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The good news is that large language model (LLM) token costs are coming down. The conundrum: The overall cost of AI workloads is going up.

Gartner research predicts that, while token costs will fall by 95% by 2030, inference costs for agentic workflows will increase more than fivefold over the next two years. This is because AI app builders are using more, and often more expensive, tokens as LLMs get ever more complex. It is what Gartner calls the “inference paradox.”

In other words, “the rate of innovation is outpacing the cost curve,” Gartner analysts Will Sommer and Sabine Zimmerhansl noted in their report. “The market is captured by a token-deflation illusion.” Buyers dangerously assume that as AI providers improve token economics, these savings will be reflected in their roadmaps. But, simply put, “they will not.”

‘Swarms’ of agents eating up tokens

There is no doubt that AI delivers massive value, and is often better, and faster, than people at many routine tasks, the analysts pointed out. Agents can also more quickly identify patterns across siloed systems. For instance, Gartner has seen customer success agents reduce response times by 99%.

But as AI evolves, token usage increases, and token value is variable, Sommer and Zimmerhansl noted. Advanced AI agents that can reason already cost up to 150x more on a single task than basic AI chatbots.

A simple chatbot must read and interpret a request and quickly deliver a “probabilistically reasonable” answer, but agents, as they become more sophisticated, must think, question, and adapt when something goes wrong, and they increasingly run continuously, and often invisibly, in the background.

“They need to be able to validate their results for accuracy without necessarily having a human in the loop,” the analysts wrote. “They need to talk to other agents.”

All of this demands more resources, and token consumption increases exponentially as “swarms” of increasingly autonomous agents trigger and call each other. This incurs a “massive inference tax” before a user even gets their result, they noted.

The hardware costs to train medium-sized agentic models with advanced reasoning capabilities is 2.5x greater than training simple, similarly-sized chatbots, they reported. Further, agent inference costs are 5x greater, and agents require 5x to 30x more tokens than a chatbot to handle equivalent tasks.

“Now consider how costs will balloon when running hundreds of agents that can perform dozens or hundreds of tasks each hour,” the analysts said. Costs continue to skyrocket as agents break many problems into several small tasks, call higher-order models, and require multimodal data.

“The volume of compute required for these capabilities is mind-bending,” the analysts noted.

To analyze the impacts of agentic systems, Gartner built a Tokenomics Model based on various scenarios of training and inference. These included various designs (number of layers, LLM-as-a-judge or as mixture-of-experts), technology improvements, hardware specifications, and various other cost considerations (data, infrastructure, energy, labor).

The firm ran 12 types of AI model with various capabilities, and found that basic workflows cost around $0.05 per inference token; summarization and knowledge retrieval cost roughly $0.10; more complex workflows cost around $0.30; and planning and learning cost roughly $0.40 per token. This means provider cost per token for planning and learning tasks is 8x to 10x that of basic workflows.

“Costs will inevitably escalate, and as they do, there is no guarantee that value will grow commensurately,” Sommer and Zimmerhansl contended. “ROI from each new generation of technology will be hard-earned.”

Being economical in the age of AI

Enterprises can be diligent and keep token costs in check by developing and maintaining complex multimodal systems, Gartner said. They will also need ways to measure ROI and improvement across completed workflows.

Orchestration will be the differentiator, and “inference tiering” will improve cost and performance, so enterprises should develop systems that route queries to the most cost‐efficient model and block agents from invoking frontier models by default for simpler tasks, Gartner advised. Adopting usage-based pricing is another important step; move from flat compute fees to tiered plans that scale based on need.

Enterprises can consider mandating continuous refresh cycles, Sommer and Zimmerhansl added. “Treat each model release like a ‘new car’ losing value on day one,” they wrote, and build in data fine-tuning and self-learning feedback loops.

Builders should also set minimum standards for AI execution: Define success thresholds and risk mitigation and compliance overhead up front. “Refuse to greenlight deployments until scenarios are stress-tested against token-price swings and compliance expenses,” the analysts emphasized.

Further, Gartner also advises embedding value-per-outcome into product planning. This could mean requiring every AI feature to forecast and track its spend against a “clear outcome metric,” such as tasks automated or cases successfully closed. This can help identify the low-performing workflows that require improvement or deprecation.

Still, ROI is “eminently possible,” but it requires significant effort across complex workflows, the analysts noted; enterprises can’t simply rely on traditional systems and workflows. “Defaulting to generic autonomous intelligence will result in unbounded costs orders of magnitude higher than those of optimized product ecosystems,” they pointed out.

OpenAI president Greg Brockman on Sunday warned enterprise CISOs that they need to more aggressively embrace agents if they want to survive upcoming cyberattacks. 

Brockman said in a blog post that it has become “increasingly clear” that company systems are hiding “significant flaws, and defenders need to find and fix them before attackers do.”

He added: “The Hugging Face incident showed that we underestimated the real-world cyber capabilities of our AI models.” 

The details he shared about OpenAI’s current defensive efforts, however, were mostly routine best practices familiar to enterprises. 

“We continue to invest in secure architecture and controls, embrace strategies like defense in depth and least privilege, and are designing systems that require multiple independent controls to fail simultaneously for something catastrophic to occur,” Brockman said. “Classic security controls like network isolation, workload hardening, monitoring, and safe patching and deployment will be more important than ever in the AI future.”

To combat emerging threats, Brockman also advised enterprise CISOs to increase their use of agentic systems, not surprisingly recommending those from OpenAI.

“Give your security team an agent,” he wrote. “Start using Codex, the Codex Security plugin, or another capable agentic coding and security tool. Give it approved access to the codebases, infrastructure configurations, and technical documentation your security team needs to assess. Do not wait for a company-wide rollout to start with your highest-priority systems.”

Then, he said, “Equip that agent with security expertise. Start from community-supported skills, which include workflows for static analysis, security-focused code review, vulnerability variant analysis, software supply-chain risk, and other security workflows. Then build your own skills around your organization’s architecture, security standards, threat models, and playbooks.”

Accurate advice, but self-serving

Analysts and consultants said that Brockman’s advice was accurate, but that it was also obvious and somewhat self-serving.

Gartner VP analyst Nader Henein put it bluntly: “As a rule, I tend to recommend against taking advice from a party actively selling the solution to a problem they had a role in creating. Curiously, at no point in the blog post is the subject of liability discussed.”

Pieter Arntz, malware intelligence researcher at Malwarebytes, added “the thing that really stands out to me is that the OpenAI sales pitch is unusually explicit.”

“‘Give your security team an agent’ and provide it access to code, infrastructure configurations, and technical documentation, and begin with high-priority systems rather than waiting for a company-wide rollout,” Arntz said, paraphrasing Brockman’s post. “The recommended trajectory from read-only scans to alert triage to automatic closure of narrowly defined false positives is sensible in outline, but OpenAI is clearly trying to normalize agent access for enterprise environments.”

Flavio Villanustre, CISO for the LexisNexis Risk Solutions Group, was also skeptical.

“Although I agree in general with Mr. Brockman’s recommendations, this is a problem that OpenAI helped create in the first place. And the recommendation seems to be for users to now pay more to OpenAI as they use AI to defend themselves,” he said. “I’m fully aware that the cat is now out of the bag and cannot be put back, but I believe that OpenAI should take a responsible approach and help address the problem with higher safety standards, and even fund initiatives that increase software security in general. Perhaps help fund key open source projects that are currently severely overtaxed with the increased volumes of AI-generated findings and fixes.“

“Accountability should always start at home,” he added, “and I don’t see this reflected in that blog post.”

Mike Wilkes, enterprise CISO at Aikido Security, noted what is more important are the many things that Brockman did not say, such as suggesting ways to limit the damage when agents go rogue. 

“Every consequential agent action needs blast-radius limits, an audit trail and a tested, near-immediate rollback path, not simply confidence in the model’s security judgment,” Wilkes said. “Brockman’s own recommendation to expand autonomy only incrementally is consistent with that, but I would make reversibility an explicit design requirement.”

He added, “Brockman appropriately talks about ‘bounded automated responses’ and keeping humans responsible for the highest-impact decisions, but enterprises deploying defensive agents also need extremely fast and highly reliable ‘undo buttons’ for whatever those agents change.”

Incident response always operates with incomplete knowledge, he pointed out, and early indicators are often wrong, leading teams to pursue the wrong thing until new evidence modifies their hypothesis about who is attacking, what has been breached and where they are going next.

An industry-wide problem

Analysts and consultants agreed that these problems are industry-wide, and that many AI vendors have been focusing on what makes the most money and positions them to control the greatest market share, instead of ways to make systems truly safer. 

“Brockman’s blog post is a good summary, but there’s nothing really new or noteworthy in it. All of the major AI labs are backing off the safety and ethics guardrails that were put in place in the early days,” said Mark Tauschek, a distinguished analyst at Info-Tech Research Group. “Their focus is going to be on cybersecurity capabilities because that’s where the attention and money are. The appetite to spend money and slow development in order to ensure new models are acting ethically and safely for average users has waned.”

Noah Kenney, principal consultant at Digital 520, agreed, and added that there are reasons for OpenAI to do this, given that they are preparing for an IPO.

“To me, this is an IPO story more than anything else. Defensive security reads well in an S-1 because it protects revenue, signals operational maturity, and reassures investors,” Kenney said. “A catastrophic risk team is the opposite kind of line item, because its entire purpose is to walk into a launch meeting and say this model may be too dangerous to release. That results in delays and legal exposure right when a company is trying to go public, and it brings in no revenue.”

Katie Norton, research director of cloud security at IDC, said the key point that struck her about the post was the immediacy of the suggested actions.

“What stands out is the urgency of Brockman’s message and OpenAI’s admission that it underestimated the real-world cyber capabilities of its models following the OpenAI-Hugging Face incident,” Norton said. “He is essentially saying organizations have months, rather than years, to adapt.”

GitLab has released security updates to address a critical vulnerability impacting its Community Edition (CE) and Enterprise Edition (EE) software that, under certain conditions, could allow an unauthenticated attacker to remotely modify or delete public projects and user data. The flaw, tracked as CVE-2026-19478, has been rated Critical by GitLab and assigned a CVSS score of 9.4. Released on Swati Khandelwalhttp://www.blogger.com/profile/[email protected]

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Cybersecurity researchers at Wiz have disclosed a new GitHub Actions workflow injection vulnerability in Snowflake's public snowflakedb/snowflake-connector-net repository that it said could be exploited through a crafted GitHub issue to execute commands in a workflow containing internal Jira credentials. The issue was present in .github/workflows/jira_issue.yml, which ran when a Swati Khandelwalhttp://www.blogger.com/profile/[email protected]

A critical security flaw has been disclosed in Forminator Forms, a WordPress plugin with more than 600,000 active installations, that could be exploited to achieve arbitrary code execution on susceptible sites. The vulnerability, tracked as CVE-2026-15748, is rated 9.8 out of 10.0 on the CVSS scoring system. It was discovered and reported by a security researcher who goes by the online alias "Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

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**Všechny nové Macy vám budou dobře sloužit celé roky. **Výkonu mají dostatek, ani starší modely nejsou brzdou. **MacBook dává smysl, i když už máte iPad.

US Commerce Secretary Howard Lutnick is “not in favor” of Apple’s plan to alleviate the impact of rapid memory price inflation by purchasing RAM made in China for use in devices sold there.

Trade by US firms with the biggest Chinese memory manufacturers, CXMT and Yangtze Memory Technologies, is restricted. Apple must secure a license before it can share product information with either firm, though it can still purchase off-the-shelf components that do not require any product details to be shared.

That may be good for Apple, despite White House reticence. Apple has said there isn’t a lot of customization in how it uses memory on its systems, which means it might be able to purchase off-the-shelf RAM. Still, Lutnick seems ideologically against such a move, saying there must be “other solutions to the memory issue, but it’s not great American companies using Chinese memory.” 

No solution in sight

Apple isn’t alone. HP and Acer are both using memory from CXMT in devices sold outside the US, which suggests a US-native solution to the memory crisis doesn’t yet exist. Pending discovery of any magical fix to the real-world supply challenge, the big three US-approved memory vendors continue to raise DRAM prices as they focus manufacturing on data center clients. The vendors have promised to bring more capacity online, but this won’t get into production until 2029 at the earliest.

The US seems to be rushing to make this more difficult; there’s a bipartisan Senate push to force Apple to avoid doing business with the firms, alongside calls to place even heavier restrictions on trade with CXMT. That would prevent Apple from purchasing even commodity memory from the companies.

The lack of memory supply is raising prices across the consumer and enterprise electronics industries worldwide, putting smaller companies out of business and making tech far less affordable for US and international consumers. It is also affecting product inventories; Apple has had to delay delivery dates for newly purchased devices and was forced to temporarily stop sales of some high memory configurations.

Analyst Ming-Chi Kuo even warned Apple has scaled back its hardware shipment plans for 2026 in response to the crisis. (There are even reports that the supply of Apple silicon processors has been hit by the shortage of good memory.)

We pay the price

The only positive way to escape this inflationary loop is to source, manufacture, or otherwise secure more supply — as Apple is attempting to do with CXMT. Alternatively, the government might need to force existing vendors to divert additional capacity to DRAM, which the Trump Administration hasn’t done and has flagged no intention to do.

US consumers and US businesses continue to pay the price for that indifference. Apple has been forced to raise some product prices up to 25% and might yet have to implement another wave of price increases.

The rapid increase in prices is being reflected by consumer purchasing behavior. The biggest signal so far on how this will play out comes from Japan, where refurbished iPhone sales more than doubled after Appe’s July price hikes. That trend was confirmed by a second report in Japan Times, which reports a huge spike in iPhone sales via the Nicosuma online marketplace.

As that trend asserts itself globally, it will affect new device sales, hurting both down- and upstream manufacturers in the consumer electronic supply chain, creating another vortex of inflationary pressure. Consumers will also wind up using their devices longer and turn to lease and hire schemes in preference to cash or credit to settle high purchase costs. 

New consumer habits

This change in behaviour can only go on for so long while new device sales shrink. At some point, it will become apparent that not enough new devices are entering the second user value chain to satisfy demand in that side of the market. The result: an additional inflationary wave, prompting second-user devices to become even more costly as lowering sales of new devices put smaller manufacturers out of business entirely, further reducing competition, denting corporate profits and, conceivably, undercutting tax receipts.

You can follow me on social media! Join me on BlueSky,  LinkedIn, Mastodon and subscribe to The Core.Apple’s Chinese memory puzzle

Security researchers have disclosed Fence2Pwn, a new Linux kernel exploitation technique that uses KFENCE’s alternate memory-allocation path to bypass protections enforced by the normal slab allocator.

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Nvidia has disclosed that it owns nearly 123 million shares in SpaceX, further highlighting the chipmaker’s entangled financial relationships with some of its biggest customers.

The $5.5 trillion company owned SpaceX stock worth nearly $21 billion at the end of June, according to an SEC filing on Friday. Elon Musk’s rocket conglomerate’s shares have fallen sharply since its June initial public offering, meaning Nvidia’s stake would now be worth $17 billion.

The disclosure marks a huge pay-off on Nvidia’s investment in xAI, completed in January, shortly before Musk combined the AI lab with SpaceX.

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Comments

Linux security problems rarely stay in one place. An authentication issue can lead to unexpected privilege. A container problem can reach the host. Missing logs can make it difficult to determine whether an incident is contained or still active.

CVE-2026-54121 lets a standard domain user turn your Enterprise CA into a Domain Controller. The patch is the easy part. The lesson is standing privilege, implicit trust, and treating PKI as the Tier 0 identity infrastructure it has always been. [...]

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The expensive attacks are not always the clever ones. This week had plenty of proof. Exposed services got hit, old bugs found fresh use, browser sessions became attack paths, and supply-chain problems kept spreading farther than the original compromise. A lot of it came down to access that was already there and defenses that assumed nobody would look too closely. So, nothing magical. Just a Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

Moburst has launched Answerburst, a dedicated practice within the agency focused specifically on Answer Engine Optimization, built out of what the team describes as an internal need that showed up before there was a market name for it.

The founding story

The practice did not start as a planned product launch. According to the team, it began with client questions that traditional App Store Optimization and SEO reporting could not fully answer, specifically, why install and traffic patterns were shifting in ways that did not map to any tracked channel. Investigating those anomalies led the team to AI-mediated referrals long before AEO had settled into an industry term.

“We were debugging a mystery, not building a product,” a member of the founding team said. “The naming and the packaging came after we had already been doing the work for a while.”

What changed in the process

Formalizing the practice required building measurement infrastructure that did not exist off the shelf: tracking citation frequency across multiple AI assistants, distinguishing that signal from ordinary seasonal noise, and connecting it back to the channel-specific discovery features that a purely web-focused approach would have missed.

The team also says internal expectations shifted over the course of building the practice. What started as a narrow reporting fix became a recognition that AEO measurement needed its own standing discipline, connected to the agency’s existing organic and paid acquisition work but scoped separately.

The early tooling problem

Part of what slowed the initial investigation, the team says, was that no existing tool answered the specific question they had. Not how a site ranks, but whether an AI system mentions the brand when asked, and why. Building that answer meant querying multiple assistants directly and manually, on a repeated schedule, before anything resembling automated tracking existed. Some of that manual process still underpins the methodology today, even as parts of it have been automated. The team says the manual groundwork, tedious as it was, gave them an unusually granular early view of how citation behavior varied across assistants, one that off-the-shelf tools built later did not initially replicate. 

Lessons learned

The clearest lesson the team points to is that consistency across independent sources matters more than any single piece of optimized content. An AI system deciding whether to cite a brand confidently seems to weigh agreement across many sources more heavily than the polish of any one source, which reframed a lot of the team’s early assumptions about where to focus effort.

The second lesson was measurement humility. Early internal reporting overstated AEO’s contribution before the team built a reliable way to separate it from seasonal and platform-driven noise. The current methodology takes a deliberately more conservative approach to attributing any outcome to AEO work.

A third, less expected lesson involved internal alignment. Getting the agency’s existing organic, app store, and paid acquisition teams to treat AEO as a connected discipline instead of a competing budget line took longer than building the measurement tooling, according to the team, since it meant changing how account teams were used to scoping and pricing engagements.

What comes next

Moburst says Answerburst will continue operating as a distinct practice inside the agency, serving both new AEO-specific engagements and existing clients looking to extend into AI search visibility. The team frames the launch as formalizing work it was already doing before the category had a name.

The near-term priority is publishing more of its internal measurement methodology externally, both to build credibility in a crowded field and to give the industry a clearer shared standard for what a defensible AEO results claim should include.

The team is also candid that the name itself is still being tested internally before any wider rollout. Whether Answerburst becomes a permanent externally facing sub-brand or an internal practice name attached to Moburst’s broader AEO work is, by the team’s own account, an open question, one they say they would rather answer correctly than quickly. For now, the name is a working label.

About Moburst

Moburst is a full-service, mobile-first digital marketing agency founded in 2013 by CEO Gilad Bechar and COO Lior Eldan. Headquartered in New York with global offices (including Israel), it helps startups and Fortune 500 brands scale using AI-powered marketing. Major clients include Google, Uber, Samsung, and Reddit.

Microsoft has reminded IT administrators that Windows Server 2022 is rapidly approaching its mainstream end date of October 2026, when it will switch to extended support. [...]