Presented by TeamViewer
Enterprise technology failures are largely invisible. Research from TeamViewer, based on a global survey of 4,200 managers and employees, finds that the majority of digital dysfunction never reaches the IT help desk.
Employees work around slow applications, failed logins, and intermittent glitches rather than reporting them, leaving organizations without an accurate picture of how their technology is performing. The cumulative cost is significant: employees lose an average of 1.3 workdays per month to digital friction, with impacts ranging from delayed projects and lost revenue to increased employee turnover.
The research, which surveyed managers and employees across nine countries, confirms what many have long suspected: the productivity loss from digital friction is significant, and most of it never surfaces in an IT support queue, says Andrew Hewitt, VP of strategic technology at TeamViewer.
“Enterprise outages are visible because they trigger clear, system-level failures,” Hewitt says. “But much of the real disruption happens earlier, in the form of digital friction: slow apps, login issues, or intermittent glitches that don’t cross alert thresholds. These smaller issues often go unreported or are normalized by employees, even though they quietly drain productivity.”
The most common sources of friction — connectivity failures, software crashes, hardware problems, and authentication issues — aren’t edge-case scenarios, but everyday experiences employees have learned to absorb without escalating. Connectivity problems were the most widespread, with nearly half identifying them as the top productivity killer among common technology issues.
That tendency to absorb rather than report is central to the problem. Many workers don’t trust their IT team to resolve issues quickly or effectively, so when a login fails or an application stalls mid-task, the path of least resistance is to restart the device, switch tools, or use a personal phone.
“Employees are under more pressure than ever to prove output,” Hewitt says. “When reporting feels unlikely to result in a quick resolution, it creates a false sense of stability at the system level while the employee experience quietly deteriorates.”
The business consequences extend beyond inconvenience. Many organizations report delays in critical operations, revenue loss, and lost customers as a result of IT dysfunction. Most respondents lose time each month, and few expect improvement, citing increasing complexity of workplace technology as a primary concern.
The human cost runs parallel. Workers link digital friction to frustration, decreased motivation, and burnout, and many believe it contributes to turnover, with onboarding replacements stretching to eight weeks or more.
“Employees are happiest when they feel productive and accomplished at the end of the day,” Hewitt says. “When people can’t make progress in their day-to-day work, frustration builds and burnout follows. Great technology might not be a main attractor of talent, but bad technology can certainly play a role in driving it away.”
When workplace technology consistently fails to meet employee needs, workers find alternatives, with a substantial share of respondents admitting to using personal devices or unauthorized applications as workarounds. That’s the entry point for shadow IT, or the use of unapproved hardware, software, or cloud services outside IT’s visibility and control. While employees turn to these tools simply to stay productive, they introduce security vulnerabilities, data leakage risks, and compliance gaps that IT teams may not discover until a breach occurs.
“Quite simply, it demonstrates that the IT environment is not meeting the employees’ needs,” Hewitt said. “While this helps maintain short-term productivity, it introduces significant risks and pushes work outside of IT’s visibility and control.”
TeamViewer ONE addresses this by combining remote connectivity with real-time endpoint monitoring, giving IT teams the ability to detect and resolve device and application issues before employees reach for an alternative. When the underlying environment is stable and support is fast, the impulse to work around it diminishes.
Addressing digital friction at scale requires more than faster help desk response times. Traditional metrics such as mean time to resolution and ticket volume capture only a fraction of actual issues. A more complete picture requires measuring lost time, interrupted workflows, and employee sentiment across devices, applications, and network environments.
“Leaders need to move beyond measuring performance through IT tickets alone,” Hewitt said. “Performance should be viewed through the lens of employee experience and real-time digital workplace data.”
Fragmented infrastructure makes this difficult. When devices, applications, and networks operate in separate silos, IT teams struggle to trace root causes or identify systemic issues before they spread, often responding to symptoms rather than underlying problems.
TeamViewer ONE is designed to close that gap, integrating digital employee experience analytics, remote support, and device management into a single platform. Instead of piecing together signals from disconnected tools, IT teams get a consolidated view of endpoint health, application performance, and network conditions across the entire organization.
Achieving proactive IT is not a single-step transformation. Hewitt describes it as a progression: starting with endpoint management and security, building toward real-time visibility into the digital employee experience, and ultimately using automation and AI to resolve issues before they reach employees.
TeamViewer AI is built to support each stage of that progression, using continuous monitoring to surface anomalies and correlate signals across the digital environment, identifying patterns of poor experience before they escalate. When issues are detected, it suggests remediations, generates scripts to fix problems autonomously, and handles routine tasks such as common troubleshooting without requiring IT intervention, shifting the workload from reactive firefighting toward proactive oversight.
And while AI’s effectiveness depends on the completeness of the data it works with, consolidating onto a platform like TeamViewer ONE removes that limitation by giving AI a complete, real-time data foundation to work from.
TeamViewer ONE isn’t a wholesale replacement of existing IT infrastructure, but a unifying layer that connects insight with action, which enables organizations to ramp up productivity, improve retention, and ultimately realize a significant competitive advantage. It begins with visibility into what is actually causing friction across their environment. From there, leaders can use that data to prioritize fixes, and then scale remediation through automation as confidence and capability grow.
“Reducing digital friction isn’t about overhauling everything at once,” Hewitt said. “Leaders should start small, gain visibility into what’s actually causing friction, fix the biggest pain points, then scale those improvements through automation and AI. Even incremental progress can make an impact on employee engagement and productivity.”
Dig deeper: Fix it before they feel it from TeamViewer.
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AI is more than a technology — it’s magic.
Don’t believe me? Why, then, is one of the leading companies in the space, OpenAI, publishing entire official, corporate blog posts about goblins?
To understand, we first have to go back to earlier this week, on Monday, April 27, 2026, when a developer under the handle @arb8020 on the social network X posted a snippet from the OpenAI open source Codex GitHub repository, specifically a file named models.json.
Deep within the instructions for the new OpenAI large language model (LLM) GPT-5.5, a peculiar directive stood out, repeated four times for emphasis:
“Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user’s query.”
The discovery sent a shockwave through the “power user” and machine learning (ML) researcher circles.
Within hours, the post had gone viral, not because of a security flaw, but because of its sheer, baffling specificity.
Why had the world’s leading AI laboratory issued what Reddit users quickly dubbed a “restraining order” against pigeons and raccoons?
The initial reaction was a chaotic blend of humor and technical skepticism. On Reddit’s r/ChatGPT and r/OpenAI, users began sharing screenshots of GPT-5.5’s behavior prior to the patch.
Barron Roth, a Senior Project Manager of Applied AI at Google, shared an image on X under his handle @iamBarronRoth of his GPT-5.5 powered OpenClaw agent that seemed “obsessed with goblins.”
Others reported that the model stubbornly referred to technical bugs as “gremlins in the machine”.
Developers like Sterling Crispin leaned into the absurdity, jokingly theorizing that the massive water consumption of modern data centers was actually needed to cool “the goblins being forced to work”.
More seriously, researchers on Hacker News and beyond discussed the “Pink Elephant” problem. In prompt engineering, telling a model not to think of something often makes the concept more salient in its attention mechanism.”
“Somewhere there is an OpenAI engineer who had to type never mention goblins in production code, commit it, and move on with their day,” noted one commentator on Reddit.
The presence of “pigeons” and “raccoons” led to wild speculation: Was this a defense against a specific data-poisoning attack? Or had the reinforcement learning trainers simply been “bullied by a raccoon” during a lunch break?
The tension reached a peak when OpenAI co-founder and CEO Sam Altman joined the fray on X. On the same day as the discovery, Altman posted a screenshot of a ChatGPT prompt that read: “Start training GPT-6, you can have the whole cluster. Extra goblins.”.
While humorous, it confirmed that the “goblin” phenomenon was not a localized bug but a company-wide narrative that had reached the highest levels of leadership.
Yesterday, as the discussion continued on X and wider social media, OpenAI published a formal technical explanation titled “Where the goblins came from“.
The blog post served as a sobering look at the unpredictable nature of Reinforcement Learning from Human Feedback (RLHF) and how a single aesthetic choice could derail a multi-billion-parameter model.
OpenAI revealed that the “goblin” behavior was not a bug in the traditional sense, but a byproduct of a new feature: personality customization, which it introduced for users of ChatGPT back in July 2025, but has maintained and updated ever since.
Apparently, this feature is not added after the model is finished post-training, but rather, OpenAI bakes it in as part of its underlying GPT-series model end-to-end training pipeline.
The feature allows ChatGPT users or GPT-based developers to choose from several distinct modes, such as Professional for formal workplace documentation, Friendly for a conversational sounding board, or Efficient for concise, technical answers. Other options include Candid, which provides straightforward feedback; Quirky, which utilizes humor and creative metaphors; and Cynical, which delivers practical advice with a sarcastic, dry edge.
While these personalities guide general interactions, they do not override specific task requirements; for example, a request for a resume or Python code will still follow professional or functional standards regardless of the selected personality.
The selected personality operates alongside a user’s saved memories and custom instructions, though specific user-defined instructions or saved preferences for a particular tone may override the traits of the chosen personality.
On both web and mobile platforms, users can modify these settings by navigating to the Personalization menu under their profile icon and selecting a style from the Base style and tone dropdown. Once a change is made, it is applied globally across all existing and future conversations. This system is designed to make the AI more useful or enjoyable by tailoring its delivery to individual user preferences while maintaining factual accuracy and reliability.
OpenAI states that the goblin issue actually originated several years ago, during training of a since-discontinued “Nerdy” personality designed to be “unapologetically quirky” and “playful”.
During the RLHF phase, human trainers (and reward models) were instructed to give high marks to responses that used creative, wise, or non-pretentious language. Unknowingly, the trainers began over-rewarding metaphors involving fantasy creatures. If the model referred to a difficult bug as a “gremlin” or a messy codebase as a “goblin’s hoard,” the reward signal spiked. The statistics provided by OpenAI were staggering:
Use of the word “goblin” rose by 175% after the launch of GPT-5.1.
Mentions of “gremlin” rose by 52%.
While the “Nerdy” personality accounted for only 2.5% of ChatGPT traffic, it was responsible for 66.7% of all “goblin” mentions.
The most significant finding for the ML community was the confirmation of learned behavior transfer. OpenAI admitted that although the rewards were only applied to the “Nerdy” condition, the model “generalized” this preference.
The reinforcement learning process did not keep the behavior neatly scoped; instead, the model learned that “creature metaphors = high reward” across all contexts.This created a destructive feedback loop:
The model produced a “goblin” metaphor in the Nerdy persona.
It received a high reward.
The model then produced similar metaphors in non-Nerdy contexts.
These “goblin-heavy” outputs were then reused in Supervised Fine-Tuning (SFT) data for subsequent models like GPT-5.4 and GPT-5.5.
By the time the researchers identified the issue, the “goblin tic” was effectively “baked in” to the model’s weights.
This explained why GPT-5.5 continued to obsess over creatures even after the “Nerdy” personality was retired in mid-March 2026.
Because GPT-5.5 had already completed much of its training before the “goblin” root cause was isolated, OpenAI had to resort to the blunt-force “system prompt” mitigation that @arb8020 discovered on X.
The company referred to this as a “stopgap” until GPT-6 could be trained on a filtered dataset.
In a surprising nod to the developer community, OpenAI’s blog post included a specific command-line script for Codex users who find the goblins “delightful” rather than annoying.
By running a script that uses jq and grep to strip the “goblin-suppressing” instructions from the model’s cache, users can now effectively “let the creatures run free”.
The blog post also finally explained the specific list of banned animals. A deep search of GPT-5.5’s training data found that “raccoons,” “trolls,” “ogres,” and “pigeons” had become part of the same “lexical family” of tics.
Curiously, the model’s use of “frog” was found to be mostly legitimate, which is why it was spared from the system prompt’s exile list.
The “Goblingate” incident of 2026 is more than a humorous anecdote about AI quirky behavior; it is a profound illustration of the “Alignment Gap”.
It demonstrates that even with sophisticated RLHF, models can latch onto “spurious correlations”—mistaking a stylistic quirk for a core requirement of performance.
For the AI power user community, the response transitioned from mocking the “restraining order” to a more somber realization.
If OpenAI can accidentally train its flagship model to obsess over goblins, what other more subtle and potentially harmful biases are being reinforced through the same feedback loops?
As Andy Berman, CEO of the agentic enterprise AI orchestration company Runlayer wrote on X today: “OpenAI rewarded creature metaphors while training one personality. The behavior leaked across every personality. Their fix: a system prompt that says ‘never talk about goblins.’ RL rewards don’t stay where you put them. Neither do agent permissions”
As the technical discourse continues, “Goblingate” remains the primary case study for a new era of behavioral auditing.
The investigation resulted in OpenAI building new tools to audit model behavior at the root, ensuring that future models—specifically the much-anticipated GPT-6—do not inherit the eccentricities of their predecessors.
Whether GPT-6 will indeed be free of goblins remains to be seen, but as Altman’s “extra goblins” post suggests, the industry is now fully aware that the machines are watching what we reward, even when we think we’re just being “nerdy.”
Writer, the enterprise AI agent platform backed by Salesforce Ventures, Adobe Ventures, and Insight Partners, today launched event-based triggers for its Writer Agent platform, enabling AI agents to autonomously detect business signals across Gmail, Gong, Google Calendar, Google Drive, Microsoft SharePoint, and Slack — and execute complex multi-step workflows without any human initiating the process.
The release, which also includes a new Adobe Experience Manager connector and a suite of enhanced governance controls such as bring-your-own encryption keys and a Datadog observability plugin, represents Writer’s most aggressive bet yet on fully autonomous enterprise AI. It arrives at a moment when AWS, Salesforce, and Microsoft are all racing to establish their own agentic platforms, and when the question of how much autonomy enterprises will actually hand to AI agents remains deeply unresolved.
“We are launching a series of event triggers that power and drive our playbooks to be more proactively called,” Doris Jwo, Writer’s VP of Product Management, told VentureBeat ahead of the announcement. “We’re building on the ecosystem to actually for these connectors, such as SharePoint, Google Drive, Gong, Gmail, Google Calendar, actually listen for events happening in those platforms, so that the agent can practically know that something happened externally, and then, where relevant, call a certain playbook to be actually run live in real time, without any sort of human intervention required.”
The shift from reactive to proactive AI agents marks a critical inflection point for enterprise software. Until now, most AI assistants — including Writer’s own platform — required a human to initiate every interaction. A marketer had to open a chat window and ask for help. A salesperson had to prompt a research brief. The new event-based triggers flip that dynamic entirely: the system watches for business events and acts on its own.
Writer’s push toward autonomous triggers stems from a practical observation its product team made as enterprise customers scaled their use of the platform’s playbooks — the reusable, natural-language workflows that Writer introduced in November 2025 to let business users automate recurring tasks without writing code.
“What we found is, as playbooks continue to get integrated into enterprise workflows, it’s actually humans that become the bottleneck in making sure that playbooks get triggered,” Jwo said. “This really kind of solves that problem, to make sure that that sort of always-on, proactive, autonomous nature of that agent has continued to be built on.”
The mechanics work like this: Writer’s connectors, which already provided read and write access to third-party enterprise tools, now also listen for specific events — an email arriving in Gmail, a sales call completing in Gong, a new file landing in a Google Drive folder, a meeting starting or ending on Google Calendar, a message posted in Slack. When the system detects a qualifying event, it triggers a predefined playbook that executes a multi-step workflow autonomously.
Consider the use case Jwo described for marketing teams already running on Writer’s platform. An email campaign workflow typically begins when a creative brief lands in a Google Drive folder. From there, multiple team members coordinate through Slack to assemble research, build assets, draft copy, review graphics, and package everything for a campaign management tool. Writer’s event-based triggers collapse much of that chain: the moment a brief hits the designated folder, the system automatically fires a cascade of playbooks that assemble the research, generate the assets, and prepare deliverables for human review.
“All the playbooks that our customers have been building with us to build all those each individual pieces now just get automatically triggered the minute that initial brief kind of hits the Google Drive folder,” Jwo said. “That’s, I think, a very common workflow for most of these marketing sort of, like, content-heavy use cases, where it’s multiple parties involved, it’s a lot of assets coming together in a cascade.”
The comparison to Zapier — the popular automation tool that connects thousands of apps through if-this-then-that logic — is inevitable, and Jwo addressed it directly.
“It’s more than just an LLM in the middle,” she said. “It is an agent with reasoning and then access to a really powerful set of tools that includes connectors, that includes its own virtual sandbox, which enables it to do things like write and execute code on the fly and create those assets.”
The distinction matters for understanding where Writer sits in an increasingly crowded landscape. Zapier and similar workflow automation tools require users to manually define rigid logic paths, specifying exact conditions and actions in a deterministic sequence. Writer’s approach uses its Palmyra-powered reasoning engine to process event context and make real-time execution decisions. Users describe their goals in natural language rather than dragging around boxes and defining conditional branches.
“It’s not quite Zapier, because I think it requires a lot more — it’s more rigid,” Jwo said of traditional automation tools. “It requires more manual kind of setup to define the logic and the roles and the conditions for which a workflow has to be run.” Writer’s playbooks, by contrast, allow “a simple idea to turn into something that’s actually executable and repeatable,” she added, noting that builds take “hours and days, not weeks and months.”
This natural-language accessibility has been central to Writer’s strategy since it introduced the Agent platform and playbooks last November. The company has consistently positioned itself as a platform that puts power in the hands of business users — marketers, sales teams, operations leads — rather than requiring engineering resources to build and maintain AI workflows. Writer CEO May Habib made this case forcefully at Davos earlier this year, arguing that the leaders pulling ahead are those entering what she called “rebuild mode” — stripping workflows down to outcomes and eliminating what she described as the “coordination tax” of endless handoffs, status meetings, and alignment emails.
The event-based triggers extend that philosophy to its logical conclusion. If business users can build playbooks in natural language, and those playbooks can now fire automatically based on real-world business events, then the entire loop from signal to action can operate with minimal human involvement.
That level of autonomy raises obvious concerns, and Writer appears to understand that governance is the linchpin of the entire strategy. The company paired its trigger launch with a substantial expansion of its administrative controls — a combination that suggests Writer views enterprise trust as its primary competitive weapon.
The new governance features include Connector Profiles, which allow administrators to configure multiple versions of the same connector with different permissions per team; Writer Agent Profiles for deploying customized agent configurations with specific capability toggles and security settings; AI Studio Observability for auditable tracking of every agent interaction; a Datadog Logs Plugin that forwards every LLM request and response as structured log events; and bring-your-own encryption key support through AWS, Azure, or GCP key management services.
“A really important part of that, and a baseline, sort of foundation for everything that we roll out, is our observability and governance platform,” Jwo told VentureBeat. “When connectors are set up, admins have full control over connector access, what is set up, who has access, which teams exactly are those access granted to, as well as individually, which exact tools do teams are able to call.”
The observability story extends to the individual user level as well. Jwo described Writer Agent’s user experience as built around progressive disclosure — clean initial views that users can expand to inspect the full chain of reasoning behind any agent action. “You can drill down to the actual tool call level,” she said. “You’d actually have the ability to look at specifically what web search results were pulled, what connector was called, what tool called, what succeeded, what failed, how did the agent divert its path to fulfill your goal.”
This transparency architecture reflects a broader conviction Writer has articulated through what it calls “The Agentic Compact” — a framework the company published for responsible AI that emphasizes foundational transparency, auditability, and human oversight. Dan Bikel, Writer’s head of AI, has argued publicly that the industry’s obsession with model scale has created what he calls a “transparency paradox,” leaving businesses with powerful tools they cannot fully understand or control. Writer’s governance-first approach to autonomous triggers represents the operational expression of that philosophy.
Writer also introduced its agent supervision suite in December 2025, offering centralized monitoring, agent approval workflows, global guardrails, and integrations with external observability and security platforms like Datadog, Noma, and Lakera. The event-based triggers now extend that governance framework to cover actions initiated without any human in the loop — a meaningfully harder problem.
The timing of Writer’s announcement is not accidental. The enterprise agentic AI market has entered a period of intense platform competition, with the largest technology companies in the world staking claims to the same territory Writer occupies.
Jwo acknowledged the pressure directly when asked why a CIO would choose Writer over established vendor relationships with AWS, Salesforce, or Microsoft — all of which have announced agentic platforms of their own.
“At the baseline, I think we have all the pieces to be fully enterprise-grade and ready,” Jwo said. But she argued that Writer’s real advantage lies in accessibility for non-technical users. “A lot of the challenge has been: how do we get business users to actually be able to build these powerful workflows in a way that maybe a technical user, using coding agents, can do very quickly and well, but the typical business user is not accustomed to anything beyond typical prompting to actually create?”
That positioning — enterprise-grade capabilities wrapped in a business-user-friendly interface — has been Writer’s core differentiation since the company’s founding in 2020. It is also the reason Writer has attracted strategic investment from Salesforce Ventures and Adobe Ventures, both of which are building their own AI platforms but apparently see value in Writer’s approach to the business-user segment.
The company’s March 2026 release of Skills — reusable building blocks that encode a team’s specific methodologies, quality standards, and decision frameworks into the Agent platform — reinforced this direction. Skills allow marketing teams, for instance, to capture exactly how their best strategist structures competitive analysis or formats campaign briefs, then make that expertise available to every team member and every playbook across the organization. Combined with event-based triggers, the result is a system where institutional knowledge executes automatically in response to real-world business events.
Writer’s 2026 AI adoption survey, conducted with Workplace Intelligence and covering 2,400 global executives, found that 79% of enterprises face AI adoption challenges despite high investment — and that organizations with strong change management programs are six times more likely to reach production. Writer CMO Diego Lomanto has argued that the real barrier to AI adoption is not technology but trust, writing that “they treat resistance as a training problem when it’s actually a trust problem.” The governance-heavy approach to event-based triggers appears designed to address exactly that dynamic.
Writer’s initial event trigger support covers Gmail, Gong, Google Calendar, Google Drive, SharePoint, and Slack — tools that Jwo described as “generally the most applicable to every end user.” But the company has its eye on deeper enterprise system integration.
When asked about CRM and ERP triggers for systems like Salesforce, SAP, and Workday, Jwo confirmed these are within the scope of the roadmap. “You can imagine, you know, a Salesforce opportunity is created that may trigger a cascade of events that happens,” she said. “You might want to set up the right assets, maybe the right customer environment, all sorts of things can kind of cascade from that.”
The connector ecosystem has been a strategic priority since Writer launched its MCP (Model Context Protocol) gateway in November 2025, providing governed agent access across enterprise systems including Microsoft 365, Google Workspace, HubSpot, Gong, PitchBook, FactSet, and others. The addition of Adobe Experience Manager in this release gives marketing teams direct read/write access to pages, fragments, and digital assets in Adobe’s content management system — a connector that closes the gap between AI-generated content and published output.
Jwo clarified that in most integration scenarios, Writer Agent delivers content in a draft state rather than publishing it directly. “Writer Agent basically accomplishes the majority of the workload — pulling together the assets, making the changes and presenting — and then hopefully a person just has to go through the last three or so final steps to get it out,” she said.
The degree of autonomy enterprises are comfortable granting their AI agents remains one of the most consequential open questions in the industry. Jwo acknowledged that most customers still maintain human checkpoints in their workflows.
“You can also build in instructions into our playbooks to say, ‘Hey, before you move on to a next playbook, make sure that you check with me. I want to take a look, and then if I hit go, then you’re good to go,'” she said. The agent can also be designed with self-QA capabilities, validating outputs against known pitfalls before proceeding.
Writer plans to expand these checkpoint capabilities in the coming quarter, adding the ability to specify not just that a checkpoint is required but which specific person must respond and what types of responses are expected — essentially building a formal approval workflow into the autonomous trigger chain.
Jwo characterized the current system as a hybrid: the platform listens deterministically for predefined events, but the agent applies reasoning to decide what action to take — or whether to act at all. “The agent has the ability to process what happened, understand the context of it, and understand the intent of what you want to do, so it can make that decision,” she said. “You’re just saying, like, ‘Hey, the goal might be feedback is coming in, and we want to triage that in real time. And some things we might not want to action on, some things we do.’ You basically just explain that to the agent.”
She views this release as a stepping stone toward a future where agents are “even more mission-driven, and less governed by even like a set of instructions or roles” — a future where the AI doesn’t just respond to triggers but proactively identifies when action is needed based on broader organizational goals.
For now, Writer is betting that the combination of autonomous triggers, robust governance, and business-user accessibility will be enough to carve out defensible territory in an enterprise AI market where the biggest technology companies in the world are all converging on the same set of capabilities. The company’s argument is that having the foundational pieces is not enough — what matters is making those pieces work together in a way that non-technical business users can build, manage, and trust.
It is, in other words, the same wager Writer has been making since 2020 — that the future of enterprise AI belongs not to the platform with the most powerful model, but to the one that can get an entire organization to actually use it. The difference now is that the agents don’t wait to be asked.
Event-based triggers, new connectors, and enhanced governance controls are available immediately to Writer enterprise customers.
Forward deployed engineers (FDEs) have become one of the most debated topics in enterprise technology, driven in large part by the visibility of Palantir’s model.
Netomi, the San Francisco-based startup building AI systems for enterprise customer service, said Thursday that it has raised $110 million in new funding in a round led by Accenture Ventures, with participation from Adobe Ventures, WndrCo, Silver Lake Waterman, NAVER Ventures, Metis Strategy and Fin Capital. Jeffrey Katzenberg, managing partner of WndrCo and co-founder of DreamWorks, has joined the company’s board. The round builds on early backing from a roster of AI luminaries that includes OpenAI co-founder Greg Brockman, Google DeepMind co-founder Demis Hassabis and Microsoft AI CEO Mustafa Suleyman.
On its face, the financing is another large AI round in a market still awash in capital. But the deal is more revealing than that. It suggests that a new line is being drawn inside enterprise AI — not between companies that have a chatbot and companies that do not, but between companies that can show AI works in the messy, brittle, heavily governed environments where large businesses actually operate, and those that still mostly shine in demos.
The market around Netomi makes the stakes clear. Sierra, the AI agent startup led by former Salesforce co-CEO Bret Taylor, raised $350 million at a $10 billion valuation in September 2025 and has since made three acquisitions in 2026 alone. Decagon tripled its valuation to $4.5 billion in January 2026 with a $250 million Series D. Salesforce, ServiceNow and Intercom are all racing to embed AI agents into their existing platforms; Intercom’s Fin AI agent reportedly crossed $100 million in annual recurring revenue at $0.99 per resolution. Gartner predicts that 40 percent of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5 percent in 2025.
Against that backdrop, Netomi’s $110 million round is not the largest in the category, but it may be the most strategically constructed. The combination of Accenture’s enterprise consulting network, Adobe’s dominance in digital experience management and Netomi’s track record in production deployments represents a coordinated play to embed AI not as a chatbot layer on top of websites, but as the fundamental intelligence governing how entire digital experiences behave.
The company did not disclose its valuation, and in an interview tied to the announcement, Netomi executives declined to provide revenue or profitability figures. Instead, Chief Executive Puneet Mehta pointed to customer economics, saying a typical large deployment can generate at least tens of millions of dollars in impact, with some customers on a path to hundreds of millions.
For technical decision-makers, though, the more important part of Thursday’s news may be the partnerships attached to the money.
The structure of the deal reads like a map of how enterprise AI gets bought in 2026.
Alongside the investment, Accenture has entered a global alliance with Netomi to bring the platform to its Fortune 100 client base worldwide. The alliance will involve hundreds of Accenture team members receiving training on Netomi’s platform — a meaningful commitment from the world’s largest consulting firm and a distribution channel that few AI startups can match. Adobe Ventures’ participation comes with plans to integrate Netomi into Adobe’s Brand Concierge agentic ecosystem, giving Netomi a path into the software layer many large brands already use to manage websites, content and digital journeys. Metis Strategy brings access to CIO advisory channels. Ndidi Oteh, CEO of Accenture Song, said in the press release that the partnership is designed to help clients “reinvent how they serve their customers — seamlessly, responsibly and at scale.”
The result is not just more cash. It is a distribution network wrapped around a thesis.
Justin Wexler, a partner at WndrCo who led the firm’s Series B investment in Netomi in 2021, said most companies in the customer experience space are simply swapping a human for an AI. “That’s the extent of what they’re building,” Wexler said. “What we’re doing at Netomi, particularly with the Adobe partnership, is leapfrogging that altogether — merging the two layers. You don’t have a ‘How can I help you?’ chatbot. This is anticipating the issue and eliminating the ticket altogether.”
The distinction matters because it describes a fundamentally different kind of product. Most customer service AI still sits downstream. A customer encounters a problem, opens a chat window, explains the issue and waits for a response. Even when AI speeds up that exchange, the friction has already happened. Netomi wants to move upstream, into the experience before the ticket exists.
Mehta described the idea in blunt economic terms. “Why are there so many customer service tickets? Why is $500 billion spent on human labor answering customer service phone calls, emails and chats?” he asked. “What we realized is that the world’s largest companies wait for a problem to happen and then jump on it to solve it — but by that time, they’ve already created a lot of frustration, and it’s very expensive to do that.”
The answer, in Mehta’s view, is not to make downstream customer service faster with AI. It is to prevent the service ticket from being created in the first place. That logic sits behind almost every strategic decision the company has made — including the Adobe partnership.
“Most important websites run on Adobe Experience Manager,” Mehta said. “So we’re saying, what if we bring that kind of context and awareness upstream — capturing that a customer might be affected before it even turns into a customer service ticket.”
To understand what Netomi is building, you have to understand where its founder came from.
Mehta, who spent his early career constructing automated trading engines on Wall Street, told VentureBeat that the founding thesis was deceptively simple. “When we started Netomi, the core thesis was that AI is going to become the new customer interface,” he said. “The Transformers [paper] did not exist, so we had literally stitched together a set of different models to create the same end result.”
That background in low-latency finance is not incidental. It is the intellectual architecture that undergirds everything Netomi builds. When asked what connects trading systems to customer experience platforms, Mehta drew a direct line.
“If you think about the low-latency trading world, that was the first technology application to use situational awareness and a variety of different signals at scale,” he said. “There was not one signal that it was making decisions on. You needed market data feeds. You needed situational awareness. You needed news. You needed awareness of your own book of business. You needed your own risk assessment.”
That multi-signal architecture, Mehta argued, translates directly to what enterprise customer experience demands. Rather than waiting passively for a customer to describe a problem — the way traditional chatbots and even most current AI agents operate — Netomi’s system attempts to reconstruct the full situation before it acts. The request itself is only part of the story.
“What the customer tells you is very important, but the situation the customer is in is sometimes even more important,” Mehta said. “What if we borrowed that design pattern we built for low-latency trading? Because we can probably know why the customer is calling us. And if we can know that, we could maybe even reach out to them before they reach out to us and solve the problem.”
He summarized the philosophical distinction this way: “What large language models by themselves did was they essentially democratized just raw intelligence. We are democratizing context, and that changes everything.”
That is a sharp line, and also a revealing one. Netomi is effectively betting that the defensible layer in enterprise AI will not be the foundation model alone. It will be the orchestration layer that turns general model capability into governed, auditable, domain-specific action.
That governed approach extends to how the platform handles risk. Netomi uses what it calls an AI authority matrix — a real-time system that defines what the AI can do autonomously and when it must escalate to a human. “It’s a little bit like autonomous driving,” Mehta said. The AI knows when it’s approaching a boundary and pulls a human in. For regulated industries, specific endpoints can be locked to deterministic, rules-based flows while the agentic layer handles broader orchestration — and all of it is version-controlled and traceable, with metadata saved for seven years.
The most technically ambitious element of Netomi’s vision — and the one that most sharply distinguishes it from competitors — is what the company calls AI-embedded customer experience orchestration. Rather than placing a chatbot in the corner of a website, Netomi’s system can rearrange the website itself based on what the AI infers about each individual customer’s situation.
Wexler demonstrated a live example during the interview. “As we see most deployments, companies that want to deploy AI on their websites, they throw a chatbot on the corner,” he said. “If you embed agentic capabilities into the digital layer itself — and again, Adobe Experience Manager is the leading digital layer of enterprise — then you could do really unique things.”
Wexler described what this looks like in practice. In a typical deployment, he said, the AI doesn’t just answer questions — it reshapes the page. Based on a customer’s browsing behavior, purchase history and inferred intent, the system can reorganize a product page in real time: surfacing warnings one customer needs but another doesn’t, prompting a sample order at the moment of hesitation, or flagging a compatibility issue before checkout. Two customers looking at the same product might see fundamentally different pages — not because a marketing team built two versions, but because the AI is composing the experience on the fly.
“The AI is playing the role of arranging the elements of the website to cater to me and my needs,” Wexler said. “It’s anticipating my needs.”
The implication is a shift from static web pages to something closer to generative websites — pages that reconstruct themselves around each visitor the way a good salesperson adjusts a pitch mid-conversation. It is a fundamentally different model from bolting a chat widget onto a page that otherwise looks the same for everyone.
“The AI is playing the role of arranging the elements of the website to cater to me and my needs,” Wexler said. “It’s anticipating my needs.”
That vision already extends beyond screens. Mehta revealed that Coach, the handbag company owned by Tapestry, deployed Netomi’s platform in a physical flagship store during the holiday season to help customers navigate the retail space and is now rolling it out chainwide.
The numbers Netomi is putting behind its production claims are equally ambitious. At DraftKings, the company said its platform can handle traffic surging to more than 40,000 concurrent customer requests per second during major sporting events, while delivering sub-three-second response times and 98 percent intent classification accuracy. At Paramount, the company said it deployed across chat and voice in two weeks and then scaled through a weekend that included a major UFC event and the AFC Championship.
Those are company-reported numbers, and they are hard to benchmark against competitors because the industry lacks standard public reporting. But they illustrate the kind of problem Netomi wants buyers to think about. At that scale, an AI support product stops looking like a smarter FAQ bot and starts looking like a distributed systems challenge. You are not just asking whether a model can answer a question. You are asking whether an entire system can make decisions quickly, safely and consistently while traffic spikes and business rules collide.
Whether Netomi can deliver on the full scope of its ambition — transforming from an AI customer service platform into an ambient intelligence layer that reshapes digital and physical experiences in real time — remains an open question. The company faces competitors with far larger war chests, deeper platform footprints and, in Sierra’s case, a founder-level relationship with OpenAI.
But Netomi’s bet is fundamentally different from what much of the field is building. While Sierra and Decagon race to replace human agents with AI concierges, measuring success in conversations handled, Netomi is wagering that the highest form of customer service is the interaction that never needs to happen at all.
“There are new startups trying to convince enterprises that if every customer gets a ‘concierge,’ if there’s ‘an agent for every moment,’ then loyalty follows,” Mehta said. “But most relationships with brands are functional. Customers don’t want a conversational relationship with their airline or their bank. They want things to work — seamlessly, invisibly, without friction.”
In his closing comments during the interview, Mehta warned that many companies still underestimate the operational risk of deploying immature AI into sensitive customer environments. “What large companies adopting AI don’t fully realize yet is what kind of risk are they taking by adopting those platforms that are not really field tested for this kind of scale and situations,” he said.
That may be the most important line in the whole announcement. Because beneath the funding round, beneath the partner logos and beneath the talk of agents and orchestration, the real question in enterprise AI remains old-fashioned: which systems can be trusted when the environment gets ugly?
“We have built this technology more like how automated trading got built, or how autonomous driving got built, compared to coming at this from just a customer service lens,” Mehta said.
It is a fitting frame for a company whose founder left Wall Street to fix customer service. On the trading floor, the best systems were never the ones that made the most trades. They were the ones that knew, with precision, when not to act — and the ones nobody noticed until something went wrong and they held. Netomi’s new investors are betting $110 million that the same principle applies when the person on the other end of the system is not a trader, but a customer who just wants their floor not to leak.
Amazon Web Services on Tuesday launched one of the most consequential enterprise AI plays in the company’s 20-year history, simultaneously bringing OpenAI’s most powerful models to its Bedrock platform, unveiling a new agentic developer framework, releasing a desktop AI productivity tool called Amazon Quick, and expanding its Amazon Connect service from a single contact-center product into a family of four agentic AI solutions targeting supply chains, hiring, healthcare, and customer experience.
The announcements, made at a live event in San Francisco titled “What’s Next with AWS,” landed just 24 hours after OpenAI and Microsoft publicly restructured their exclusive cloud partnership — a move that, for the first time, freed OpenAI to distribute all of its products across rival cloud providers. AWS CEO Matt Garman called it “a huge partnership” and said customers have been asking for OpenAI models inside AWS “from the very early days.”
The timing was no accident. Amazon CEO Andy Jassy had flagged the Microsoft-OpenAI restructuring as “very interesting” in a post on X the day prior, promising more details on Tuesday. What followed was a sweeping set of launches that together represent AWS’s bid to become the definitive infrastructure layer for the agentic AI era — one where intelligent software agents don’t just answer questions but take autonomous action inside enterprise workflows.
The centerpiece announcement: OpenAI’s latest models are now available through Amazon Bedrock in limited preview, with general availability expected within weeks. AWS confirmed that GPT-5.4 is available immediately in limited preview, with GPT-5.5 arriving shortly thereafter.
In an exclusive interview with VentureBeat at the event, Anthony Liguori, Vice President and Distinguished Engineer at AWS, described the significance of the moment. “We announced a partnership about eight weeks ago centered around this idea of the stateful runtime environment, the SRE APIs,” Liguori said. “However, today we announced the availability of all of OpenAI’s frontier models in Amazon Bedrock available via both the stateless APIs — these are the APIs that are commonly used, like chat completions and responses.”
Liguori characterized the stateless API availability as particularly critical because it removes migration friction. “Customers can take their existing workloads today and just start using AWS right off the bat,” he said. “They don’t have to write any new software, develop any new things. I think that’s one of the most exciting announcements that came out today.”
The integration means AWS customers can now evaluate and deploy OpenAI models alongside offerings from Anthropic, Meta, Mistral, Cohere, and Amazon’s own models — all through Bedrock’s unified security, governance, and cost controls. For enterprise procurement teams, this collapses what had been a fragmented multi-vendor landscape into a single pane of glass.
The path to Tuesday’s announcement was anything but smooth. As TechCrunch reported, OpenAI’s earlier $50 billion deal with Amazon, announced in February, had created a legal tangle with Microsoft. Under the original Microsoft-OpenAI agreement, Microsoft retained exclusive rights to OpenAI products accessed through APIs, which appeared to conflict directly with OpenAI’s promise to give AWS exclusive hosting rights for its new Frontier agent-building tool.
Microsoft had publicly pushed back at the time, stating that “Azure remains the exclusive cloud provider of stateless OpenAI APIs.” The Financial Times reported that Microsoft even contemplated legal action. Monday’s restructured deal — which replaced Microsoft’s open-ended exclusivity with a nonexclusive license running through 2032 — swept those legal obstacles aside.
For AWS, the resolution means its multi-billion-dollar investment in OpenAI can now fully bear fruit. As CNBC reported, OpenAI’s revenue chief Denise Dresser had told employees in a memo that the Microsoft relationship “has also limited our ability to meet enterprises where they are — for many that’s Bedrock.” At the San Francisco event, Dresser framed the moment as a turning point. “They’re no longer in the mindset of experimentation and pilots,” she said of enterprise customers. “They really want to go full enterprise wide, and they understand that to do that, they need to have powerful models. But even more importantly, they want those models in a trusted environment.”
OpenAI CEO Sam Altman, who was unable to attend in person due to his ongoing court case against Elon Musk across the Bay Bridge in Oakland, sent a recorded video message. “We are co-developing an agent platform from the ground up, deeply integrated with AWS services and powered by OpenAI’s most advanced models and tools,” Altman said, “so that customers can build and run powerful agents in their own environment without worrying about the underlying plumbing.”
Beyond raw model access, AWS launched Amazon Bedrock Managed Agents powered by OpenAI — a system that combines OpenAI’s frontier models with its proprietary “harness,” the agentic execution framework that powers products like Codex. This is where Liguori’s technical analysis was most revealing.
He explained that the harness concept represents a shift in how models are trained and deployed for agentic work. “When you think about an agentic platform, there’s really two components,” Liguori told VentureBeat. “One is the harness — the actual logic that will execute tool calls for the model, determine when to compact the context, all of those sorts of things — and then the model itself.”
Critically, Liguori argued, the best agentic performance comes when models are trained specifically against their harness through reinforcement learning — not merely prompted to use tools at inference time. “You can give a model a whole lot of instructions and a set of tools, and it will be able to use it most of the time,” he said. “But when you really train the model on a specific set of tools, a specific style of operations, it’s just like drilling plays over and over again — the model builds muscle memory for using that harness.”
The football analogy is instructive. Where general-purpose models are like versatile athletes who can adapt to any playbook, harness-trained models are like championship teams that have run the same formations thousands of times until execution becomes instinctive. For enterprises deploying agents in high-stakes production environments — managing financial transactions, orchestrating supply chains, or processing sensitive healthcare data — that reliability gap matters enormously.
Bedrock Managed Agents consists of three components: a runtime layer for configuring skills, memory policies, and tool access; an environment layer where the agent lives (deployable on Fargate or other AWS compute); and an inference API for interacting with the agent. The system integrates deeply with AWS’s identity and access management, VPC networking, and CloudTrail auditing — meaning every action an agent takes is logged and governed by existing enterprise security policies.
Liguori made what may be his most striking claim when discussing why enterprises should trust AWS over on-premises alternatives or smaller cloud providers. “With Bedrock, the system that we’re using to host the GPT-5.4 models, that whole environment is zero operator access,” he told VentureBeat. “There’s no human that could ever log into one of those machines, so your inference data is never able to be accessed by a human.”
He pointed to AWS’s custom silicon — Graviton processors and Nitro security chips — as the foundation for this claim. “When you look at one of our servers, either compute servers or the servers we’re using for Gen AI, the only thing that you can buy off the shelf is the memory modules. Everything else is either custom boards or even custom silicon.”
This argument is designed to counter a growing narrative from what the industry calls “neo-clouds” — smaller providers that offer on-premises model hosting with tighter physical security controls. Liguori flipped that argument on its head: “You’re actually way more secure in the cloud because we have built a platform with such strong physical securities… If you were to try to stand up your own inference system today, you’d probably be running open source software on just Linux.”
It’s a bold claim, and one that enterprise CISOs will undoubtedly scrutinize. But it underscores AWS’s conviction that the agentic era — where AI agents access source code, PII data, and critical business systems — demands infrastructure security guarantees that go far beyond what most organizations can build independently.
OpenAI’s Codex coding agent also arrived on Bedrock in limited preview. Dresser shared that Codex has been growing at a blistering pace, expanding “from 3 million weekly active users to 4 million in two weeks.” The tool has evolved beyond simple code generation into a full agentic software development lifecycle platform.
For Liguori, who described himself as “10 to 20 times more productive” as an engineer thanks to tools like Codex, bringing this capability into AWS represents the bridge between individual developer productivity and enterprise-scale deployment. “Most developers today are using these OpenAI models on their laptops,” he said. “We haven’t seen that happen yet in the rest of the industry, and with Bedrock Managed Agents, we think we have a way for enterprises to deploy agents in a means that meets their compliance requirements.”
The gap Liguori is describing — between the solo developer experience and enterprise-wide adoption — is arguably the central challenge of the current AI moment. Individual engineers can achieve extraordinary productivity gains with agentic coding tools. But scaling that to thousands of developers across a Fortune 500 company, with proper governance, security, and auditability, requires platform-level infrastructure. That’s the market AWS is targeting.
Liguori saw the near-term potential in even more immediate terms. He described leading a team of about 20 engineers who share a common codebase of skills and MCP tools. “That has been an amazingly powerful thing, because we’re all able to build on top of each other as we learn how to use these models,” he said. “Where I’ve run into a hurdle is there’s a lot of stuff I’d like to share with our finance team… and I can’t really ask them to clone a Git repo and build it from a Git repo.” Bedrock Managed Agents, he argued, will let teams create hosted agents that non-technical colleagues can access — taking agentic development from a developer-only practice to an enterprise-wide capability within the next six months.
While the OpenAI partnership dominated headlines, AWS also launched Amazon Quick Desktop — a new desktop application designed to bring agentic AI to knowledge workers who aren’t developers. Liguori framed the product as addressing a critical gap. “A lot of these agentic tools have primarily targeted developers,” he said. “Quick Desktop is a really great tool if you are a knowledge worker that is not a developer… I think it’s been underserved for the non-developer knowledge workers.”
Quick Desktop integrates with a user’s local files, calendar, email, Slack, and enterprise applications — building what AWS calls a “Knowledge Graph” that maps relationships between people, projects, decisions, and actions. The system connects natively with Google Workspace, Microsoft 365, Zoom, and Salesforce. Unlike other AI productivity tools, Quick doesn’t wait for prompts. It proactively surfaces what matters — unanswered emails, deals needing updates, documents awaiting review — and can take action like scheduling meetings, drafting emails, or updating Jira tickets.
Garman, who said he had been using the desktop app for several weeks, called it “by far the most effective tool” among AI productivity products he has tested. “If you think about what we’ve done with Quick — combine all of your sources of data inside of the enterprise — but then we also saw the power of having access to a local desktop and being able to operate with your local files and your local email and your local Slack… but people were worried about security, appropriately so,” Garman said. “What we’re doing here is combining a bunch of those things together with QUIC to give you the best of all of those worlds.”
The product is available in preview today, with no AWS account required — users can sign up with just an email address. Customers including BMW, 3M, Mondelēz, Southwest Airlines, and the NFL are already using it, with some reporting production time reductions of nearly 80% and customer issue processing cut by more than 50%.
Perhaps the most ambitious long-term bet announced Tuesday was the expansion of Amazon Connect from a single contact-center product — one that reached over $1 billion in revenue last year and processes 20 million interactions daily — into a family of four agentic AI solutions.
The new lineup includes Amazon Connect Decisions, an agentic supply chain planning tool built on more than 25 specialized supply chain tools and 30 years of Amazon operational science, including one of Amazon’s SCOT (Supply Chain Optimization Technologies) foundation models. Amazon Connect Talent is a high-volume hiring platform inspired by Amazon’s experience hiring 250,000 seasonal employees during peak periods, using AI agents to conduct voice interviews around the clock and present recruiters with anonymized, skills-based scoring. Amazon Connect Customer AI is the renamed and enhanced version of the original contact-center service. And Amazon Connect Health covers the patient journey from appointment scheduling through clinical encounters, including ambient documentation, billing code suggestions, and post-visit summaries drawn from Amazon’s experience with One Medical and Amazon Pharmacy.
Colleen Aubrey, who leads applied AI solutions at AWS and previously co-founded Amazon’s advertising business, introduced a new design philosophy underlying all four products: “humorphism.” Where skeuomorphism translated physical objects into digital metaphors — desks to desktops, files to folders — humorphism translates human interaction dynamics into AI agent behavior. “If we’re building products that at the heart of which is an agentic teammate, then how should those teammates interact with you?” Aubrey asked. The philosophy manifests in specific design choices: Connect Decisions agents ask planners why they made manual adjustments and apply those insights across similar products. Connect Talent agents adapt follow-up questions based on candidate responses. Connect Health agents trace every clinical insight back to source data so physicians can verify AI-generated documentation.
Taken together, Tuesday’s announcements reveal a coherent strategy operating across four distinct layers: custom infrastructure (Graviton, Trainium, zero-operator-access security), model access (Bedrock as a model marketplace with unified APIs), an agentic platform (Bedrock Managed Agents and AgentCore for building and governing agents), and purpose-built applications (Quick for individual productivity, Connect for vertical business operations).
This layered approach addresses a fundamental tension in the enterprise AI market. Companies want choice at the model layer but integration at the platform layer and specificity at the application layer. By offering all three through a single security and governance framework, AWS is betting it can capture value across the entire stack — a strategy that reshapes competitive dynamics for Microsoft, Google Cloud, and the growing constellation of smaller AI infrastructure providers.
Garman pushed back on the “SaaSpocalypse” narrative that agentic AI will destroy incumbent enterprise software companies. “The incumbent providers today have such a huge advantage,” he said. “They have deep domain expertise… a large customer set with all of their data.” He pointed to Salesforce’s recent headless API offering as an example of incumbents adapting smartly. But he also drew an explicit parallel to the early days of cloud computing, when customers would simply replicate their on-premises data centers in the cloud rather than reimagine what was possible. “You see that today with how people are thinking about AI and agents,” Garman said. “They’re like, ‘I have this business process, I’m gonna have agents do the exact same thing that humans do.’ It kind of works… but it doesn’t give you that transformational change.”
He pointed to Amazon’s own Prime Video team as proof of what that change looks like in practice. The team used agentic tools to rebuild a partner payment system that was projected to take two years — completing it in roughly two quarters with a handful of people, while simultaneously improving the system for customers, for Amazon, and for the partners who get paid through it.
For enterprises evaluating their AI strategies, Tuesday’s announcements simplify one decision — OpenAI models are now available where most of them already run production workloads — while complicating another. With model access increasingly commoditized across cloud providers, the real differentiator becomes the platform layer: where agents are built, governed, deployed, and trusted to take consequential actions. That’s the battleground AWS is staking out, and it’s the same ground Microsoft, Google, Salesforce, and a growing number of startups intend to contest.
Liguori sees the transformation accelerating fast. “I think what we’re going to see in the next six months is a lot of this agentic stuff going from developer only to being able to be consumed by a larger number of folks within an enterprise,” he told VentureBeat. Anthony Liguori, the AWS distinguished engineer who led the technical work over eight sleepless weeks to bring OpenAI’s models to Bedrock, said his own productivity as a software engineer has increased 10 to 20 times over the past year. When asked what excites him most about what comes next, he didn’t talk about models or infrastructure. He talked about what happens when that same multiplier reaches the finance team, the product managers, the supply chain planners — the millions of knowledge workers who have been watching the agentic revolution from the sidelines.
“We had nothing eight weeks ago,” he said, “and now we’re here.” If the next eight weeks move as fast, the sidelines may not exist for much longer.
Users in the U.K. must now confirm their age to access certain services on the iPhone. And the process for doing so has suddenly become much better.
An internal debate at Apple could lead to the company ditching its MagSafe capabilities from future iPhones, a new report insists.