The latest big headline in AI isn’t model size or multimodality — it’s the capacity crunch. At VentureBeat’s latest AI Impact stop in NYC, Val Bercovici, chief AI officer at WEKA, joined Matt Marshall, VentureBeat CEO, to discuss what it really takes to scale AI amid rising latency, cloud lock-in, and runaway costs.

Those forces, Bercovici argued, are pushing AI toward its own version of surge pricing. Uber famously introduced surge pricing, bringing real-time market rates to ridesharing for the first time. Now, Bercovici argued, AI is headed toward the same economic reckoning — especially for inference — when the focus turns to profitability.

"We don't have real market rates today. We have subsidized rates. That’s been necessary to enable a lot of the innovation that’s been happening, but sooner or later — considering the trillions of dollars of capex we’re talking about right now, and the finite energy opex — real market rates are going to appear; perhaps next year, certainly by 2027," he said. "When they do, it will fundamentally change this industry and drive an even deeper, keener focus on efficiency."

The economics of the token explosion

"The first rule is that this is an industry where more is more. More tokens equal exponentially more business value," Bercovici said.

But so far, no one's figured out how to make that sustainable. The classic business triad — cost, quality, and speed — translates in AI to latency, cost, and accuracy (especially in output tokens). And accuracy is non-negotiable. That holds not only for consumer interactions with agents like ChatGPT, but for high-stakes use cases such as drug discovery and business workflows in heavily regulated industries like financial services and healthcare.

"That’s non-negotiable," Bercovici said. "You have to have a high amount of tokens for high inference accuracy, especially when you add security into the mix, guardrail models, and quality models. Then you’re trading off latency and cost. That’s where you have some flexibility. If you can tolerate high latency, and sometimes you can for consumer use cases, then you can have lower cost, with free tiers and low cost-plus tiers."

However, latency is a critical bottleneck for AI agents. “These agents now don't operate in any singular sense. You either have an agent swarm or no agentic activity at all,” Bercovici noted.

In a swarm, groups of agents work in parallel to complete a larger objective. An orchestrator agent — the smartest model — sits at the center, determining subtasks and key requirements: architecture choices, cloud vs. on-prem execution, performance constraints, and security considerations. The swarm then executes all subtasks, effectively spinning up numerous concurrent inference users in parallel sessions. Finally, evaluator models judge whether the overall task was successfully completed.

“These swarms go through what's called multiple turns, hundreds if not thousands of prompts and responses until the swarm convenes on an answer,” Bercovici said.

“And if you have a compound delay in those thousand turns, it becomes untenable. So latency is really, really important. And that means typically having to pay a high price today that's subsidized, and that's what's going to have to come down over time.”

Reinforcement learning as the new paradigm

Until around May of this year, agents weren't that performant, Bercovici explained. And then context windows became large enough, and GPUs available enough, to support agents that could complete advanced tasks, like writing reliable software. It's now estimated that in some cases, 90% of software is generated by coding agents. Now that agents have essentially come of age, Bercovici noted, reinforcement learning is the new conversation among data scientists at some of the leading labs, like OpenAI, Anthropic, and Gemini, who view it as a critical path forward in AI innovation..

"The current AI season is reinforcement learning. It blends many of the elements of training and inference into one unified workflow,” Bercovici said. “It’s the latest and greatest scaling law to this mythical milestone we’re all trying to reach called AGI — artificial general intelligence,” he added. "What’s fascinating to me is that you have to apply all the best practices of how you train models, plus all the best practices of how you infer models, to be able to iterate these thousands of reinforcement learning loops and advance the whole field."

The path to AI profitability

There’s no one answer when it comes to building an infrastructure foundation to make AI profitable, Bercovici said, since it's still an emerging field. There’s no cookie-cutter approach. Going all on-prem may be the right choice for some — especially frontier model builders — while being cloud-native or running in a hybrid environment may be a better path for organizations looking to innovate agilely and responsively. Regardless of which path they choose initially, organizations will need to adapt their AI infrastructure strategy as their business needs evolve.

"Unit economics are what fundamentally matter here," said Bercovici. "We are definitely in a boom, or even in a bubble, you could say, in some cases, since the underlying AI economics are being subsidized. But that doesn’t mean that if tokens get more expensive, you’ll stop using them. You’ll just get very fine-grained in terms of how you use them."

Leaders should focus less on individual token pricing and more on transaction-level economics, where efficiency and impact become visible, Bercovici concludes.

The pivotal question enterprises and AI companies should be asking, Bercovici said, is “What is the real cost for my unit economics?”

Viewed through that lens, the path forward isn’t about doing less with AI — it’s about doing it smarter and more efficiently at scale.

The massive investment in third-party infrastructure providers reflects an industrywide capacity crunch, with Microsoft doubling its neocloud spending commitments since October.

This structured checklist provides actionable security controls across five essential categories to eliminate vulnerabilities and prevent catastrophic downtime, breaches, and regulatory penalties.

The intelligence of AI models isn't what's blocking enterprise deployments. It's the inability to define and measure quality in the first place.

That's where AI judges are now playing an increasingly important role. In AI evaluation, a "judge" is an AI system that scores outputs from another AI system. 

Judge Builder is Databricks' framework for creating judges and was first deployed as part of the company's Agent Bricks technology earlier this year. The framework has evolved significantly since its initial launch in response to direct user feedback and deployments.

Early versions focused on technical implementation but customer feedback revealed the real bottleneck was organizational alignment. Databricks now offers a structured workshop process that guides teams through three core challenges: getting stakeholders to agree on quality criteria, capturing domain expertise from limited subject matter experts and deploying evaluation systems at scale.

"The intelligence of the model is typically not the bottleneck, the models are really smart," Jonathan Frankle, Databricks' chief AI scientist, told VentureBeat in an exclusive briefing. "Instead, it's really about asking, how do we get the models to do what we want, and how do we know if they did what we wanted?"

The 'Ouroboros problem' of AI evaluation

Judge Builder addresses what Pallavi Koppol, a Databricks research scientist who led the development, calls the "Ouroboros problem."  An Ouroboros is an ancient symbol that depicts a snake eating its own tail. 

Using AI systems to evaluate AI systems creates a circular validation challenge.

"You want a judge to see if your system is good, if your AI system is good, but then your judge is also an AI system," Koppol explained. "And now you're saying like, well, how do I know this judge is good?"

The solution is measuring "distance to human expert ground truth" as the primary scoring function. By minimizing the gap between how an AI judge scores outputs versus how domain experts would score them, organizations can trust these judges as scalable proxies for human evaluation.

This approach differs fundamentally from traditional guardrail systems or single-metric evaluations. Rather than asking whether an AI output passed or failed on a generic quality check, Judge Builder creates highly specific evaluation criteria tailored to each organization's domain expertise and business requirements.

The technical implementation also sets it apart. Judge Builder integrates with Databricks' MLflow and prompt optimization tools and can work with any underlying model. Teams can version control their judges, track performance over time and deploy multiple judges simultaneously across different quality dimensions.

Lessons learned: Building judges that actually work

Databricks' work with enterprise customers revealed three critical lessons that apply to anyone building AI judges.

Lesson one: Your experts don't agree as much as you think. When quality is subjective, organizations discover that even their own subject matter experts disagree on what constitutes acceptable output. A customer service response might be factually correct but use an inappropriate tone. A financial summary might be comprehensive but too technical for the intended audience.

"One of the biggest lessons of this whole process is that all problems become people problems," Frankle said. "The hardest part is getting an idea out of a person's brain and into something explicit. And the harder part is that companies are not one brain, but many brains."

The fix is batched annotation with inter-rater reliability checks. Teams annotate examples in small groups, then measure agreement scores before proceeding. This catches misalignment early. In one case, three experts gave ratings of 1, 5 and neutral for the same output before discussion revealed they were interpreting the evaluation criteria differently.

Companies using this approach achieve inter-rater reliability scores as high as 0.6 compared to typical scores of 0.3 from external annotation services. Higher agreement translates directly to better judge performance because the training data contains less noise.

Lesson two: Break down vague criteria into specific judges. Instead of one judge evaluating whether a response is "relevant, factual and concise," create three separate judges. Each targets a specific quality aspect. This granularity matters because a failing "overall quality" score reveals something is wrong but not what to fix.

The best results come from combining top-down requirements such as regulatory constraints, stakeholder priorities, with bottom-up discovery of observed failure patterns. One customer built a top-down judge for correctness but discovered through data analysis that correct responses almost always cited the top two retrieval results. This insight became a new production-friendly judge that could proxy for correctness without requiring ground-truth labels.

Lesson three: You need fewer examples than you think. Teams can create robust judges from just 20-30 well-chosen examples. The key is selecting edge cases that expose disagreement rather than obvious examples where everyone agrees.

"We're able to run this process with some teams in as little as three hours, so it doesn't really take that long to start getting a good judge," Koppol said.

Production results: From pilots to seven-figure deployments

Frankle shared three metrics Databricks uses to measure Judge Builder's success: whether customers want to use it again, whether they increase AI spending and whether they progress further in their AI journey.

On the first metric, one customer created more than a dozen judges after their initial workshop. "This customer made more than a dozen judges after we walked them through doing this in a rigorous way for the first time with this framework," Frankle said. "They really went to town on judges and are now measuring everything."

For the second metric, the business impact is clear. "There are multiple customers who have gone through this workshop and have become seven-figure spenders on GenAI at Databricks in a way that they weren't before," Frankle said.

The third metric reveals Judge Builder's strategic value. Customers who previously hesitated to use advanced techniques like reinforcement learning now feel confident deploying them because they can measure whether improvements actually occurred.

"There are customers who have gone and done very advanced things after having had these judges where they were reluctant to do so before," Frankle said. "They've moved from doing a little bit of prompt engineering to doing reinforcement learning with us. Why spend the money on reinforcement learning, and why spend the energy on reinforcement learning if you don't know whether it actually made a difference?"

What enterprises should do now

The teams successfully moving AI from pilot to production treat judges not as one-time artifacts but as evolving assets that grow with their systems.

Databricks recommends three practical steps. First, focus on high-impact judges by identifying one critical regulatory requirement plus one observed failure mode. These become your initial judge portfolio.

Second, create lightweight workflows with subject matter experts. A few hours reviewing 20-30 edge cases provides sufficient calibration for most judges. Use batched annotation and inter-rater reliability checks to denoise your data.

Third, schedule regular judge reviews using production data. New failure modes will emerge as your system evolves. Your judge portfolio should evolve with them.

"A judge is a way to evaluate a model, it's also a way to create guardrails, it's also a way to have a metric against which you can do prompt optimization and it's also a way to have a metric against which you can do reinforcement learning," Frankle said. "Once you have a judge that you know represents your human taste in an empirical form that you can query as much as you want, you can use it in 10,000 different ways to measure or improve your agents."

When the transformer architecture was introduced in 2017 in the now seminal Google paper "Attention Is All You Need," it became an instant cornerstone of modern artificial intelligence.

Every major large language model (LLM) — from OpenAI's GPT series to Anthropic's Claude, Google's Gemini, and Meta's Llama — has been built on some variation of its central mechanism: attention, the mathematical operation that allows a model to look back across its entire input and decide what information matters most.

Eight years later, the same mechanism that defined AI’s golden age is now showing its limits. Attention is powerful, but it is also expensive — its computational and memory costs scale quadratically with context length, creating an increasingly unsustainable bottleneck for both research and industry. As models aim to reason across documents, codebases, or video streams lasting hours or days, attention becomes the architecture’s Achilles’ heel.

On October 28, 2025, the little-known AI startup Manifest AI introduced a radical alternative. Their new model, Brumby-14B-Base, is a retrained variant of Qwen3-14B-Base, one of the leading open-source transformer models.

But while many variants of Qwen have been trained already, Brumby-14B-Base is novel in that it abandons attention altogether.

Instead, Brumby replaces those layers with a novel mechanism called Power Retention—a recurrent, hardware-efficient architecture that stores and updates information over arbitrarily long contexts without the exponential memory growth of attention.

Trained at a stated cost of just $4,000, the 14-billion-parameter Brumby model performs on par with established transformer models like Qwen3-14B and GLM-4.5-Air, achieving near-state-of-the-art accuracy on a range of reasoning and comprehension benchmarks.

From Attention to Retention: The Architectural Shift

The core of Manifest AI’s innovation lies in what they call the Power Retention layer.

In a traditional transformer, every token computes a set of queries (Q), keys (K), and values (V), then performs a matrix operation that measures the similarity between every token and every other token—essentially a full pairwise comparison across the sequence.

This is what gives attention its flexibility, but also what makes it so costly: processing a sequence twice as long takes roughly four times the compute and memory.

Power Retention keeps the same inputs (Q, K, V), but replaces the global similarity operation with a recurrent state update.

Each layer maintains a memory matrix S, which is updated at each time step according to the incoming key, value, and a learned gating signal.

The process looks more like an RNN (Recurrent Neural Network) than a transformer: instead of recomputing attention over the entire context, the model continuously compresses past information into a fixed-size latent state.

This means the computational cost of Power Retention does not grow with context length. Whether the model is processing 1,000 or 1,000,000 tokens, the per-token cost remains constant.

That property alone—constant-time per-token computation—marks a profound departure from transformer behavior.

At the same time, Power Retention preserves the expressive power that made attention successful. Because the recurrence involves tensor powers of the input (hence the name “power retention”), it can represent higher-order dependencies between past and present tokens.

The result is an architecture that can theoretically retain long-term dependencies indefinitely, while remaining as efficient as an RNN and as expressive as a transformer.

Retraining, Not Rebuilding

Perhaps the most striking aspect of Brumby-14B’s training process is its efficiency. Manifest AI trained the model for only 60 hours on 32 Nvidia H100 GPUs, at a cost of roughly $4,000 — less than 2% of what a conventional model of this scale would cost to train from scratch.

However, since it relied on a transformer-based model, it's safe to say that this advance alone will not end the transformer AI-era.

As Jacob Buckman, founder of Manifest AI, clarified in an email to VentureBeat: “The ability to train for $4,000 is indeed only possible when leveraging an existing transformer model,” he said. “Brumby could not be trained from scratch for that price.”

Still, Buckman emphasized the significance of that result: “The reason this is important is that the ability to build on the weights of the previous generation of model architectures is a critical accelerant for the adoption of a new modeling paradigm.”

He argues this demonstrates how attention-free systems can catch up to transformer performance “for orders-of-magnitude less” investment.

In the loss curves released by Manifest AI, Brumby’s training loss quickly converges to that of the Qwen3 baseline within 3,000 training steps, even as the architecture diverges significantly from its transformer origins.

Although Brumby-14B-Base began life as Qwen3-14B-Base, it did not remain identical for long. Manifest AI fundamentally altered Qwen3’s architecture by removing its attention layers—the mathematical engine that defines how a transformer model processes information—and replacing them with their new “power retention” mechanism. This change restructured the model’s internal wiring, effectively giving it a new brain while preserving much of its prior knowledge.

Because of that architectural swap, the existing Qwen3 weights no longer fit perfectly. They were trained to operate within a transformer’s attention dynamics, not the new retention-based system. As a result, the Brumby model initially “forgot” how to apply some of its learned knowledge effectively. The retraining process—about 3,000 steps of additional learning—served to recalibrate those weights, aligning them with the power retention framework without having to start from zero.

A helpful way to think about this is to imagine taking a world-class pianist and handing them a guitar. They already understand rhythm, harmony, and melody, but their hands must learn entirely new patterns to produce the same music. Similarly, Brumby had to relearn how to use its existing knowledge through a new computational instrument. Those 3,000 training steps were, in effect, its crash course in guitar lessons.

By the end of this short retraining phase, Brumby had regained its full performance, reaching the same accuracy as the original Qwen3 model. That quick recovery is what makes the result so significant: it shows that an attention-free system can inherit and adapt the capabilities of a transformer model with only a fraction of the training time and cost.

The benchmark progression plots show a similar trend: the model rapidly approaches its target accuracy on core evaluations like GSM8K, HellaSwag, and MMLU after only a few thousand steps, matching or even slightly surpassing Qwen3 on several tasks.

Benchmarking the Brumby

Across standard evaluation tasks, Brumby-14B-Base consistently performs at or near parity with transformer baselines of comparable scale.

Task	Brumby-14B	Qwen3-14B	GLM-4.5-Air	Nemotron Nano (12B)
ARC	0.89	0.94	0.92	0.93
GSM8K	0.88	0.84	0.83	0.84
GSM8K (Platinum)	0.87	0.88	0.85	0.87
HellaSwag	0.77	0.81	0.85	0.82
MATH	0.62	0.54	0.47	0.26
MBPP	0.57	0.75	0.73	0.71
MMLU	0.71	0.78	0.77	0.78
MMLU (Pro)	0.36	0.55	0.51	0.53

While it lags slightly behind transformers on knowledge-heavy evaluations like MMLU-Pro, it matches or outperforms them on mathematical reasoning and long-context reasoning tasks—precisely where attention architectures tend to falter. This pattern reinforces the idea that recurrent or retention-based systems may hold a structural advantage for reasoning over extended temporal or logical dependencies.

Hardware Efficiency and Inference Performance

Brumby’s power retention design offers another major advantage: hardware efficiency.

Because the state update involves only local matrix operations, inference can be implemented with linear complexity in sequence length.

Manifest AI reports that their fastest kernels, developed through their in-house CUDA framework Vidrial, can deliver hundreds-fold speedups over attention on very long contexts.

Buckman said the alpha-stage Power Retention kernels “achieve typical hardware utilization of 80–85%, which is higher than FlashAttention2’s 70–75% or Mamba’s 50–60%.”

(Mamba is another emerging “post-transformer” architecture developed by Carnegie Mellon scientists back in 2023 that, like Power Retention, seeks to eliminate the computational bottleneck of attention. It replaces attention with a state-space mechanism that processes sequences linearly — updating an internal state over time rather than comparing every token to every other one. This makes it far more efficient for long inputs, though it typically achieves lower hardware utilization than Power Retention in early tests.)

Both Power Retention and Mamba, he added, “expend meaningfully fewer total FLOPs than FlashAttention2 on long contexts, as well as far less memory.”

According to Buckman, the reported 100× speedup comes from this combined improvement in utilization and computational efficiency, though he noted that “we have not yet stress-tested it on production-scale workloads.”

Training and Scaling Economics

Perhaps no statistic in the Brumby release generated more attention than the training cost.

A 14-billion-parameter model, trained for $4,000, represents a two-order-of-magnitude reduction in the cost of foundation model development.

Buckman confirmed that the low cost reflects a broader scaling pattern. “Far from diminishing returns, we have found that ease of retraining improves with scale,” he said. “The number of steps required to successfully retrain a model decreases with its parameter count.”

Manifest has not yet validated the cost of retraining models at 700B parameters, but Buckman projected a range of $10,000–$20,000 for models of that magnitude—still far below transformer training budgets.

He also reiterated that this approach could democratize large-scale experimentation by allowing smaller research groups or companies to retrain or repurpose existing transformer checkpoints without prohibitive compute costs.

Integration and Deployment

According to Buckman, converting an existing transformer into a Power Retention model is designed to be simple.

“It is straightforward for any company that is already retraining, post-training, or fine-tuning open-source models,” he said. “Simply pip install retention, change one line of your architecture code, and resume training where you left off.”

He added that after only a small number of GPU-hours, the model typically recovers its original performance—at which point it gains the efficiency benefits of the attention-free design.

“The resulting architecture will permit far faster long-context training and inference than previously,” Buckman noted.

On infrastructure, Buckman said the main Brumby kernels are written in Triton, compatible with both NVIDIA and AMD accelerators. Specialized CUDA kernels are also available through the team’s in-house Vidrial framework. Integration with vLLM and other inference engines remains a work in progress: “We have not yet integrated Power Retention into inference engines, but doing so is a major ongoing initiative at Manifest.”

As for distributed inference, Buckman dismissed concerns about instability: “We have not found this difficulty to be exacerbated in any way by our recurrent-state architecture. In fact, context-parallel training and GPU partitioning for multi-user inference both become significantly cleaner technically when using our approach.”

Mission and Long-Term Vision

Beyond the engineering details, Buckman also described Manifest’s broader mission. “Our mission is to train a neural network to model all human output,” he said.

The team’s goal, he explained, is to move beyond modeling “artifacts of intelligence” toward modeling “the intelligent processes that generated them.” This shift, he argued, requires “fundamentally rethinking” how models are designed and trained—work that Power Retention represents only the beginning of.

The Brumby-14B release, he said, is “one step forward in a long march” toward architectures that can model thought processes continuously and efficiently.

Public Debate and Industry Reception

The launch of Brumby-14B sparked immediate discussion on X (formerly Twitter), where researchers debated the framing of Manifest AI’s announcement.

Some, including Meta researcher Ariel (@redtachyon), argued that the “$4,000 foundation model” tagline was misleading, since the training involved reusing pretrained transformer weights rather than training from scratch.

“They shuffled around the weights of Qwen, fine-tuned it a bit, and called it ‘training a foundation model for $4k,’” Ariel wrote.

Buckman responded publicly, clarifying that the initial tweet had been part of a longer thread explaining the retraining approach. “It’s not like I was being deceptive about it,” he wrote. “I broke it up into separate tweets, and now everyone is mad about the first one.”

In a follow-up email, Buckman took a measured view of the controversy. “The end of the transformer era is not yet here,” he reiterated, “but the march has begun.”

He also acknowledged that the $4,000 claim, though technically accurate in context, had drawn attention precisely because it challenged expectations about what it costs to experiment at frontier scale.

Conclusion: A Crack in the Transformer’s Wall?

The release of Brumby-14B-Base is more than an engineering milestone; it is a proof of concept that the transformer’s dominance may finally face credible competition.

By replacing attention with power retention, Manifest AI has demonstrated that performance parity with state-of-the-art transformers is possible at a fraction of the computational cost—and that the long-context bottleneck can be broken without exotic hardware.

The broader implications are twofold. First, the economics of training and serving large models could shift dramatically, lowering the barrier to entry for open research and smaller organizations.

Second, the architectural diversity of AI models may expand again, reigniting theoretical and empirical exploration after half a decade of transformer monoculture.

As Buckman put it: “The end of the transformer era is not yet here. Our release is just one step forward in a long march toward the future.”

Market researchers have embraced artificial intelligence at a staggering pace, with 98% of professionals now incorporating AI tools into their work and 72% using them daily or more frequently, according to a new industry survey that reveals both the technology's transformative promise and its persistent reliability problems.

The findings, based on responses from 219 U.S. market research and insights professionals surveyed in August 2025 by QuestDIY, a research platform owned by The Harris Poll, paint a picture of an industry caught between competing pressures: the demand to deliver faster business insights and the burden of validating everything AI produces to ensure accuracy.

While more than half of researchers — 56% — report saving at least five hours per week using AI tools, nearly four in ten say they've experienced "increased reliance on technology that sometimes produces errors." An additional 37% report that AI has "introduced new risks around data quality or accuracy," and 31% say the technology has "led to more work re-checking or validating AI outputs."

The disconnect between productivity gains and trustworthiness has created what amounts to a grand bargain in the research industry: professionals accept time savings and enhanced capabilities in exchange for constant vigilance over AI's mistakes, a dynamic that may fundamentally reshape how insights work gets done.

How market researchers went from AI skeptics to daily users in less than a year

The numbers suggest AI has moved from experiment to infrastructure in record time. Among those using AI daily, 39% deploy it once per day, while 33% use it "several times per day or more," according to the survey conducted between August 15-19, 2025. Adoption is accelerating: 80% of researchers say they're using AI more than they were six months ago, and 71% expect to increase usage over the next six months. Only 8% anticipate their usage will decline.

“While AI provides excellent assistance and opportunities, human judgment will remain vital,” Erica Parker, Managing Director Research Products at The Harris Poll, told VentureBeat. “The future is a teamwork dynamic where AI will accelerate tasks and quickly unearth findings, while researchers will ensure quality and provide high level consultative insights.”

The top use cases reflect AI's strength in handling data at scale: 58% of researchers use it for analyzing multiple data sources, 54% for analyzing structured data, 50% for automating insight reports, 49% for analyzing open-ended survey responses, and 48% for summarizing findings. These tasks—traditionally labor-intensive and time-consuming — now happen in minutes rather than hours.

Beyond time savings, researchers report tangible quality improvements. Some 44% say AI improves accuracy, 43% report it helps surface insights they might otherwise have missed, 43% cite increased speed of insights delivery, and 39% say it sparks creativity. The overwhelming majority — 89% — say AI has made their work lives better, with 25% describing the improvement as "significant."

The productivity paradox: saving time while creating new validation work

Yet the same survey reveals deep unease about the technology's reliability. The list of concerns is extensive: 39% of researchers report increased reliance on error-prone technology, 37% cite new risks around data quality or accuracy, 31% describe additional validation work, 29% report uncertainty about job security, and 28% say AI has raised concerns about data privacy and ethics.

The report notes that "accuracy is the biggest frustration with AI experienced by researchers when asked on an open-ended basis." One researcher captured the tension succinctly: "The faster we move with AI, the more we need to check if we're moving in the right direction."

This paradox — saving time while simultaneously creating new work — reflects a fundamental characteristic of current AI systems, which can produce outputs that appear authoritative but contain what researchers call "hallucinations," or fabricated information presented as fact. The challenge is particularly acute in a profession where credibility depends on methodological rigor and where incorrect data can lead clients to make costly business decisions.

"Researchers view AI as a junior analyst, capable of speed and breadth, but needing oversight and judgment," said Gary Topiol, Managing Director at QuestDIY, in the report.

That metaphor — AI as junior analyst — captures the industry's current operating model. Researchers treat AI outputs as drafts requiring senior review rather than finished products, a workflow that provides guardrails but also underscores the technology's limitations.

Why data privacy fears are the biggest obstacle to AI adoption in research

When asked what would limit AI use at work, researchers identified data privacy and security concerns as the greatest barrier, cited by 33% of respondents. This concern isn't abstract: researchers handle sensitive customer data, proprietary business information, and personally identifiable information subject to regulations like GDPR and CCPA. Sharing that data with AI systems — particularly cloud-based large language models — raises legitimate questions about who controls the information and whether it might be used to train models accessible to competitors.

Other significant barriers include time to experiment and learn new tools (32%), training (32%), integration challenges (28%), internal policy restrictions (25%), and cost (24%). An additional 31% cited lack of transparency in AI use as a concern, which could complicate explaining results to clients and stakeholders.

The transparency issue is particularly thorny. When an AI system produces an analysis or insight, researchers often cannot trace how the system arrived at its conclusion — a problem that conflicts with the scientific method's emphasis on replicability and clear methodology. Some clients have responded by including no-AI clauses in their contracts, forcing researchers to either avoid the technology entirely or use it in ways that don't technically violate contractual terms but may blur ethical lines.

"Onboarding beats feature bloat," Parker said in the report. "The biggest brakes are time to learn and train. Packaged workflows, templates, and guided setup all unlock usage faster than piling on capabilities."

Inside the new workflow: treating AI like a junior analyst who needs constant supervision

Despite these challenges, researchers aren't abandoning AI — they're developing frameworks to use it responsibly. The consensus model, according to the survey, is "human-led research supported by AI," where AI handles repetitive tasks like coding, data cleaning, and report generation while humans focus on interpretation, strategy, and business impact.

About one-third of researchers (29%) describe their current workflow as "human-led with significant AI support," while 31% characterize it as "mostly human with some AI help." Looking ahead to 2030, 61% envision AI as a "decision-support partner" with expanded capabilities including generative features for drafting surveys and reports (56%), AI-driven synthetic data generation (53%), automation of core processes like project setup and coding (48%), predictive analytics (44%), and deeper cognitive insights (43%).

The report describes an emerging division of labor where researchers become "Insight Advocates" — professionals who validate AI outputs, connect findings to stakeholder challenges, and translate machine-generated analysis into strategic narratives that drive business decisions. In this model, technical execution becomes less central to the researcher's value proposition than judgment, context, and storytelling.

"AI can surface missed insights — but it still needs a human to judge what really matters," Topiol said in the report.

What other knowledge workers can learn from the research industry's AI experiment

The market research industry's AI adoption may presage similar patterns in other knowledge work professions where the technology promises to accelerate analysis and synthesis. The experience of researchers — early AI adopters who have integrated the technology into daily workflows — offers lessons about both opportunities and pitfalls.

First, speed genuinely matters. One boutique agency research lead quoted in the report described watching survey results accumulate in real-time after fielding: "After submitting it for fielding, I literally watched the survey count climb and finish the same afternoon. It was a remarkable turnaround." That velocity enables researchers to respond to business questions within hours rather than weeks, making insights actionable while decisions are still being made rather than after the fact.

Second, the productivity gains are real but uneven. Saving five hours per week represents meaningful efficiency for individual contributors, but those savings can disappear if spent validating AI outputs or correcting errors. The net benefit depends on the specific task, the quality of the AI tool, and the user's skill in prompting and reviewing the technology's work.

Third, the skills required for research are changing. The report identifies future competencies including cultural fluency, strategic storytelling, ethical stewardship, and what it calls "inquisitive insight advocacy" — the ability to ask the right questions, validate AI outputs, and frame insights for maximum business impact. Technical execution, while still important, becomes less differentiating as AI handles more of the mechanical work.

The strange phenomenon of using technology intensively while questioning its reliability

The survey's most striking finding may be the persistence of trust issues despite widespread adoption. In most technology adoption curves, trust builds as users gain experience and tools mature. But with AI, researchers appear to be using tools intensively while simultaneously questioning their reliability — a dynamic driven by the technology's pattern of performing well most of the time but failing unpredictably.

This creates a verification burden that has no obvious endpoint. Unlike traditional software bugs that can be identified and fixed, AI systems' probabilistic nature means they may produce different outputs for the same inputs, making it difficult to develop reliable quality assurance processes.

The data privacy concerns — cited by 33% as the biggest barrier to adoption — reflect a different dimension of trust. Researchers worry not just about whether AI produces accurate outputs but also about what happens to the sensitive data they feed into these systems. QuestDIY's approach, according to the report, is to build AI directly into a research platform with ISO/IEC 27001 certification rather than requiring researchers to use general-purpose tools like ChatGPT that may store and learn from user inputs.

"The center of gravity is analysis at scale — fusing multiple sources, handling both structured and unstructured data, and automating reporting," Topiol said in the report, describing where AI delivers the most value.

The future of research work: elevation or endless verification?

The report positions 2026 as an inflection point when AI moves from being a tool researchers use to something more like a team member — what the authors call a "co-analyst" that participates in the research process rather than merely accelerating specific tasks.

This vision assumes continued improvement in AI capabilities, particularly in areas where researchers currently see the technology as underdeveloped. While 41% currently use AI for survey design, 37% for programming, and 30% for proposal creation, most researchers consider these appropriate use cases, suggesting significant room for growth once the tools become more reliable or the workflows more structured.

The human-led model appears likely to persist. "The future is human-led, with AI as a trusted co-analyst," Parker said in the report. But what "human-led" means in practice may shift. If AI handles most analytical tasks and researchers focus on validation and strategic interpretation, the profession may come to resemble editorial work more than scientific analysis — curating and contextualizing machine-generated insights rather than producing them from scratch.

"AI gives researchers the space to move up the value chain – from data gatherers to Insight Advocates, focused on maximising business impact," Topiol said in the report.

Whether this transformation marks an elevation of the profession or a deskilling depends partly on how the technology evolves. If AI systems become more transparent and reliable, the verification burden may decrease and researchers can focus on higher-order thinking. If they remain opaque and error-prone, researchers may find themselves trapped in an endless cycle of checking work produced by tools they cannot fully trust or explain.

The survey data suggests researchers are navigating this uncertainty by developing a form of professional muscle memory — learning which tasks AI handles well, where it tends to fail, and how much oversight each type of output requires. This tacit knowledge, accumulated through daily use and occasional failures, may become as important to the profession as statistical literacy or survey design principles.

Yet the fundamental tension remains unresolved. Researchers are moving faster than ever, delivering insights in hours instead of weeks, and handling analytical tasks that would have been impossible without AI. But they're doing so while shouldering a new responsibility that previous generations never faced: serving as the quality control layer between powerful but unpredictable machines and business leaders making million-dollar decisions.

The industry has made its bet. Now comes the harder part: proving that human judgment can keep pace with machine speed — and that the insights produced by this uneasy partnership are worth the trust clients place in them.

Industry executives warn that current demand may not align with future capabilities, creating significant risk for consolidation and distress in the sector.

We look at some of the latest data center developments announced over the past month.

I’m thrilled to announce a fantastic new addition to our leadership team: Karyne Levy is joining VentureBeat as our new Managing Editor. Today is her first day.

Many of you may know Karyne from her most recent role as Deputy Managing Editor at TechCrunch, but her career is a highlight reel of veteran tech journalism. Her resume includes pivotal roles at Protocol, NerdWallet, Business Insider, and CNET, giving her a deep understanding of this industry from every angle.

Hiring Karyne is a significant step forward for VentureBeat. As we’ve sharpened our focus on serving you – the enterprise technical decision-maker navigating the complexities of AI and data – I’ve been looking for a very specific kind of leader.

The "Organizer's Dopamine Hit"

In the past, a managing editor was often the final backstop for copy. Today, at a modern, data-focused media company like ours, the role is infinitely more dynamic. It’s the central hub of the entire content operation.

During my search, I found myself talking a lot about the two types of "dopamine hits" in our business. There’s the writer’s hit – seeing your name on a great story. And then there’s the organizer’s hit – the satisfaction that comes from building, tuning, and running the complex machine that allows a dozen different parts of the company to move in a single, powerful direction.

We were looking for the organizer.

When I spoke with Karyne, I explained this vision: a leader who thrives on creating workflows, who loves being the liaison between editorial, our data and survey team, our events, and our marketing operations.

Her response confirmed she was the one: "Everything you said is exactly my dopamine hit."

Karyne’s passion is making the entire operation hum. She has a proven track record of managing people, running newsrooms, and interfacing with all parts of a business to ensure everyone is aligned. That operational rigor is precisely what we need for our next chapter.

Why This Matters for Our Strategy (and for You)

As I’ve written about before, VentureBeat is on a mission to evolve. In an age where experts and companies can publish directly, it’s not enough to be a secondary source. Our goal is to become a primary source for you.

How? By leveraging our relationship with our community of millions of technical leaders. We are increasingly surveying you directly to generate proprietary insights you can’t get anywhere else. We want to be the first to tell you which vector stores your peers are actually implementing, what governance challenges are most pressing for data scientists, or how your counterparts are budgeting for generative AI.

This is an ambitious strategy. It requires a tight-knit team where our editorial content, our research surveys and reports, our newsletters, and our VB Transform events are all working from the same playbook.

Karyne is the leader who will help us execute that vision. Her experience at Protocol, which was also dedicated to serving technical and business decision-makers, means she fundamentally understands our audience. She is ideally suited to manage our newsroom and ensure that every piece of content we produce helps you do your job better. She’ll be working alongside Carl Franzen, our executive editor, who continues to drive news decision-making.

This is a fantastic hire for VentureBeat. It’s another sign of our commitment to building the most focused, expert team in enterprise AI and data.

Please join me in welcoming Karyne to the team.

The buzzed-about but still stealthy New York City startup Augmented Intelligence Inc (AUI), which seeks to go beyond the popular "transformer" architecture used by most of today's LLMs such as ChatGPT and Gemini, has raised $20 million in a bridge SAFE round at a $750 million valuation cap, bringing its total funding to nearly $60 million, VentureBeat can exclusively reveal.

The round, completed in under a week, comes amid heightened interest in deterministic conversational AI and precedes a larger raise now in advanced stages.

AUI relies on a fusion of the transformer tech and a newer technology called "neuro-symbolic AI," described in greater detail below.

"We realize that you can combine the brilliance of LLMs in linguistic capabilities with the guarantees of symbolic AI," said Ohad Elhelo, AUI co-founder and CEO in a recent interview with VentureBeat. Elhelo launched the company in 2017 alongside co-founder and Chief Product Officer Ori Cohen.

The new financing includes participation from eGateway Ventures, New Era Capital Partners, existing shareholders, and other strategic investors. It follows a $10 million raise in September 2024 at a $350 million valuation cap, coinciding with the company’s announced go-to-market partnership with Google in October 2024. Early investors include Vertex Pharmaceuticals founder Joshua Boger, UKG Chairman Aron Ain, and former IBM President Jim Whitehurst.

According to the company, the bridge round is a precursor to a significantly larger raise already in advanced stages.

AUI is the company behind Apollo-1, a new foundation model built for task-oriented dialog, which it describes as the "economic half" of conversational AI — distinct from the open-ended dialog handled by LLMs like ChatGPT and Gemini.

The firm argues that existing LLMs lack the determinism, policy enforcement, and operational certainty required by enterprises, especially in regulated sectors.

Chris Varelas, co-founder of Redwood Capital and an advisor to AUI, said in a press release provided to VentureBeat: “I’ve seen some of today’s top AI leaders walk away with their heads spinning after interacting with Apollo-1.”

A Distinctive Neuro-Symbolic Architecture

Apollo-1’s core innovation is its neuro-symbolic architecture, which separates linguistic fluency from task reasoning. Instead of using the most common technology underpinning most LLMs and conversational AI systems today — the vaunted transformer architecture described in the seminal 2017 Google paper "Attention Is All You Need" — AUI's system integrates two layers:

• Neural modules, powered by LLMs, handle perception: encoding user inputs and generating natural language responses.

• A symbolic reasoning engine, developed over several years, interprets structured task elements such as intents, entities, and parameters. This symbolic state engine determines the appropriate next actions using deterministic logic.

This hybrid architecture allows Apollo-1 to maintain state continuity, enforce organizational policies, and reliably trigger tool or API calls — capabilities that transformer-only agents lack.

Elhelo said this design emerged from a multi-year data collection effort: “We built a consumer service and recorded millions of human-agent interactions across 60,000 live agents. From that, we abstracted a symbolic language that defines the structure of task-based dialogs, separate from their domain-specific content.”

However, enterprises that have already built systems built around transformer LLMs needn't worry. AUI wants to make adopting its new technology just as easy.

"Apollo-1 deploys like any modern foundation model," Elhelo told VentureBeat in a text last night. "It doesn’t require dedicated or proprietary clusters to run. It operates across standard cloud and hybrid environments, leveraging both GPUs and CPUs, and is significantly more cost-efficient to deploy than frontier reasoning models. Apollo-1 can also be deployed across all major clouds in a separated environment for increased security."

Generalization and Domain Flexibility

Apollo-1 is described as a foundation model for task-oriented dialog, meaning it is domain-agnostic and generalizable across verticals like healthcare, travel, insurance, and retail.

Unlike consulting-heavy AI platforms that require building bespoke logic per client, Apollo-1 allows enterprises to define behaviors and tools within a shared symbolic language. This approach supports faster onboarding and reduces long-term maintenance. According to the team, an enterprise can launch a working agent in under a day.

Crucially, procedural rules are encoded at the symbolic layer — not learned from examples. This enables deterministic execution for sensitive or regulated tasks.

For instance, a system can block cancellation of a Basic Economy flight not by guessing intent but by applying hard-coded logic to a symbolic representation of the booking class.

As Elhelo explained to VentureBeat, LLMs are "not a good mechanism when you’re looking for certainty. It’s better if you know what you’re going to send [to an AI model] and always send it, and you know, always, what’s going to come back [to the user] and how to handle that.”

Availability and Developer Access

Apollo-1 is already in active use within Fortune 500 enterprises in a closed beta, and a broader general availability release is expected before the end of 2025, according to a previous report by The Information, which broke the initial news on the startup.

Enterprises can integrate with Apollo-1 either via:

• A developer playground, where business users and technical teams jointly configure policies, rules, and behaviors; or

• A standard API, using OpenAI-compatible formats.

The model supports policy enforcement, rule-based customization, and steering via guardrails. Symbolic rules allow businesses to dictate fixed behaviors, while LLM modules handle open-text interpretation and user interaction.

Enterprise Fit: When Reliability Beats Fluency

While LLMs have advanced general-purpose dialog and creativity, they remain probabilistic — a barrier to enterprise deployment in finance, healthcare, and customer service.

Apollo-1 targets this gap by offering a system where policy adherence and deterministic task completion are first-class design goals.

Elhelo puts it plainly: “If your use case is task-oriented dialog, you have to use us, even if you are ChatGPT.”