If you have ever lost a great ChatGPT answer somewhere in your endless chat history, that headache is finally over. OpenAI has rolled out a major search upgrade that lets you find old chats, projects, documents, and images all from one place.

Before this update, the sidebar search only pulled up past conversations, leaving uploaded files, projects, and generated images completely out of reach. The new search option is now available across web, iOS, and Android, on every ChatGPT plan, including free accounts.

How does the new ChatGPT search work?

You can start searching right from the ChatGPT sidebar, just like before, except now it covers everything in your account instead of only chats. If you want to narrow things down, there are filters that let you search by specific content type, such as pulling up only images or files tied to one particular project. Once you find what you need, selecting a result opens that chat, project, or file directly inside ChatGPT to skip endless scrolling.

This rollout also lines up closely with OpenAI’s recent GPT-5.6 launch, which brought its own wave of upgrades, including a new AI agent called ChatGPT Work that takes on entire projects instead of single questions. It connects to your apps and files, works through multi-step tasks, and can keep running in the background even after you close ChatGPT.

Why does it matter?

The new ChatGPT search does not change what it stores; it simply makes everything already saved much easier to surface. That’s useful if you regularly use ChatGPT for ongoing projects, research, or document analysis. By bringing everything together in one searchable place, you can look for what you need, filter the results, and jump back into your work within seconds.

Vinyl’s resurrection stopped being a charming nostalgia story years ago. U.S. vinyl revenue surpassed $1 billion in 2025, with 46.8 million records sold and the format recording its nineteenth consecutive year of growth. Luminate previously reported that annual U.S. vinyl album sales had increased from 13.1 million in 2016 to 49.6 million in 2023, an increase of nearly 300 percent. 

That growth has transformed the turntable category along with everything connected to it. Consumers can now choose from inexpensive wireless record players, sophisticated direct-drive designs, restored vintage decks and turntables with genuinely balanced outputs. Phono preamplifiers have consequently become a serious battleground again rather than an inexpensive circuit manufacturers conceal beside the headphone jack.

EAT, or European Audio Team, has operated at the more ambitious end of that market for years. Its turntables, tonearms, cartridges, vacuum tubes and phono stages are not designed for shoppers searching Amazon for something to play the copy of Aja they bought at Target — an album whose reputation remains vastly more impressive than the music itself.

The new E-Glo CB and E-Glo SB continue that approach with full-width chassis, dual-mono construction, discrete circuitry, extensive cartridge adjustment and tube-based amplification. VANA Ltd., EAT’s North American distributor, has announced immediate availability at $4,599 for the E-Glo CB and $6,250 for the E-Glo SB.

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The EAT and Pro-Ject Connection

EAT is owned and led by Jozefina Lichtenegger, who is married to Pro-Ject Audio Systems founder Heinz Lichtenegger. That relationship has inevitably resulted in some shared analog DNA, manufacturing resources and expertise, but the two companies are not merely different badges attached to the same products.

Pro-Ject has built its reputation by making serious analog playback accessible across a wide range of prices. EAT operates several floors higher, with heavier construction, more elaborate materials, tube-based electronics and products aimed at listeners assembling genuinely high-end vinyl systems.

That relationship also helps explain EAT’s interest in balanced phono playback. Pro-Ject has spent several years expanding its True Balanced ecosystem with compatible turntables, phono stages and cables, including the X1 B, X2 B and X8. 

Two Models Built Around a Common Platform

The E-Glo CB and SB occupy the space between EAT’s compact Petit B and flagship E-Glo FB. Both use three gain stages, discrete components rather than integrated-circuit op-amps, split passive and active RIAA equalization and dedicated DC servo circuitry to minimize offset at the outputs.

The first gain stage uses three discrete semiconductor amplifier circuits. Separate circuits handle the positive and negative phases, while a third sums the two signals to reject common-mode noise. The high-frequency portion of the RIAA equalization is also applied at this stage.

Both models accept moving-coil cartridges through their balanced XLR inputs, while the RCA inputs support both moving-magnet and moving-coil designs. Balanced XLR and single-ended RCA outputs can be used simultaneously. 

EAT reserves the balanced input for MC cartridges, whose very low output makes common-mode noise rejection especially valuable. Although a phono cartridge is inherently a balanced signal source, the internal wiring of many MM designs prevents access to the complete differential signal. Purpose-built balanced MM cartridges exist, but the E-Glo CB and SB are not designed to accept them through their XLR inputs.

The Difference Is in the Second Stage

The two models diverge most significantly in their second amplification stages.

The E-Glo CB uses a hybrid, single-ended tube and transistor circuit composed of two ECC83S triodes and one transistor. This stage handles the low-frequency portion of the RIAA equalization and incorporates a switchable subsonic filter.

The more expensive E-Glo SB moves closer to the architecture of the flagship E-Glo FB. Its second stage is a symmetrical tube design using two ECC83S and two E88CC triodes. EAT also specifies higher-grade semi-crystalline hydrocarbon polypropylene capacitors in the gain and RIAA networks. 

The difference is not simply one additional pair of glowing bottles behind the ventilation slots. The SB maintains balanced operation through its second gain stage, while the CB converts to a single-ended signal before its output stage creates the balanced output.

That does not make the CB badly designed, but it does mean that “balanced” requires some qualification. The CB provides a symmetrical MC input stage, common-mode noise rejection and balanced XLR output, but it is not a fully differential circuit from cartridge to output. An XLR socket is a connector, not a blessing from your rabbi.

The SB comes considerably closer to the flagship concept through its symmetrical tube stage and upgraded capacitor selection.

Cartridge Adjustment

Both models provide enough adjustment to accommodate a broad range of moving-magnet and moving-coil cartridges.

Moving-magnet resistance can be selected from 30,000 to 75,000 ohms, while capacitance settings range from 50 to 620 pF. Moving-coil loading options extend from 10 ohms to 1.2 kilohms.

Six gain settings are offered: 40, 45, 50, 55, 65 and 70 dB. According to EAT’s specifications, using the balanced XLR outputs adds another 6 dB.

Both models also include a 20 Hz subsonic filter with an 18 dB-per-octave slope. That will be useful for systems vulnerable to record warps, turntable suspension movement or subwoofers attempting to reproduce low-frequency information that was never part of the recording. 

RIAA accuracy is specified within 0.5 dB from 20 Hz to 20 kHz. The CB claims an MM signal-to-noise ratio greater than 92 dBV and an MC figure greater than 80 dBV. The SB is rated at greater than 90 dBV for MM and greater than 80 dBV for MC.

Each measures 395 x 86 x 262 mm (15.6 x 3.4 x 10.3 inches). The E-Glo CB weighs 5 kg (11 pounds), while the E-Glo SB weighs 5.1 kg (11.2 pounds). Both use an external 18-volt power supply, keeping the transformer away from circuitry responsible for amplifying signals measured in fractions of a millivolt.

The Bottom Line

Neither model represents an attempt to make balanced tube phono amplification affordable in any conventional sense. The E-Glo CB costs $4,599, and the E-Glo SB raises the admission price to $6,250 before one buys the turntable, tonearm, cartridge, cables or records.

Welcome back to high-end analog, where the software occasionally costs $150 and the component amplifying it can cost more than the car used to bring it home.

The CB nevertheless fills a credible position for listeners using a serious moving-coil cartridge who want balanced input capability, extensive loading control and some tube character without climbing into five-figure phono-stage territory. Its hybrid architecture also gives EAT an alternative to competitors such as the Nagra Compact Phono, MoFi UltraPhono Pro and upper-tier phono stages from Rega, Musical Fidelity and Pro-Ject.

The SB is the more technically ambitious product. Its symmetrical tube-based second stage, upgraded capacitors and closer relationship to the E-Glo FB make it the logical choice for systems already built around a high-end balanced preamplifier and a low-output MC cartridge.

EAT has not simply removed parts from its flagship, installed cheaper capacitors and declared victory. The CB and SB are distinct implementations for different buyers, and their construction, flexibility and circuit design suggest products created for long-term analog systems rather than another brief ride aboard the vinyl revival train.

Vinyl, after nineteen consecutive years of growth, is no longer waiting at the station.

For more information: europeanaudioteam.com

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• AI software claims hidden grid capacity could ease America’s growing electricity shortage
• GridCARE says simulations reveal unused transmission capacity across existing power infrastructure
• Existing transmission lines may hold far more capacity than previously estimated

A new software platform claims it can unlock roughly 300 gigawatts of hidden electrical transmission capacity across the existing United States power grid within three to five years.

The technology, developed by GridCARE and led by founder and CEO Amit Narayan, relies on advanced grid modelling rather than costly new infrastructure.

Increased attendance, better attention in classrooms, stronger friendships, and more engaged citizens – these are not a long wishlist of preferred traits in an elementary school student. They are what some advocates believe are a direct impact from recess.

Recess, long a staple in children’s school days, has been put on the back burner or cut entirely by some districts as the push for more class time, higher academic performance, and increased test scores take center stage.

Recess advocates are pushing back in their efforts to guarantee a playtime each day. They argue adding in more structured play time benefits children’s academic, social and emotional well-being.

“It’s not that we don’t need hard work and concentrated effort, but when you hit a wall, you take a break,” says Catherine Ramstetter, who co-authored a new report for the American Academy of Pediatrics touting the importance of structured play. “That’s where I think, systematically, we’re kind of broken; that we expect little kids to be like little robots.”

The Push for Play

The AAP recently affirmed its 2013 stance that not only is recess important for children’s cognitive, physical and emotional well-being but expanded the recommendations to include middle and high school students too.

“I don’t know many high school teachers that are studying or deep into play,” Ramstetter says, pointing out early childhood teachers typically receive training in structured play. “Also, culturally in older grades, rigor is somehow equated with your nose to the grindstone –- when in reality, when we want to attain rigor, we have to have breaks.”

Similar to a push against screentime – specifically cell phones – in the classroom, grassroots efforts have formed to bring back recess. More than a dozen states, largely led by the nonprofit Yes to Recess Movement, are pushing for 60 minutes of play per day and ensuring it is not used as a bargaining chip for good or bad behavior.

“There has been a lot of evolution of the understanding of the value of recess over 30 years,” says Elizabeth Cushing, CEO of PlayWorks, a nonprofit that helps schools implement evidence-based play tactics.

“What might have been perceived as a ‘break’ is now seen as a critical part of the school day,” she adds. “It’s enabling kids to be in connection with each other in a way that’s fun, with low stakes, to build a community.”

Pushing for state or federal bills have yielded mixed reactions. Each advocate interviewed points out that they have never come with an allocation of funding to help facilitate the implementation, and also had concerns with a lack of other resources, namely helping teachers find time to accommodate the recess breaks. Deborah Rhea, founder of the Let’s inspire innovation ‘N Kids (LiinK) Project, suggests each local district tackles it by deciding what is best for its own schools and students.

“I think we have made more strides than I ever thought possible,” says Rhea, who also serves as a professor of kinesiology at Texas Christian University. “But at the same time, we’re limping along. We’re not being successful with momentum. Doing this propels them forward academically.”

But Ramstetter says introducing those minutes alone is not enough.

“I think policy can help support practice, but to make it quality playtime — something that doesn’t feel like an onerous task on a school — you have to spend some time planning,” she says. “Similar to introducing a new curriculum on English. It’s treating it like the crucial instructional time that it is.”

The Benefits of Play

In addition to benefiting younger students, the boost in social skills like teamwork and inclusion, along with physical benefits can be particularly important as students get older, Cushing says.

“The opportunities and skill building that happens in elementary school around cooperation, teamwork and how to include everyone in a game are easily done at that age,” she says. “They follow into middle and high schools where technology and social pressures require they have those skills already. If we want to develop citizens who work in a team and make friends, we have to start early.”

Experts added that recess can also boost attendance, a particularly important factor given high rates of chronic absenteeism sweeping the nation. Massachusetts-based Bedford High School offered “movement breaks” during lunch and saw chronic absenteeism decrease from 35% to 23% within its first year alone.

“There’s a lot of focus on recess to help with belonging and source of positive, joyful feelings about school,” Cushing says, adding schools with the PlayWorks framework saw lower chronic absenteeism rates than those without it.

Rhea of LiiNK listed multiple benefits she’s seen across the roughly 25,000 students that underwent her programming: cortisol levels (tested by hair samples) went down; academic assessment scores went up; off-task behavior in the classroom dropped 40 percent, and schools found offering the programming could be used as a recruitment tactic.

“The only time I had to convince parents was the first year I started this,” she says. “After that, word of mouth spread.”

There still is the uphill battle of convincing schools to find time in their day. Not every district can afford to roll out a system similar to Rhea’s or Cushing’s, either financially or with spare time.

The Future of Play

However, Cushing pointed out even with little resources, children tend to thrive with simple, structured play.

“Recess is the only time in the school day where children naturally know they have mastery,” she says. “The beauty of recess is that kids will play everywhere. Despite all the complexity there’s a real beauty in the universality of it.”

However, students do need some resources, like a jump rope and designated play areas, otherwise they may not receive the full benefits of recess even if they are outside.

“If you look at a playground where there’s no frame for it, you’ll see a majority of kids standing around the outside of the playground,” Cushing says. “They’re too afraid or shy to jump in and don’t know if it’s going to be fun or not. It’s not that they don’t want to play, they just need the conditions created to do it.”

While cell phones are less common in elementary school settings, experts added a lack of screens could improve play conditions.

Schools have pushed for more tech-free time, specifically with “bell to bell” bans that require cell phones remain untouched for the entirety of the school day, including during lunch, recess and passing periods.

The AAP study did not explicitly mention the use of technology. However, Ramstetter says the implication was “yeah, get it out of the way,” she adds.

“Don’t give them to kids at recess: Encourage them to connect, give them quiet places to sit. to run around, to dig in the dirt,” she says, comparing the ban to other forms of consent. “If I tell you I don’t want to play anymore, I need to mean it. Otherwise it gets muddy.”

She adds sometimes simple is best, pointing toward schools that just have a jump rope, chalk, and Four Square – things that allow children to make their own rules. “Everyone agrees recess is beneficial, but you have to do it well to reap the benefits,” Ramstetter says. “If we all believe it’s beneficial, let’s take a step back to see how can we better tap into some of this time, preparing to do it well.”

Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model-provider APIs, yet the next dollar is aimed at specialized compute almost none of them use today; a majority intend to switch or add providers within the year, many within a quarter. Buying decisions turn on integration and total cost of ownership rather than headline token price — which is fortunate, because most enterprises cannot yet see their unit economics clearly: GPUs sit at half utilization or less, and fewer than half rigorously track what their compute actually costs. The result is a compute gap — heavy, fast-moving investment running ahead of the visibility needed to control it.

This wave of VentureBeat Pulse Research examines enterprise AI infrastructure and compute: where organizations are in their deployment journey, what they run AI on today, how satisfied they are, what would make them switch, where they plan to evaluate their investments, and — most revealingly — how well they can measure and control the economics of the compute underneath it all.

The central finding is a compute gap — the distance between how aggressively enterprises are investing in AI infrastructure and how little of its economics they can see. Only about one in five (21%) run AI in production at scale, yet spending intentions are outrunning that maturity: the single largest planned area enterprises plan to evaluate over the next year is AI-specialized clouds (45%), a layer almost none of these enterprises use today. Meanwhile the compute already in place runs cold — 83% report GPU utilization of 50% or less — and fewer than half (44%) can rigorously track what their AI compute costs. Enterprises are buying more infrastructure faster than they can account for what they already own.

Enterprises are not settled on their infrastructure vendors, either: A clear majority (64%) plan to switch or add an infrastructure provider within twelve months, and 38% within the next quarter — unusually high churn intent for a category this foundational. When they choose, they choose on integration with the existing stack (41%) and total cost of ownership (35%), not on headline price: cost per million tokens is the deciding factor for just 8%. And the frontier constraint that will shape the next round of decisions — the shift from GPU compute to memory bandwidth as inference scales — is barely on the radar, with roughly one in five enterprises either unaware of it or yet to address it.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey focused on enterprise AI infrastructure, compute, and inference economics. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

By organization size the sample concentrates in the mid-market: 101–250 employees (36%) and 251–1,000 (27%) lead, with 1,001–5,000 (22%), 5,001–10,000 (8%), and 10,001+ (7%) above them. By role it spans managers (38%), individual contributors (28%), VPs and directors (19%), and the C-suite (13%); on purchasing authority it is buyer-credible, with 45% final decision-makers and another 30% recommenders or influencers for AI solutions. Technology/Software is the largest industry at 26%, followed by Healthcare/Life Sciences (15%), Financial Services (13%), and Retail/E-commerce (12%).

At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It also skews toward the mid-market and toward earlier-stage adopters, so it is best read as the view from organizations actively building out AI infrastructure rather than from the largest hyperscale operators.

Finding 1: Ambition outpaces production

Only one in five run AI in production at scale

We asked where organizations sit in their AI deployment journey. Most are still building toward production rather than operating at scale.

The maturity curve is front-loaded. Three-quarters of enterprises (76%) are either experimenting or running only some workloads in production, and just 21% describe AI in production at scale. This matters for everything that follows: the infrastructure decisions in this report are being made largely by organizations still early in deployment, whose compute footprint — and whose costs — are about to grow. The evaluation and switching intentions in Findings 3 and 4 are the leading edge of that build-out, not the settled preferences of operators who have already found what works.

Finding 2: Enterprises run on hyperscalers and model APIs

The specialized GPU clouds barely register — today

We asked which providers and platforms enterprises currently use to run their AI. The answer is a familiar one: the incumbents.

The current stack is hyperscaler-and-API. Google Cloud leads at 48%, and the general-purpose clouds (Google, Microsoft, AWS, Oracle) together with the major model APIs (Gemini, OpenAI, Anthropic) account for essentially all current deployment. The specialized “neocloud” GPU providers that dominate AI-infrastructure headlines — CoreWeave, Lambda, Crusoe, Nebius and peers — register at or near zero among these enterprises today. Only 6% run their own on-prem GPU clusters and 4% a custom open-source stack. Enterprises are, for now, running AI on the providers they already buy from — which makes the evaluation intentions in Finding 3 all the more striking.

(A note on reading these shares. As described in the methodology section, this sample is self-selected and skews mid-market, and this question counted every provider a respondent uses — an average of 2.1 selections each — so the figures measure presence in the stack rather than spending or primary status. A sample built this way will show a different provider mix than a spend-weighted census of the broader market; Google’s strength here, for example, is consistent with its long-standing position among smaller enterprises building on AI. Read these shares as a portrait of what this AI-active cohort runs today, and treat gaps between these figures and industry-wide market share estimates as a property of the sample rather than a contradiction of either.)

Finding 3: The next dollar goes to infrastructure they don’t yet run

AI-specialized clouds top the evaluations list

We asked where enterprises planned to evaluate AI infrastructure over the next 12 months. Their answers point away from the stack they run today.

Here is the report’s sharpest tension. The single most-cited planned evaluation area — AI-specialized clouds, at 45% — is the very category almost none of these enterprises use today (Finding 2). Nearly a third (32%) intend to evaluate non-Nvidia accelerators, and 28% in next-generation Nvidia silicon; even decentralized compute networks (16%) and sovereign compute (11%) draw meaningful interest. Read against current usage, this is not incremental — it is the leading edge of a re-platforming. The direction-of-travel question tells the same story: every infrastructure approach is net-expanding, but specialized AI clouds carry the highest net momentum (+24), edging out even the hyperscalers (+22). Enterprises are preparing to move a meaningful share of AI compute off the general-purpose cloud.

This continues a trend we saw in our April-May survey wave. Back then, usage of the AI-specialized clouds was equally marginal — CoreWeave at 3%, Lambda at 4%, Crusoe at 2% of enterprises. When we asked enterprises what change they planned in their AI infrastructure strategy over the next twelve months, the most-cited answer was moving workloads to specialized AI clouds, at 33%. Asked in April-May which emerging compute option they were most likely to evaluate AI-specialized clouds again drew the most responses. Two waves, two differently worded questions, one consistent picture: the type of cloud enterprises are most eager to assess is the type they have barely begun to use.

Finding 4: A switching wave is building

Six in 10 plan to change providers within a year — many within a quarter

We asked whether and when enterprises plan to switch or add an infrastructure provider. Very few intend to stand still.

For a category as foundational as compute, this is a remarkable amount of intended movement. Only 36% have no plans to change, meaning a clear majority (64%) intend to switch or add a provider within twelve months — and 38% within the next quarter alone. Where that interest points is telling: the providers drawing the most switching consideration are again the incumbents — Microsoft Azure and Google Cloud (33% each), OpenAI (30%), and Gemini (22%) — which suggests much of the near-term movement is reshuffling among the majors and consolidating spend rather than defecting to new entrants. The neocloud interest in Finding 3 is a 12-month evaluation thesis; the switching in the next quarter is mostly incumbents trading share.

(Method note: Respondents who selected both “no plans to change” and a specific switching window are counted as switchers, on the logic that naming a timeframe is the more specific answer; three respondents were reclassified under this rule.)

Finding 5: Nobody buys on token price

Integration and total cost of ownership decide — not sticker price

We asked what matters most when enterprises select an AI infrastructure provider. Headline price finished last.

Enterprises do not buy AI infrastructure on pricing, which is the place vendors compete on hardest. Integration with the existing stack (41%) and total cost of ownership (35%) dominate, while the headline metric — cost per million tokens — is the deciding factor for just 8%, dead last. The pattern is coherent: buyers are optimizing for how a provider fits and what it truly costs to operate, not for the advertised unit rate. It also foreshadows Finding 7 — enterprises say TCO matters most, yet most cannot yet measure it rigorously. The stated priority and the measured capability are out of step.

Finding 6: Expensive GPUs, idle most of the time

83% report GPU utilization of 50% or less

We asked what share of their GPU capacity enterprises actually utilize. The answer is a well-known but rarely quantified inefficiency.

Disclosure: Band percentages count every selection against all 107 qualified respondents; 14 respondents selected more than one band, so bands overlap. At the respondent level, 83 of the 100 GPU-operating enterprises reported utilization at or below 50%

The compute already in place runs cold. Adding the bands at or below half capacity, 83% of enterprises that operate GPUs report utilization of 50% or less, and nearly half (49%) run at 25% or below. Only 12% clear the 50% mark, and a further 8% do not measure utilization at all. Idle accelerators are expensive accelerators, and this is the clearest single measure of the compute gap: enterprises are planning to buy more GPUs and specialized compute (Finding 3) while the capacity they already own sits substantially unused. The efficiency headroom in the current fleet is large — and largely unmeasured.

Finding 7: Spending fast, measuring slowly

Fewer than half rigorously track what their compute costs

We asked whether enterprises can quantify the cost and return of their AI infrastructure spend, and how satisfied they are with what they run. Confidence in the ledger lags the spending.

Measurement trails money. Fewer than half of enterprises (44%) rigorously track the cost and return of their AI compute; the majority track only partially (39%), cannot quantify it yet (20%), or have not prioritized it (6%). That gap is consequential given Finding 5, where total cost of ownership was the second-ranked buying criterion — enterprises are choosing providers on an economic basis they mostly cannot yet measure. Satisfaction with current infrastructure is moderately positive but not enthusiastic: on a five-point scale, overall satisfaction averages 4.0, with ease of implementation (3.8) and value for money (3.9) trailing slightly — the softness landing, tellingly, on cost. Enterprises are spending quickly and accounting slowly.

Finding 8: The next bottleneck few are watching

As inference shifts from compute to memory, the field scatters

Finally, we asked how enterprises would address the emerging constraint in large-scale inference — the shift from GPU compute to memory, specifically KV-cache capacity. The responses reveal a frontier that is not yet a priority.

The memory frontier is real but barely governed. Asked which approach they would rely on as the binding constraint in inference shifts from compute to memory bandwidth, enterprises scatter: Dell leads at 31%, Nvidia follows at 16%, and the rest fragments across storage vendors, open-source tooling, and model-level efficiency techniques. Most telling is that roughly one in five (18%) either do not recognize the constraint or have not begun to address it. For a shift that will reshape inference cost and architecture, this is an early and unsettled market — and, consistent with the measurement gap in Finding 7, one where many enterprises simply do not yet have a view. It is the next chapter of the compute gap, arriving before most have closed the current one.

The bottom line: A compute gap that faster spending will widen, not close

Organizations with more than 100 employees are investing in AI infrastructure faster than they can measure it. Most are still early in deployment, yet their spending intentions point past their current stack — toward specialized clouds and alternative accelerators almost none of them run today — and a clear majority intend to change providers within the year. They buy on integration and total cost of ownership rather than headline price, which is rational; the difficulty is that most cannot yet see those economics clearly.

The visibility gap is concrete. The GPUs enterprises already own run at half utilization or less for the overwhelming majority, and fewer than half can rigorously track what their compute costs or returns. Satisfaction is decent but unenthusiastic, softest on value for money — the dimension hardest to judge without measurement. And the next constraint, the shift from compute to memory in large-scale inference, is arriving while most enterprises are still unaware of it. At 107 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market and earlier-stage adopters — but the direction is consistent: the appetite to spend is running well ahead of the instrumentation to spend well. The compute gap is not a capacity problem that more hardware will solve on its own; it is, first, a problem of seeing what the hardware already costs. The open question for later waves is whether enterprises build that visibility before the re-platforming arrives — or buy the next layer of infrastructure as blind to its economics as the last.

Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the results read cross-sectionally rather than as a month-over-month trend, and at 107 respondents this is a directional signal rather than a precise measurement — the sample is self-selected, skews mid-market, and leans toward earlier-stage adopters rather than the largest hyperscale operators. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with buyer-credible purchasing authority, across Technology/Software, Healthcare/Life Sciences, Financial Services, Retail/E-commerce, and other industries.

Google is renaming NotebookLM to Gemini Notebook, folding one of its most popular AI products into the Gemini brand while keeping it as a standalone tool. The company said on Thursday that the rebrand reflects how deeply the research assistant has been woven into the broader Google ecosystem, including the Gemini app and Google Search. More than 30 million people and over 600,000 organizations now use the tool, which Google first introduced as Project Tailwind at I/O 2023.

The name change arrives alongside a significant expansion of the tool’s most powerful feature. Last month, Google gave every notebook a secure cloud computer capable of writing and running code against a user’s uploaded sources, enabling complex data analysis, charts, and new output formats including spreadsheets and slide decks. That update was limited to Google AI Ultra subscribers and select Workspace business accounts, but with the Gemini Notebook rebrand, Google is rolling it out to AI Pro users on the web over the coming weeks.

The cloud computing environment runs Python scripts inside a secure container, processing tables and generating visualizations directly from the documents a user has uploaded. Digital Trends reported that the June update also moved the tool onto a new reasoning engine and added support for generating PDFs and structured data files. Google has not said when or whether free-tier users will gain access to code execution.

Beyond the rebrand, Google is pushing notebooks into more surfaces across its product line. Users can already create and access notebooks directly within the Gemini app, with full cross-app syncing between the two experiences. Google said it plans to bring notebooks into AI Mode in Search as well, though it did not share a timeline, a move that would place the research tool inside the same AI-driven search interface that now serves more than one billion monthly users.

The rebrand continues a pattern Google has followed across its AI lineup, absorbing experimental products into the Gemini brand once they prove successful. NotebookLM’s core proposition, grounding AI responses exclusively in user-provided sources rather than the open web, remains unchanged. Josh Woodward, VP of Google Labs, the Gemini app, and AI Studio, oversees the product and outlined Google’s broader AI integration strategy at I/O 2026 in May.

Whether the Gemini Notebook name helps or hurts the product’s identity is an open question. NotebookLM built its reputation precisely because it felt distinct from the chatbot-style Gemini experience, and some users may see the rebrand as a sign that Google is prioritizing brand consistency over the product’s independent character. The tool itself is better than it has ever been, but the name now carries the weight of an AI brand that Google has applied to everything from laptops to smart glasses.

North American buyers interested in the new Marantz MODEL 70 Integrated Amplifier can put their wallets away. Marantz has confirmed that the €850 component will not be sold in North America, leaving customers on this side of the Atlantic with only the matching CD 70 CD Player.

That is disappointing because the MODEL 70 looks like a thoughtful modernization of the long-running PM6007 formula, adding HDMI ARC, improved Bluetooth support and a more contemporary industrial design without turning the amplifier into another app-dependent streaming box.

North American buyers are not entirely out of options, however. The roughly $1,000 Marantz MODEL M1 Network Amplifier earned our 2025 Editor’s Choice Award and remains a compact, streaming-focused alternative worth considering.

European buyers will be able to purchase the MODEL 70 for €850 or £749. The CD 70 will be offered more broadly for $750 USD, $999 CAD, €600 or £499, with availability beginning August 15, 2026.

Marantz describes both components as successors to the PM6007 integrated amplifier ($750) and CD6007 CD player ($650), two of the brand’s most successful entry-level products. The new models retain the traditional full-width component format but adopt the cleaner design language used by Marantz’s more expensive MODEL Series products.

Both will be available in Black and Silver/Gold finishes.

Related Reviews:

Marantz MODEL 70 Integrated Amplifier

The MODEL 70 is a conventional two-channel integrated amplifier built around an upgraded current-feedback Class A/B output stage delivering 50 watts per channel.

That represents a modest increase over the PM6007, which is rated at 45 watts per channel into 8 ohms. More importantly, Marantz says the MODEL 70 uses an enhanced power supply and larger toroidal transformer intended to improve dynamics, loudspeaker control and overall authority.

Marantz has not disclosed the impedance or distortion conditions attached to the 50-watt rating, so it would be premature to draw conclusions about how much usable power the amplifier can deliver into more demanding 4-ohm loudspeakers.

The analog section incorporates Marantz’s proprietary HDAM circuitry, along with an internal DAC for digital sources. An MM phono stage allows a turntable to be connected directly, although moving-coil cartridge users will still require an external phono preamplifier or step-up device.

Where the MODEL 70 separates itself most clearly from the PM6007 is connectivity.

An HDMI ARC input allows the amplifier to handle television audio, with HDMI CEC providing volume control from a compatible TV remote. Marantz has also expanded Bluetooth functionality to support both transmission and reception.

Supported codecs include aptX Adaptive, aptX HD, AAC and SBC. Music can therefore be streamed from compatible phones and tablets, while the amplifier can also transmit audio to Bluetooth headphones for private music or television listening.

The MODEL 70 includes analog and digital inputs, along with preamplifier and subwoofer outputs. Marantz has not yet provided the number or type of each connection, nor has it disclosed the DAC chipset, maximum PCM and DSD resolutions, dimensions, weight or complete amplifier measurements.

Marantz has not mentioned HEOS, Wi-Fi, Ethernet, AirPlay 2 or integrated network streaming in the initial launch material. That may reflect an incomplete specification list, because a new Marantz integrated amplifier arriving in 2026 without HEOS would make very little sense. If those features are genuinely absent, the MODEL 70 would feel needlessly limited rather than refreshingly traditional, particularly when network playback has become central to Marantz’s modern product ecosystem.

MODEL 70 vs. STEREO 70s

The naming will inevitably create some confusion because Marantz already sells the STEREO 70s Network Stereo Receiver in North America for $1,200.

Despite their similar names and HDMI connectivity, these are not variations of the same product.

The STEREO 70s delivers 75 watts per channel and includes six HDMI inputs, three of which support 8K video. It also offers HEOS streaming, Wi-Fi, Ethernet, AirPlay 2, an AM/FM tuner, two subwoofer outputs and extensive television and gaming support.

The MODEL 70 is rated at 50 watts per channel and appears to offer a single HDMI ARC connection rather than functioning as a video-switching hub. It focuses more narrowly on two-channel audio, with a toroidal transformer, current-feedback amplification, HDAM circuitry, Bluetooth transmission and a built-in MM phono stage.

The practical difference is simpler: European buyers will receive a modern replacement for the PM6007, while North American customers looking for a Marantz amplifier with HDMI will have to consider the more feature-heavy and more expensive STEREO 70s.

Marantz CD 70 CD Player

The CD 70 is the component North American customers will actually be able to purchase, and its $750 price makes it one of the more interesting Marantz digital products in recent memory.

At its core, the CD 70 is a dedicated compact disc player incorporating the same high-performance DAC used in the MODEL 70, along with Marantz HDAM circuitry in the analog output stage.

A front-panel USB-A input expands playback beyond compact discs, with support for FLAC HD, ALAC, AIFF and DSD files stored on compatible USB devices. Marantz has not yet disclosed the maximum sampling rates or DSD resolution.

Construction upgrades include an improved power supply, double-layered chassis base, rigid isolation feet and strategically positioned copper hardware intended to reduce noise, vibration and electrical interference.

The CD 70 also includes a fully discrete headphone amplifier using HDAM technology. Adjustable gain should make it suitable for a wider selection of headphones, while automatic detection activates the headphone output when a plug is inserted.

Marantz has not yet confirmed whether the CD 70 includes optical and coaxial digital outputs, selectable digital filters, CD-R and CD-RW compatibility or the ability to function as a standalone DAC for external digital sources.

Those details matter, particularly when comparing it with the existing CD 60.

CD 70 vs. CD 60

The CD 70 will sell for $750 in the United States, making it $350 less expensive than the current $1,100 CD 60.

On the surface, the two players offer considerable feature overlap. Both use Marantz HDAM circuitry, include front-panel USB-A playback, support high-resolution PCM and DSD files and provide headphone amplifiers with adjustable gain.

The CD 60 supports CD and CD-R/RW playback, PCM files up to 24-bit/192kHz and DSD up to 5.6MHz. It also includes two selectable digital filters, fixed analog outputs and both optical and coaxial digital outputs. Its audio stage uses Marantz HDAM and HDAM-SA2 modules, while the chassis weighs 7.5 kilograms and includes an aluminum center panel.

Marantz has not provided enough information to determine how the CD 70’s transport mechanism, DAC implementation, analog output stage or overall construction compare with those of the CD 60.

The lower price suggests that there will be differences, but it would be unwise to invent them before the complete specifications arrive. A less expensive product does not automatically mean a lesser-sounding one, especially when the newer model benefits from several years of component and manufacturing development.

The more logical comparison may ultimately be with the CD6007, which remains available in the United States for $650. The CD 70 costs only $100 more while adding the current Marantz design, revised internal construction and a new DAC and headphone platform.

That could make the CD 70 the new value option in the range rather than a direct replacement for the more substantially constructed CD 60.

Familiar Marantz Design Without the Premium Price

Marantz has adopted the visual language of its more expensive MODEL Series components, including a symmetrical front panel, circular center display and full-width chassis.

The company says both models use vibration-resistant construction, optimized internal layouts and carefully selected components. Packaging has also been revised with FSC-certified cardboard and paper-based protective materials to reduce plastic consumption.

There is a substantial amount of marketing language about “culture-driven consumers” and audio becoming part of the home environment, but the underlying strategy is sound. Entry-level components no longer need to resemble laboratory equipment designed during the Carter administration.

The Bottom Line

The MODEL 70 may be the more interesting of the two products because it combines traditional Class A/B amplification, a toroidal transformer, MM phono stage, HDMI ARC and modern Bluetooth support in a relatively affordable full-size integrated amplifier.

Unfortunately, North American buyers will not get it.

The CD 70 is considerably better news. At $750, it lands only $100 above the CD6007 while undercutting the CD 60 by $350. Its combination of dedicated CD playback, high-resolution USB support, HDAM circuitry and a discrete headphone amplifier could make it one of the strongest values in the Marantz lineup.

There is still a lack of transparency surrounding the MODEL 70’s network capabilities. Marantz has not confirmed support for HEOS, AirPlay 2, Wi-Fi or Ethernet, which would be a surprising omission from a new integrated amplifier in 2026. It may simply be a case of incomplete launch specifications, but buyers should not have to guess whether a core part of the modern Marantz ecosystem is included.

Complete specifications will determine whether the CD 70 is merely a better-dressed CD6007 replacement or something capable of making the more expensive CD 60 rather uncomfortable. They will also reveal whether the MODEL 70 is a genuinely modern integrated amplifier or a product undermined by connectivity decisions that make little sense at this stage.

Pricing & Availability

Both new 2026 Marantz hi-fi products will be available in Black or Silver/Gold finishes at the following prices:

• Marantz MODEL 70 Integrated Amplifier – €850, £749 (a release date hasn’t been set at this time, but won’t be available in the North America)
• Marantz CD 70 CD Player – $750 USD, $999 CAD, €600, £499 (available August 15, 2026)

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