular pick with more than 500 pairs sold in just the last month. They’re also currently on sale for only $40, thanks to 67% off their regular price of $122. However, this is a limited-time deal. If you’ve been waiting for a solid discount on quality headphones, now’s the time to add them to your cart.Tevese Bone Conduction Swimming Headphones, $40 (was $122) at Amazon

Courtesy of Amazon

Shop at AmazonWhy do shoppers love it?At only $40, these bone conduction headphones are a steal. They work by using vibrations through your cheekbones and an open-ear design to deliver quality sound, whether you’re listening to music, a playlist, an audiobook, or a podcast. As open-ear headphones, they also don’t impact your ear canal. Unlike earbuds that can cause discomfort after long periods of wear, the silhouette is more comfortable, according to shoppers. They also allow for better situational awareness. Since they don’t have noise-canceling technology, you can hear what’s going on around you, keeping you safe and alert.These bone conduction headphones are a dream for working out, especially if you like to swim. They’re not only secure, but they’re also waterproof with a rating of IPX8. With two listening modes, you can use them while in or out of a pool. Bluetooth mode is designated for non-water activities, and MP3 mode with 32 gigabytes (GB) of built-in storage lets you tap into a playlist with over 9,000 songs. Related: Walmart’s highly rated $179 pair of wireless earbuds is now 88% offDetails to knowWaterproof rating: IPX8.Built-in storage: 32 GB.Modes: Bluetooth above water and MP3 underwater.Amazon shoppers said these headphones are the “best pair” they’ve tried. One shopper said they’re “a must-have for swimming,” adding that the Bluetooth connection is easy, the sound is “crystal clear with zero muffled effects,” and “it now makes my laps so much less boring!” Reviewers also said they work well for everyday wear. A customer who said they purchased a pair for office use said they were surprised by their performance. They shared that the battery life is robust and lasts for days, and “it’s very comfortable for long hours and doesn’t cause any ear fatigue.” They also said that it allows them to stay aware of their surroundings in the office without having to take them off.Shop more dealsCptea Bone Conduction Headphones, $30 (was $100) at AmazonDemicea Bone Conduction Earphones, $60 (was $80) at AmazonCxk Bone Conduction Headphones, $28 (was $46) at AmazonThe Tevese Bone Conduction Swimming Headphones are on sale for only $40, and they’re a fantastic buy for swimming, running, and everyday use.

Blockchain data shows the Japanese treasury firm moved the bitcoin between wallets it controls, not to an exchange, so the transfer isn’t a sale despite its $1.4 billion paper loss.

TheStreet aims to feature only the best products and services. If you buy something via one of our links, we may earn a commission.Why we love this dealA comforter is easily one of my favorite bedroom upgrades. You don’t need to deal with the hassle of putting on a duvet cover, and many options come with fun designs that add an eye-catching touch to your bedroom. Best of all, you can toss them in the washing machine, which makes weekly bedding cleanings a breeze.Walmart is a fantastic place to shop for affordable comforters. In fact, it has a lot of multipiece sets that can practically give you a full bed makeover without having to spend time and money buying extra pieces. The Lux Decor Collection 5-Piece Comforter Set is one of my latest bedding finds at Walmart, and it’s on sale now for only $29. With a limited-time Flash deal, you can get the bestselling $55 queen-size set for 47% off. Lux Decor Collection 5-Piece Comforter Set, $29 (was $55) at Walmart

Courtesy of Walmart

Shop at WalmartWhy do shoppers love it?With five pieces, this comforter set has practically everything you need to switch up the style of your bedroom. It comes with a comforter, two matching pillow shams, a decorative pillow, and a bed skirt, all in the same matching pattern. Speaking of, the design is reversible, which is like having two comforters in one. The set features a solid color on one side and a geometric, diamond-like pattern on the other. While the solid color provides neutrality that you can blend into other styles, the diamond pattern has an elevated appearance that looks sophisticated and elegant.Made of polyester, the comforter set is soft, durable, and resistant to wrinkles, which is ideal for having a crisp appearance. The set is machine washable as well, making it easy to maintain a clean bed.The set is available in twin, queen, and king sizes, with availability and sale prices varying based on both size and color. In the gray color and pattern, the twin and queen sizes are still available, but the king is sold out. For the queen size, only six colorways are still in stock. With sizes and colors already sold out, you’re going to want to get in on this deal before it’s too late.Related: Macy’s is selling a $100 8-piece reversible comforter set for only $35Details to knowSizes available: Twin, queen, and king.Colors: Only six colors are still in stock.Material: Polyester.According to shoppers, the thickness is “just right,” offering enough weight to keep you warm but not make you sweat. Reviewers say they love the fabric quality and color, saying it’s “very soft and fluffy.”Shop more dealsAmberspace 7-Piece Comforter Set, $56 (was $90) at WalmartVccoem Store Cooling Comforter, $20 (was $40) at WalmartRegency Heights 9-Piece Comforter Set, $50 (was $130) at WalmartOn sale for just $29, the Lux Decor Collection 5-Piece Comforter Set is a Walmart Flash deal you don’t want to miss.

Find out which slugging first baseman and which left-handed-hitting outfielder in a homer-friendly ballpark have the most appealing home run bets on today’s MLB slate.

TheStreet aims to feature only the best products and services. If you buy something via one of our links, we may earn a commission.Why we love this dealThe right accessories can make the outfit, but thankfully, you don’t always have to spend hundreds to achieve the polished look you want. Versatile and affordable jewelry makes it easy to add finishing touches, and a classic hoop earring with eye-catching crystals is a great option for going to work, going out for the evening, or running to the store, making it a simple option that you’ll reach for again and again.The Cate and Chloe Bianca Gold-Plated Swarovski Earrings are on sale for 50% off, offering dozens of stunning Swarovski crystals that adorn the front side of the hoop for just $20. These sleek hoops are perfect for any occasion and offer a high-end look for a super low price. Cate and Chloe Bianca Gold-Plated Swarovski Earrings, $20 (was $40) at Walmart

Courtesy of Walmart

Shop at WalmartWhy do shoppers love it?These refined earrings add a touch of sparkle to any outfit without overwhelming it. It’s adorned with 34 Swarovski crystals, each measuring 1.6 millimeters, embedded into 25 millimeter hoops. The versatility these earrings offer makes them a useful everyday pair that you can take on trips without using up tons of storage space for jewelry, while relying on just a few staple pieces, and also allows you to save money on multiple pieces that can only be worn during specific occasions. They are designed with long-lasting comfort in mind, offering a hypoallergenic, lead-free, and nickel-free option for people with sensitivities, with some reviewers saying they have worn them for weeks without an allergic reaction. Related: Walmart’s bestselling gold-plated Swarovski crystal hoop earrings are 50% offThe lightweight design features a secure latch-back closure that keeps the earrings in place while also remaining comfortable during long wear, making them easy and practical to wear for longer occasions like bridal showers, birthdays, or holidays without irritation. These earrings come wrapped in a jewelry box that keeps them shiny and dust-free when not in use, and doubles as a gift box if you’re gifting them to a loved one. They are also backed by a 30-day warranty, offering confidence to every purchase. These earrings are available in rose gold, white gold, and yellow gold options, and come in 20 and 25-millimeter sizes. Details to knowSize: These earrings feature 34 crystals on a 25-millimeter hoop, or shoppers can also choose the 20-millimeter hoop option.Colors: Choose from white gold, yellow gold, or rose gold. Material: These hypoallergenic earrings feature lead and nickel-free material that’s lightweight and stays comfortable during long wear. One reviewer couldn’t say enough good things, saying, “They have continued to impress with their elegant sparkle, lightweight comfort, and lasting quality. The rose gold plating holds up beautifully without tarnishing, and the crystals—whether simulated diamonds or Swarovski accents — add just the right touch of shimmer for both everyday wear and special occasions. They’re comfortable for sensitive ears, secure with lever-back or stud closures, and versatile enough to pair with casual outfits or dressier looks. Stylish, affordable, and thoughtfully designed, these earrings are a staple in my jewelry rotation.”Shop more dealsJeenmata Cubic Zirconia Tennis Bracelet, $15 (was $99) at WalmartCate and Chloe 18k White Gold Pearl Earrings, $20 (was $76) at WalmartCate and Chloe Giselle Swarovski Hoop Earrings, $20 (was $115) at WalmartWhether you’re heading out for a casual lunch in a sweater and jeans or attending a family wedding, the Cate and Chloe Bianca Gold-Plated Swarovski Earrings offer elegance, style, and shine for any occasion for just $20. With over 24,000 5-star ratings, these are sure to be a daily staple.

I see a remarkable source of hope: we are entering the greatest philanthropic opportunity for conservation in history.