For decades, procurement has been the back office that enterprise software forgot. Billions of dollars flow through vendor negotiations, purchase orders, and supplier communications every year at the largest manufacturers and construction companies in the country — and the vast majority of that work still runs on email threads, spreadsheets, and phone calls.
Traza, a newly launched startup headquartered in New York, believes the moment has arrived to change that. The company announced today the close of a $2.1 million pre-seed round led by Base10 Partners, with participation from Kfund, a16z scouts, Clara Ventures, Masia Ventures, and a roster of angel investors including Pepe Agell, who scaled Chartboost to 700 million monthly users before its acquisition by Zynga.
The funding is modest by Silicon Valley standards. But Traza’s pitch is anything but incremental: the company deploys AI agents that don’t just recommend procurement actions — they execute them autonomously, handling vendor outreach, request-for-quote generation, order tracking, supplier communications, and invoice processing without continuous human supervision.
“AI is redesigning the procurement category from the ground up,” said Silvestre Jara Montes, Traza’s CEO and co-founder, in an exclusive interview with VentureBeat. “This wave of AI won’t just build procurement software — it will rebuild how procurement works.”
The market Traza is targeting is enormous and, by the company’s framing, spectacularly underserved. The procurement software market alone exceeds $8 billion and grows at roughly 10% annually. But the real cost sits in the labor — the armies of people, agencies, and ad hoc workarounds required to actually run procurement operations at scale. Most enterprises meaningfully engage with only their top 20% of suppliers. The remaining 80% — the vendor outreach, order tracking, invoice reconciliation, and compliance monitoring — goes largely unmanaged.
Research from World Commerce & Contracting and Ironclad finds that organizations lose an average of 11% of total contract value after agreements are signed, a phenomenon described as “post-signature value leakage.” As Tim Cummins, President of WorldCC, put it: “The research shows that the 11% value gap is not caused by poor negotiation, but by how contracts are managed after signature.” For a large enterprise with $500 million in annual contracted spend, that represents $55 million vanishing each year — not from bad deals, but from the operational void between what gets agreed at the negotiating table and what actually gets executed on the ground. Missed savings, unauthorized changes, and poor renewal planning are responsible for the biggest losses.
Jara Montes argues that Traza sits precisely in this gap. “The 11% spans commercial, operational, and compliance leakage. We own the operational layer — and that’s where the most recoverable value sits,” he said. “Supplier tail management that never happens, RFQ processes skipped because someone ran out of bandwidth, invoice discrepancies that slip through unnoticed. That’s where contracts bleed value after signing, and that’s exactly what we automate.” The numbers from Traza’s early deployments, while nascent, are striking: the company claims a 70% reduction in human hours spent on procurement tasks and procurement cycles running three times faster than manual baselines.
To understand what makes Traza’s approach different, it helps to understand what “AI for procurement” has meant until now. For the past several years, the term largely described dashboards, analytics layers, and recommendation engines that surfaced insights but left every decision and action in a human’s hands. Products from incumbents like SAP Ariba and Coupa — as well as newer entrants like Zip, Fairmarkit, and Tonkean — have layered AI capabilities on top of existing systems of record. But the gap between piloting AI and achieving production-scale impact remains stark, with 49 percent of procurement teams running pilots but only 4 percent reaching meaningful deployment.
Traza’s bet is that 2026 represents an inflection point. AI agents now possess the multi-step reasoning, tool use, and contextual memory required to execute full procurement workflows autonomously — from vendor discovery through invoice processing. The company frames this not as an upgrade to existing procurement software, but as an entirely new product category. “The incumbents built systems of record. They organize procurement data and they’ve never executed procurement work — and their AI additions don’t fundamentally change that,” Jara Montes said. “What they’re shipping is a recommendation layer on the same underlying architecture. A human still has to act on every suggestion. We replace the operational layer entirely.”
Industry data supports the thesis that enterprises are hungry for this shift. According to the 2025 Global CPO Survey from EY, 80 percent of global chief procurement officers plan to deploy generative AI in some capacity over the next three years, and 66 percent consider it a high priority over the next 12 months. A 2025 ABI Research survey found that 76% of supply chain professionals already see autonomous AI agents as ready to handle core tasks like reordering, supplier outreach, and shipment rerouting without human intervention — and early deployments are demonstrably reducing supply chain operational costs by 20 to 35%.
In a typical deployment, Traza’s AI agent takes over the operational labor that currently lives in inboxes, spreadsheets, and manual follow-up chains. In a standard RFQ workflow, the agent identifies suitable suppliers, drafts and sends the request for quotes, monitors supplier responses, follows up automatically when responses lag, parses incoming quotes regardless of their format, and builds a structured comparison table ready for a human decision-maker. The key design principle is deliberate: humans remain in the loop at critical junctures.
“At critical steps — approving a purchase order, flagging a compliance issue, committing spend above a threshold — a human is always in the loop,” Jara Montes explained. “That’s not a limitation, it’s the design. It’s how you maintain the auditability enterprises require while moving faster than any manual process could. You earn expanded autonomy over time, as trust is built and results compound.”
When asked about the risk of AI errors — a wrong purchase order or a missed compliance check that could prove costly — Jara Montes was direct: “Anything with meaningful financial or compliance exposure requires human approval before it executes — that’s non-negotiable and baked into the architecture. Below those thresholds, the agent acts autonomously and logs everything.” He added a point that reveals a subtler product insight: “Most procurement operations today are a black box — nobody has a clear picture of what’s happening across the supplier tail. We make it legible.” In other words, the transparency the AI agent provides may itself be a product — giving procurement leaders visibility they have never had into the long tail of supplier relationships that most enterprises simply ignore.
One of the recurring challenges for any enterprise AI startup is the integration question: How do you plug into the deeply entrenched, often decades-old technology stacks that large manufacturers and construction companies rely on? Traza’s answer is to sit on top of existing systems rather than replace them. “We connect via API or direct integration into whatever the customer already runs — ERPs, email, supplier portals. We have reach across more than 200 enterprise tools,” Jara Montes said. “We don’t rip out their system, we sit on top of them.”
The go-to-market motion mirrors this pragmatism. Instead of attempting a big-bang deployment, Traza runs a two-to-three-month proof of value focused on a single, specific workflow. Integrations are built at the key steps that matter for that particular use case, then expanded as the scope of the engagement grows. “We don’t try to connect everything upfront — we compound integrations as we expand scope within each account,” Jara Montes said. “And every integration we build compounds across customers too. Each new deployment makes the next one faster.” Throughout the process, the company works side by side with the customer’s team, managing complexity and helping them transition into a new way of operating. It is a notably high-touch approach for a company selling automation.
The company is already working with large manufacturers and construction companies and says they are paying, though it declines to name them publicly. “We want to earn the right to grow inside each account, not land a pilot that goes nowhere,” Jara Montes said. “That’s how you build something that actually sticks in enterprise.”
Traza enters a market that is rapidly heating up. The leading AI procurement solutions include platforms from Coupa, Ivalua, SAP Ariba, Zip, Zycus, and Fairmarkit. Keelvar provides autonomous sourcing bots capable of launching RFQs, collecting bids, and recommending optimal awards, while Tonkean offers a no-code orchestration platform using NLP and generative AI to streamline procurement intake and tail-spend management. Against this crowded field, Jara Montes draws a sharp distinction between horizontal automation tools and Traza’s focus on physical industry.
“We’re built specifically for the physical industry, where supplier relationships, compliance requirements, and workflow complexity are categorically different from software procurement,” he said. “A generic agent doesn’t survive contact with how procurement actually works in manufacturing or construction. Specificity is the moat.” The competitive dynamics with major incumbents are perhaps even more consequential. SAP Ariba, Coupa, and their peers have massive installed bases and deep enterprise relationships. Jara Montes frames their AI initiatives as surface-level additions to legacy architectures — but whether Traza can convert that framing into market share at scale, especially given the gravitational pull of existing vendor relationships, remains the central strategic question.
Beneath Traza’s product pitch sits a deeper strategic thesis about compounding data advantages. The company describes a two-layered learning architecture: at the agent level, Traza gets smarter across every deployment by absorbing supplier behavior patterns, RFQ response dynamics, pricing anomalies, and workflow edge cases. At the data level, each customer’s information stays fully isolated. “What we’re building is deep operational knowledge of how procurement actually runs in the physical industry — not how it’s supposed to run according to an RFP, but how it really runs, with all the exceptions and workarounds,” Jara Montes said. “That’s extraordinarily hard to replicate if you’re starting from scratch, and it gets harder to catch up with the more deployments we have.”
Traza was co-founded by three Spanish entrepreneurs — Silvestre Jara Montes, Santiago Martínez Bragado, and Sergio Ayala Miñano — who came to the United States through the Exponential Fellowship, a program that brings Europe’s top technical talent to the U.S. to build companies at the frontier of AI. Their backgrounds span both sides of the problem Traza is trying to solve. Jara Montes worked at Amazon and CMA CGM — one of the world’s largest shipping groups — at the intersection of operations strategy and supply chain optimization. Martínez Bragado built and deployed agentic AI at Clarity AI before joining Concourse (backed by a16z, Y Combinator, and CRV) as Founding AI Engineer. Ayala Miñano comes from StackAI, one of the fastest-growing enterprise AI platforms in San Francisco, where he was a Founding Engineer.
None of the founders carry the title of Chief Procurement Officer, a gap that the company acknowledges has occasionally surfaced in buyer conversations. Jara Montes’s response is characteristically direct: “Our work is the answer. The results we’re generating move that conversation quickly.” He noted that the company has senior procurement leaders serving as advisors who have run procurement at the scale of its target customers.
Base10 Partners, the lead investor, is a San Francisco-based venture capital firm that invests in companies automating sectors of what it calls “the Real Economy.” Its portfolio includes Notion, Figma, Nubank, Stripe, and Aurora Solar. Rexhi Dollaku, General Partner at Base10, framed the investment in emphatic terms: “Supply chain and procurement is one of the largest, most underautomated markets in the Real Economy. AI agents are finally capable of doing the work, not just assisting with it.” The supporting cast of investors reinforces the immigrant-founder narrative. Clara Ventures — founded by the executives behind Olapic’s $130 million exit — specifically invests in driven foreign founders building in the United States, and Agell adds operational credibility from building Chartboost into a $100 million revenue business in under three years as a Spanish founder in Silicon Valley.
At $2.1 million, this is a deliberately small round for a company selling to large enterprises with notoriously long procurement cycles. Jara Montes argues it goes further than it appears for structural reasons. “We leverage Europe as a tech talent hub, where we have a deep network of exceptional engineers — people who want to work at the frontier of AI but have far fewer opportunities to do so than their US counterparts,” he said. “We’re not just lean — we’re built to outcompete on capital efficiency while others are burning through runway trying to hire in San Francisco.”
The go-to-market motion is designed for speed to revenue. Proofs of value are scoped, time-bounded, and converted to paying partnerships. The company says it is not running 18-month enterprise sales cycles before seeing a dollar. The milestone for the next raise is explicit: more paying customers, meaningfully stronger annual recurring revenue, and a repeatable sales motion that makes the seed round, as Jara Montes put it, “an obvious conversation.”
Looking ahead, he outlined an ambitious three-year target: 20 to 30 large industrial enterprises in the U.S. and Europe running Traza across their procurement operations, with over a billion dollars in procurement spend flowing through the platform. Whether that vision is achievable depends on several interlocking variables — the pace at which AI agent capabilities continue to improve, the speed of enterprise adoption in a traditionally conservative buyer segment, and Traza’s ability to navigate the competitive gauntlet of incumbents adding AI features and well-funded startups attacking adjacent workflows.
But the underlying math may be on Traza’s side. In procurement, the money that disappears does not look like waste. It vanishes into inefficiency, missed obligations, unmanaged risks, and forgotten commitments — the kind of silent losses that no one tracks because no one has the bandwidth to track them. The traditional mandate of procurement, as currently configured, ends where the value gap begins: at signature. Traza is building an AI workforce that picks up where the humans leave off. For an industry that has spent decades losing $55 million at a time to the back office nobody watches, that might be precisely the point.
Sky takes on Ring with its new standalone Smart Home service. Featuring 1080p video, 30-day cloud storage, and flexible monthly plans
Orico has designed a high-speed SSD enclosure for m.2 NVMe SSDs which uses an inovative fanless passive cooling system while offering Thunderbolt 5 speeds of 80Gbps.
Smartphone designers know what’s coming in 2026: wide foldable phones. That comes with software complications and one company is best placed to deal with this challenge.
A new Samsung Galaxy price shock is destroying the wait and save rule. This is bad news for millions of Android buyers in 2026.
This innovative collar-clip gadget is being launched on Kickstarter and it’s designed to bridge the linguistic gap between humans and their cats and dogs.
Microsoft today launched MAI-Image-2-Efficient, a lower-cost, higher-speed variant of its flagship text-to-image model that the company says delivers production-ready quality at nearly half the price. The release, available immediately in Microsoft Foundry and MAI Playground with no waitlist, marks the fastest turnaround yet from Microsoft’s in-house AI superintelligence team — and the clearest signal that Redmond is serious about building a self-sufficient AI stack that doesn’t depend on OpenAI.
The new model is priced at $5 per million text input tokens and $19.50 per million image output tokens, a roughly 41% reduction from MAI-Image-2’s pricing of $5 and $33, respectively, for those same tiers. Microsoft says the model runs 22% faster than its flagship sibling and achieves 4x greater throughput efficiency per GPU, as measured on NVIDIA H100 hardware at 1024×1024 resolution. The company also claims it outpaces competing hyperscaler models — specifically naming Google’s Gemini 3.1 Flash, Gemini 3.1 Flash Image, and Gemini 3 Pro Image — by an average of 40% on p50 latency benchmarks.
The model is also rolling out across Copilot and Bing, Microsoft said, with additional product surfaces to follow.
Microsoft is positioning MAI-Image-2-Efficient and its flagship MAI-Image-2 as complementary tools rather than replacements for each other — a tiered pairing designed to cover the full spectrum of enterprise image generation needs.
MAI-Image-2-Efficient targets high-volume, cost-sensitive production workloads: product photography, marketing creative, UI mockups, branded asset pipelines, and real-time interactive applications. It handles short-form in-image text like headlines and labels cleanly, according to Microsoft, and is built to operate within the tight latency and budget constraints of batch processing environments. MAI-Image-2, meanwhile, remains the company’s precision instrument — the model you reach for when the brief demands the highest photorealistic fidelity, complex stylization like anime or illustration, or longer, more intricate in-image typography. Microsoft is effectively telling enterprise customers: use the efficient model for your assembly line, and the flagship for your showcase.
This approach mirrors pricing strategies that have worked across the AI industry — OpenAI’s GPT model tiers, Anthropic’s Haiku-Sonnet-Opus lineup, Google’s Flash-Pro distinction — but applies it specifically to image generation, a domain where cost-per-image economics can make or break production deployment at scale.
The speed of this release deserves attention. MAI-Image-2 itself only debuted on MAI Playground on March 19, as VentureBeat previously reported, with broader availability through Microsoft Foundry arriving on April 2 alongside two other new foundation models: MAI-Transcribe-1 (a speech-to-text model supporting 25 languages) and MAI-Voice-1 (an audio generation model). Less than a month later, Microsoft has shipped an optimized production variant.
That cadence suggests the MAI Superintelligence team — the research group led by Mustafa Suleyman, CEO of Microsoft AI, that was formed in November 2025 — is operating more like a startup shipping iterative products than a traditional corporate research lab publishing papers. When Suleyman wrote in his April 2 blog post that the team was “building Humanist AI” with a focus on “optimizing for how people actually communicate, training for practical use,” he appears to have meant it literally: the models aren’t just shipping, they’re shipping fast enough to have product roadmaps.
The early reception for MAI-Image-2 has been notably positive. Decrypt reported in its hands-on review that the model had already reached the No. 3 position on the Arena.ai leaderboard for image generation, trailing only Google and OpenAI. Decrypt’s reviewer noted that the model’s photorealism was “a real strength” and that its text rendering was “a legitimate highlight” that “handled complex typography with far more consistency than we expected.” The review also found that in some direct comparisons, MAI-Image-2 outperformed OpenAI’s GPT-Image on image quality and text rendering despite sitting below it on the leaderboard — an observation that underscores how benchmark rankings don’t always capture real-world utility.
That said, the original model shipped with significant constraints that Decrypt flagged: a 30-second cooldown between generations, a 15-image daily cap in the native UI, only 1:1 aspect ratio output, no image-to-image capabilities, and aggressive content filtering that blocked even innocuous creative prompts. Whether MAI-Image-2-Efficient inherits or relaxes any of these limitations isn’t addressed in today’s announcement, and enterprise customers accessing the model through the Foundry API will likely face different constraints than playground users.
Today’s launch cannot be understood in isolation. It arrives at a moment when the relationship between Microsoft and OpenAI — once the defining partnership of the generative AI era — is visibly fraying at the seams.
Just yesterday, CNBC reported that OpenAI’s newly appointed chief revenue officer, Denise Dresser, sent an internal memo to staff explicitly stating that the Microsoft partnership “has also limited our ability to meet enterprises where they are.” The memo reportedly touted OpenAI’s new alliance with Amazon Web Services and the Bedrock platform as a key growth driver, describing inbound customer demand as “frankly staggering” since the partnership was announced in late February. Microsoft added OpenAI to its list of competitors in its annual report in mid-2024. OpenAI, meanwhile, has diversified its cloud infrastructure across CoreWeave, Google, and Oracle, reducing its dependence on Microsoft Azure.
The MAI model family is the most tangible expression of Microsoft’s side of that strategic uncoupling. When Microsoft can generate production-quality images with its own model at $19.50 per million output tokens, the calculus for continuing to license OpenAI’s image models — and paying OpenAI a share of the resulting revenue — shifts dramatically. Every MAI model that reaches production quality is a line item that Microsoft can potentially move off OpenAI’s balance sheet and onto its own.
The organizational infrastructure to support this shift is already in place. On March 17, as disclosed in communications posted on Microsoft’s official blog, CEO Satya Nadella announced a sweeping reorganization that unified the company’s consumer and commercial Copilot efforts under a single leadership team, with Jacob Andreou elevated to EVP of Copilot reporting directly to Nadella. Critically, the reorganization also refocused Suleyman’s role. As Nadella wrote in his message to employees, the company is “doubling down on our superintelligence mission with the talent and compute to build models that have real product impact, in terms of evals, COGS reduction, as well as advancing the frontier.” That phrase — “COGS reduction” — is corporate-speak for reducing the cost of goods sold, and it points directly to the economic motivation behind models like MAI-Image-2-Efficient. Every dollar Microsoft saves by using its own models instead of licensing from partners flows straight to gross margin.
There’s one more dimension that makes today’s release strategically significant, and it may be the most important one: the rise of AI agents.
TechCrunch reported yesterday that Microsoft is testing ways to integrate OpenClaw-like features into Microsoft 365 Copilot, building toward an always-on agent that can execute multi-step tasks over extended periods. The company has also launched Copilot Cowork (an agent that takes actions within Microsoft 365 apps), Copilot Tasks (an agent for completing multi-step personal productivity tasks), and Agent 365 (referenced in Nadella’s March reorganization memo). Microsoft is expected to showcase these agentic capabilities at its Build conference in June.
In an agentic world — where AI systems don’t just answer questions but execute complex workflows autonomously — image generation becomes a primitive that agents call programmatically, not a standalone product that users interact with manually. An enterprise agent building a marketing campaign might need to generate dozens of product images, create social media assets, produce presentation graphics, and iterate on design concepts, all without human intervention at each step. The economics of that workflow are governed entirely by per-token pricing and latency, which is precisely what MAI-Image-2-Efficient optimizes for. If Microsoft’s vision for Copilot involves agents that generate images as a routine subtask within larger workflows, those agents need image generation that’s fast enough to not create bottlenecks and cheap enough to not blow up cost projections when called thousands of times per day. The 4x efficiency improvement and 41% price cut aren’t just nice marketing numbers — they’re architectural requirements for the agentic future Microsoft is betting the company on.
Several important questions remain unaddressed by today’s announcement. Microsoft didn’t disclose whether MAI-Image-2-Efficient resolves the aspect ratio limitations and aggressive content filtering that reviewers flagged in the original model. The company also didn’t specify whether the quality-to-speed tradeoffs involve visible degradation on complex prompts — the announcement describes “production-ready quality” and “flagship quality” interchangeably, but distillation models of any kind typically involve some quality concession.
The footnotes in the press release also reveal the narrow conditions under which the benchmark claims were tested: efficiency figures were measured on NVIDIA H100 at 1024×1024 with “optimized batch sizes and matched latency targets,” and the latency comparisons against Google models were conducted at p50 (median) rather than p95 or p99, which would capture worst-case performance. Enterprise customers running diverse workloads at varying concurrency levels may see different results. MAI Playground is currently available only in select markets, including the U.S., with EU availability listed as “coming soon.” Copilot integration is underway but not complete. And the enterprise API through Foundry, while live, is still in early deployment.
But the trajectory is unmistakable. In less than five months since the MAI Superintelligence team was announced, Microsoft has shipped a flagship image model, three additional foundation models, and now a cost-optimized production variant — all while reorganizing its entire Copilot organization, navigating a fracturing relationship with its most important AI partner, and laying the groundwork for agentic AI features that could redefine enterprise productivity. Whether all of that is fast enough to catch Anthropic’s momentum, contain OpenAI’s drift toward Amazon, and justify a $600 price target is the multi-hundred-billion-dollar question. But for a company that spent the first two years of the generative AI era mostly reselling someone else’s technology, Microsoft is now doing something it hasn’t done in a long time in AI: shipping its own work, on its own schedule, at its own price — and daring the market to keep up.
Sky’s latest TV is designed to be light but powerful. Here’s all you need to know.
The latest set of Valorant patch notes don’t bring major balance changes, but they do add Discord integration directly into the game.
The software industry is racing to write code with artificial intelligence. It is struggling, badly, to make sure that code holds up once it ships.
A survey of 200 senior site-reliability and DevOps leaders at large enterprises across the United States, United Kingdom, and European Union paints a stark picture of the hidden costs embedded in the AI coding boom. According to Lightrun’s 2026 State of AI-Powered Engineering Report, shared exclusively with VentureBeat ahead of its public release, 43% of AI-generated code changes require manual debugging in production environments even after passing quality assurance and staging tests. Not a single respondent said their organization could verify an AI-suggested fix with just one redeploy cycle; 88% reported needing two to three cycles, while 11% required four to six.
The findings land at a moment when AI-generated code is proliferating across global enterprises at a breathtaking pace. Both Microsoft CEO Satya Nadella and Google CEO Sundar Pichai have claimed that around a quarter of their companies’ code is now AI-generated. The AIOps market — the ecosystem of platforms and services designed to manage and monitor these AI-driven operations — stands at $18.95 billion in 2026 and is projected to reach $37.79 billion by 2031.
Yet the report suggests the infrastructure meant to catch AI-generated mistakes is badly lagging behind AI’s capacity to produce them.
“The 0% figure signals that engineering is hitting a trust wall with AI adoption,” said Or Maimon, Lightrun’s chief business officer, referring to the survey’s finding that zero percent of engineering leaders described themselves as “very confident” that AI-generated code will behave correctly once deployed. “While the industry’s emphasis on increased productivity has made AI a necessity, we are seeing a direct negative impact. As AI-generated code enters the system, it doesn’t just increase volume; it slows down the entire deployment pipeline.”
The dangers are no longer theoretical. In early March 2026, Amazon suffered a series of high-profile outages that underscored exactly the kind of failure pattern the Lightrun survey describes. On March 2, Amazon.com experienced a disruption lasting nearly six hours, resulting in 120,000 lost orders and 1.6 million website errors. Three days later, on March 5, a more severe outage hit the storefront — lasting six hours and causing a 99% drop in U.S. order volume, with approximately 6.3 million lost orders. Both incidents were traced to AI-assisted code changes deployed to production without proper approval.
The fallout was swift. Amazon launched a 90-day code safety reset across 335 critical systems, and AI-assisted code changes must now be approved by senior engineers before they are deployed.
Maimon pointed directly to the Amazon episodes. “This uncertainty isn’t based on a hypothesis,” he said. “We just need to look back to the start of March, when Amazon.com in North America went down due to an AI-assisted change being implemented without established safeguards.”
The Amazon incidents illustrate the central tension the Lightrun report quantifies in survey data: AI tools can produce code at unprecedented speed, but the systems designed to validate, monitor, and trust that code in live environments have not kept pace. Google’s own 2025 DORA report corroborates this dynamic, finding that AI adoption correlates with an increase in code instability, and that 30% of developers report little or no trust in AI-generated code.
Maimon cited that research directly: “Google’s 2025 DORA report found that AI adoption correlates with an almost 10% increase in code instability. Our validation processes were built for the scale of human engineering, but today, engineers have become auditors for massive volumes of unfamiliar code.”
One of the report’s most striking findings is the scale of human capital being consumed by AI-related verification work. Developers now spend an average of 38% of their work week — roughly two full days — on debugging, verification, and environment-specific troubleshooting, according to the survey. For 88% of the companies polled, this “reliability tax” consumes between 26% and 50% of their developers’ weekly capacity.
This is not the productivity dividend that enterprise leaders expected when they invested in AI coding assistants. Instead, the engineering bottleneck has simply migrated. Code gets written faster, but it takes far longer to confirm that it works.
“In some senses, AI has made the debugging problem worse,” Maimon said. “The volume of change is overwhelming human validation, while the generated code itself frequently does not behave as expected when deployed in Production. AI coding agents cannot see how their code behaves in running environments.”
The redeploy problem compounds the time drain. Every surveyed organization requires multiple deployment cycles to verify a single AI-suggested fix — and according to Google’s 2025 DORA report, a single redeploy cycle takes a day to one week on average. In regulated industries such as healthcare and finance, deployment windows are often narrow, governed by mandated code freezes and strict change-management protocols. Requiring three or more cycles to validate a single AI fix can push resolution timelines from days to weeks.
Maimon rejected the idea that these multiple cycles represent prudent engineering discipline. “This is not discipline, but an expensive bottleneck and a symptom of the fact that AI-generated fixes are often unreliable,” he said. “If we can move from three cycles to one, we reclaim a massive portion of that 38% lost engineering capacity.”
If the productivity drain is the most visible cost, the Lightrun report argues the deeper structural problem is what it calls “the runtime visibility gap” — the inability of AI tools and existing monitoring systems to observe what is actually happening inside running applications.
Sixty percent of the survey’s respondents identified a lack of visibility into live system behavior as the primary bottleneck in resolving production incidents. In 44% of cases where AI SRE or application performance monitoring tools attempted to investigate production issues, they failed because the necessary execution-level data — variable states, memory usage, request flow — had never been captured in the first place.
The report paints a picture of AI tools operating essentially blind in the environments that matter most. Ninety-seven percent of engineering leaders said their AI SRE agents operate without significant visibility into what is actually happening in production. Approximately half of all companies (49%) reported their AI agents have only limited visibility into live execution states. Only 1% reported extensive visibility, and not a single respondent claimed full visibility.
This is the gap that turns a minor software bug into a costly outage. When an AI-suggested fix fails in production — as 43% of them do — engineers cannot rely on their AI tools to diagnose the problem, because those tools cannot observe the code’s real-time behavior. Instead, teams fall back on what the report calls “tribal knowledge”: the institutional memory of senior engineers who have seen similar problems before and can intuit the root cause from experience rather than data. The survey found that 54% of resolutions to high-severity incidents rely on tribal knowledge rather than diagnostic evidence from AI SREs or APMs.
The trust deficit plays out with particular intensity in the finance sector. In an industry where a single application error can cascade into millions of dollars in losses per minute, the survey found that 74% of financial-services engineering teams rely on tribal knowledge over automated diagnostic data during serious incidents — far higher than the 44% figure in the technology sector.
“Finance is a heavily regulated, high-stakes environment where a single application error can cost millions of dollars per minute,” Maimon said. “The data shows that these teams simply do not trust AI not to make a dangerous mistake in their Production environments. This is a rational response to tool failure.”
The distrust extends beyond finance. Perhaps the most telling data point in the entire report is that not a single organization surveyed — across any industry — has moved its AI SRE tools into actual production workflows. Ninety percent remain in experimental or pilot mode. The remaining 10% evaluated AI SRE tools and chose not to adopt them at all. This represents an extraordinary gap between market enthusiasm and operational reality: enterprises are spending aggressively on AI for IT operations, but the tools they are buying remain quarantined from the environments where they would deliver the most value.
Maimon described this as one of the report’s most significant revelations. “Leaders are eager to adopt these new AI tools, but they don’t trust AI to touch live environments,” he said. “The lack of trust is shown in the data; 98% have lower trust in AI operating in production than in coding assistants.”
The findings raise pointed questions about the current generation of observability tools from major vendors like Datadog, Dynatrace, and Splunk. Seventy-seven percent of the engineering leaders surveyed reported low or no confidence that their current observability stack provides enough information to support autonomous root cause analysis or automated incident remediation.
Maimon did not shy away from naming the structural problem. “Major vendors often build ‘closed-garden’ ecosystems where their AI SREs can only reason over data collected by their own proprietary agents,” he said. “In a modern enterprise, teams typically have a multi-tool stack to provide full coverage. By forcing a team into a single-vendor silo, these tools create an uncomfortable dependency and a strategic liability: if the vendor’s data coverage is missing a specific layer, the AI is effectively blind to the root cause.”
The second issue, Maimon argued, is that current observability-backed AI SRE solutions offer only partial visibility — defined by what engineers thought to log at the time of deployment. Because failures rarely follow predefined paths, autonomous root cause analysis using only these tools will frequently miss the key diagnostic evidence. “To move toward true autonomous remediation,” he said, “the industry must shift toward AI SRE without vendor lock-in; AI SREs must be an active participant that can connect across the entire stack and interrogate live code to capture the ground truth of a failure as it happens.”
When asked what it would take to trust AI SREs, the survey’s respondents coalesced unanimously around live runtime visibility. Fifty-eight percent said they need the ability to provide “evidence traces” of variables at the point of failure, and 42% cited the ability to verify a suggested fix before it actually deploys. No respondents selected the ability to ingest multiple log sources or provide better natural language explanations — suggesting that engineering leaders do not want AI that talks better, but AI that can see better.
The survey was administered by Global Surveyz Research, an independent firm, and drew responses from Directors, VPs, and C-level executives in SRE and DevOps roles at enterprises with 1,500 or more employees across the finance, technology, and information technology sectors. Responses were collected during January and February 2026, with questions randomized to prevent order bias.
Lightrun, which is backed by $110 million in funding from Accel and Insight Partners and counts AT&T, Citi, Microsoft, Salesforce, and UnitedHealth Group among its enterprise clients, has a clear commercial interest in the problem the report describes: the company sells a runtime observability platform designed to give AI agents and human engineers real-time visibility into live code execution. Its AI SRE product uses a Model Context Protocol connection to generate live diagnostic evidence at the point of failure without requiring redeployment. That commercial interest does not diminish the survey’s findings, which align closely with independent research from Google DORA and the real-world evidence of the Amazon outages.
Taken together, they describe an industry confronting an uncomfortable paradox. AI has solved the slowest part of building software — writing the code — only to reveal that writing was never the hard part. The hard part was always knowing whether it works. And on that question, the engineers closest to the problem are not optimistic.
“If the live visibility gap is not closed, then teams are really just compounding instability through their adoption of AI,” Maimon said. “Organizations that don’t bridge this gap will find themselves stuck with long redeploy loops, to solve ever more complex challenges. They will lose their competitive speed to the very AI tools that were meant to provide it.”
The machines learned to write the code. Nobody taught them to watch it run.