• 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.

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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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Skullcandy has introduced two new wireless over-ear headphones built around its defining Crusher feature: adjustable bass that is designed to be physically felt as well as heard.

The Crusher 1080 ANC costs $279.99 and combines Skullcandy’s sensory bass system with a suite of licensed Sound by Bose technologies. Those include Bose QuietControl active noise cancellation, TrueSpatial audio, the WaveForm Audio Engine, SpeechClarity voice processing and Bose Sound Design tuning.

The Crusher 720 costs $209.99. It retains adjustable Crusher Bass but drops active noise cancellation and the Bose processing package. In their place are THX Spatial Audio, Personal Sound by Mimi, an adjustable Stay-Aware mode and up to 65 hours of battery life.

Although they share the same basic concept and industrial design, these are not simply one headphone offered with or without ANC.

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What Makes Crusher Bass Different?

Conventional bass-heavy headphones rely on larger drivers, elevated low-frequency tuning or digital signal processing. Crusher adds dedicated bass drivers that create physical vibration alongside the sound produced by the primary full-range drivers.

The level can be adjusted using a wheel on the earcup or through the Skullcandy app. Users can reduce the tactile effect to a mild reinforcement or increase it until subtlety leaves the building by the nearest available exit on the Garden State Parkway.

Skullcandy positions the system for music, movies and gaming, where low-frequency impact can add another dimension to kick drums, electronic bass, explosions and vehicle effects. The company does not disclose the size, frequency range or power requirements of the separate bass drivers in either new model.

That adjustable tactile system remains the feature competitors do not directly replicate. Sony, Beats, Bose and Sennheiser can deliver substantial conventional bass, but they do not offer a separate physical bass control performing the same role.

Crusher 1080 ANC

The Crusher 1080 ANC is the flagship of the pair and the more consequential release.

Skullcandy describes Sound by Bose as a suite of Bose-licensed audio technologies. Bose is not manufacturing or selling the headphone; the underlying hardware, product design, app integration and Crusher Bass system remain Skullcandy’s.

The Bose package includes:

• Bose QuietControl active noise cancellation
• Bose TrueSpatial audio with head tracking
• Bose WaveForm Audio Engine
• Bose SpeechClarity voice pickup
• Bose Sound Design tuning
• Bose-tuned Music, Movie and Podcast EQ modes
• Adjustable Aware transparency mode

QuietControl uses six microphones and adaptive processing to reduce surrounding noise. Users can switch among Quiet, Aware and ANC Off modes using the headphone controls or Skullcandy app. Skullcandy has not published attenuation measurements, so comparisons with Bose’s own QuietComfort models will require independent testing.

TrueSpatial is intended to create a wider, more speaker-like presentation for music, movies and games. Skullcandy also lists head tracking, which keeps the apparent soundstage in place as the listener moves.

The WaveForm Audio Engine manages the overall presentation across different playback levels. Skullcandy says the processing is intended to preserve vocal clarity, tonal balance and lower distortion as the volume and Crusher Bass settings increase. That is a useful objective because tactile bass is not particularly valuable if the midrange has been buried beneath it.

SpeechClarity handles voice pickup during telephone and video calls, while natural sidetone lets users hear some of their own voice rather than shouting into a conference call like someone directing aircraft on a carrier deck.

The Crusher 1080 ANC uses 36mm primary drivers with a claimed frequency response of 20Hz to 20kHz, a nominal impedance of 36 ohms and a listed weight of 374.2 grams. That is heavy for a wireless travel headphone and substantially heavier than the existing 332-gram Crusher ANC 2. Comfort and weight distribution will therefore matter almost as much as the specifications.

Battery life is rated at:

• 60 hours with ANC off
• 50 hours with ANC on
• Four hours of playback from a ten-minute charge

Bluetooth 5.3 includes LE Audio, Auracast, multipoint pairing, Google Fast Pair, automatic reconnection and a low-latency mode. Wear detection can pause playback and activate automatic power-off.

The Skullcandy app provides control over Crusher Bass, Bose noise cancellation, spatial processing, preset EQ modes, a customizable five-band equalizer, button assignments, sidetone and other smart features.

The headphones fold flat and collapse for storage. Skullcandy includes a 3.5mm analog cable, USB-C charging cable and roll-top travel bag.

Available finishes are True Black, Candy, Primer and Cement.

Crusher 720

The Crusher 720 is the less expensive model, but it is not merely a stripped-down Crusher 1080 ANC.

It uses larger 40.7mm primary drivers and retains the independently adjustable Crusher Bass system. Instead of Bose TrueSpatial, it includes THX Spatial Audio, which Skullcandy says creates a more immersive, multidimensional presentation for music, movies and gaming.

Skullcandy’s dedicated Crusher 720 product description does not clearly confirm head tracking, so we would not assume that the feature is included based solely on the company’s currently inconsistent comparison tables.

The Crusher 720 also includes Personal Sound by Mimi. The system analyzes the user’s hearing and creates a customized profile intended to compensate for frequencies that may be less audible to that individual.

There is no active noise cancellation. An adjustable Stay-Aware mode uses the microphones to pass surrounding sound through the headphones when the listener needs to hear traffic, announcements or another human being attempting to attract their attention.

Battery life is rated at 65 hours, with four hours available from a ten-minute charge.

Other features include:

• Bluetooth 5.3
• LE Audio and Auracast
• Multipoint pairing
• Google Fast Pair
• Wear detection and automatic power-off
• Low-latency audio
• Clear Voice microphone processing
• Adjustable call sidetone
• Music, Bass Boost and Podcast EQ modes
• Five-band custom EQ
• App customization
• 3.5mm analog input
• Flat-folding and collapsible construction

The Crusher 720 has a claimed frequency response of 20Hz to 20kHz, a nominal impedance of 36 ohms and a listed weight of 362.8 grams. It is lighter than the Crusher 1080 ANC, but nobody is likely to mistake it for a featherweight travel headphone.

Skullcandy includes a 3.5mm cable, USB-C charging cable and drawstring travel bag.

The five finishes are Future, Plasma, True Black, Cement and Primer.

Crusher 1080 ANC vs. Crusher 720

The $70 price difference buys more than active noise cancellation.

The Crusher 1080 ANC adds Bose QuietControl ANC, TrueSpatial with head tracking, the WaveForm Audio Engine, SpeechClarity and Bose-developed sound tuning. It is the better option for frequent travelers, commuters and anyone who wants the most complete version of Skullcandy’s sensory bass platform.

The Crusher 720 offers longer battery life, THX Spatial Audio and hearing personalization for less money. It is likely the more sensible choice for home listening, casual gaming and movie playback when active noise cancellation is not required.

The 720 also avoids paying for ANC that some users will rarely activate. That matters because a substantial portion of the Crusher audience is likely using these headphones at home, in a dorm room or in front of a television rather than attempting to erase the sound of an aircraft cabin.

Who Are They For?

Both models are aimed first at listeners who actively want elevated, physical low-frequency impact.

Hip-hop, electronic music, pop, action movies and games are the obvious applications. The adjustable bass system may also appeal to listeners who use headphones at lower playback levels but still want to feel some of the scale normally associated with loudspeakers and subwoofers.

The Crusher 1080 ANC is better suited to:

• Frequent travelers and commuters
• Listeners who want strong bass without giving up ANC
• Mobile gaming and movie playback
• Existing Crusher users looking for a more complete flagship
• Buyers interested in Bose processing at a lower price than Bose’s premium headphones

The Crusher 720 is more appropriate for:

• Home listening
• Casual gaming
• Buyers who do not require ANC
• Users who value battery life and hearing personalization
• Existing Crusher Evo owners looking for spatial processing and newer connectivity

Neither model is aimed primarily at listeners seeking neutral studio-monitor tuning, the lowest possible weight or a traditional audiophile presentation. These are sensory-bass headphones by design, not a secret replacement for an open-back headphones from Beyerdynamic or Grado Labs.

Main Competitors

The most direct external rival is the Sony ULT Wear. Sony also targets bass-focused listeners and includes ANC, an ambient mode and up to 30 hours of battery life at a regular price of $249.99. Sony uses conventional driver tuning and DSP rather than dedicated tactile bass drivers, making it the closest competitor in audience but not in operation.

The Sennheiser Momentum 4 Wireless currently sits close to the Crusher 1080 ANC in price. It offers adaptive ANC, a 42mm driver, customizable sound and up to 60 hours of battery life. Sennheiser is the more obvious choice for listeners prioritizing tonal balance and conventional high-fidelity sound, while Skullcandy offers the more physical and deliberately bass-focused experience.

The Beats Studio Pro provides ANC, transparency, personalized spatial audio with dynamic head tracking and lossless wired playback through USB-C or 3.5mm. Battery life reaches 40 hours with ANC disabled. Beats also offers tighter integration with Apple and Android devices, but it does not provide adjustable tactile bass.

The standard Bose QuietComfort Headphones remain a major competitor for travel and noise cancellation. They offer Bose’s established ANC and a significantly lighter, more conventional design, but only 24 hours of battery life and none of Skullcandy’s tactile low-frequency hardware.

Skullcandy’s Crusher ANC 2 from 2023 sits directly between the two new models at $239.99 but is often on sale for less at Amazon. It offers adjustable four-microphone ANC, Mimi Personal Sound and up to 60 hours of battery life, but lacks the newer Bose processing, spatial audio and Auracast support of the Crusher 1080 ANC.

Unless it receives a substantial price cut, the Crusher ANC 2 may now occupy an awkward middle seat sandwiched between siblings that don’t feel the need to share those lousy airline crackers. Do people actually eat that garbage?

Key Features Compared

What Skullcandy Has Not Confirmed

Skullcandy does not list support for LDAC, aptX Adaptive or aptX Lossless. Both models support LE Audio and Auracast, but the complete Bluetooth codec set has not been published.

The company also does not state whether the USB-C connection supports digital audio playback or is limited to charging.

Neither model has a published IP resistance rating.

Skullcandy has not confirmed whether Crusher Bass, spatial processing and other powered features remain available through the 3.5mm connection or when the battery is depleted.

The Bottom Line

The Crusher 1080 ANC and Crusher 720 are unusual because Skullcandy is not attempting to copy the premium wireless headphone market feature for feature.

The company already owns a distinct position with adjustable tactile bass. The new models attempt to build better-rounded wireless headphones around that technology.

The Crusher 1080 ANC is the more important of the two. Bose’s licensed technologies address noise cancellation, spatial presentation, call quality, volume-dependent processing and overall sound tuning. The intention is clear: retain the physical impact of Crusher Bass while improving everything around it.

The Crusher 720 makes a different calculation. It removes ANC, lowers the price and adds THX Spatial Audio, Mimi personalization and longer battery life. For buyers who use their headphones primarily at home, that may be the better value.

Neither model will appeal to listeners who regard tactile bass as an attack on public order. For the audience that already understands why Crusher exists, however, these are the most complete versions of the concept so far.

Now we need to hear whether Bose has helped Skullcandy control the bass or merely taught it better tray table manners for those awkward meals next to Karens at 30,000 feet.

Price & Availability

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For most of the last two decades, enterprise security ran on a workable assumption: the environment was knowable. Security teams could buy tools, inventory users, map systems, define policies, and rely on vendor-built dashboards and workflows to manage most of what happened next.

The model was imperfect, but it worked because the environment changed at human speed.

AI agents broke that assumption, and with it, the playbook.

Agents are not ordinary applications. They act autonomously, invoke tools, acquire access across systems, and change behavior based on context. Some are sanctioned and run in SaaS platforms. Others are unsanctioned and run locally. They can borrow human access and disappear before the next inventory scan.

They also vary enormously in what they can reach; Token Security research on how enterprises are actually deploying agents found everything from human-triggered chatbots to autonomous production services, with more than a fifth of local agents already holding direct access to production data sources.

The build-vs-buy conversation in cybersecurity has now fundamentally changed. The old question was simple: should we buy a tool or build one ourselves? In the agentic era, that framing is too narrow.

Security teams do not need to rebuild the entire stack, but also can’t rely on fixed workflows someone else created months earlier.

The better question is: which layer should security teams own?

The Limits of Fixed Security Workflows

AI agents make environments more specific, more dynamic, and harder to anticipate. A vendor can build a dashboard for common risks: overprivileged service accounts, stale credentials, dormant admin users, excessive permissions, and identities with access to production systems.

That is useful, but the most important questions are often specific to a single environment.

• Which agents created in the past two weeks can reach production through inherited human credentials?
• Which local coding agents still have active tokens after a project ended?
• What is a potential attack path from one system to another using AI agents?

These questions do not fit neatly into a generic workflow. They depend on the organization’s cloud footprint, SaaS stack, development practices, ownership model, compliance requirements, and AI adoption patterns. No vendor roadmap can anticipate every combination.

That is the operationalization gap. Security teams can often identify risk categories, but they cannot always translate them into the exact remediation path their environment requires. AI agents widen this gap because they move faster than traditional tooling cycles.

Waiting two quarters for a vendor feature while agents continue accumulating access is not an effective security strategy. It is a queue.

Why “Just Build It” Is Not the Answer

AI-assisted development has changed what teams can build. Retool’s 2026 Build vs. Buy report found that 35% of teams had already replaced at least one SaaS tool with something they built themselves, and 78% expected to build more this year.

This trend has real security implications, since AI has made building custom tools far faster and easier. Work that once took weeks of engineering can now be prototyped in hours.

But cybersecurity has a harder problem than most business functions: the data layer. A useful security workflow is only as good as the identity, access, permission, ownership, and activity data underneath it. Building a custom app is one thing. Connecting it safely to live enterprise systems is another.

Security teams should not have to rebuild integrations across AWS, Azure, GitHub, Salesforce, Okta, secret managers, CI/CD pipelines, SaaS platforms, agent frameworks, and on-prem systems.

They should not have to normalize every schema themselves or maintain fragile scripts that break when an upstream API changes.

That is the hidden cost of “just build it.” The hard part is not generating code but building on data that is live, normalized, secure, and complete enough to support real decisions.

Buy the Foundation to Own the Operational Layer

The future of cybersecurity is not pure build or pure buy. It is building on the right foundation.

Security teams should invest in the layers that are structurally complex and widely adopted across organizations: continuous discovery, integrations, normalization, identity correlation, access mapping, governance controls, auditability, and secure execution boundaries.

Those capabilities require depth, scale, and constant maintenance. They are not where most security teams should spend their scarce engineering time.

But teams should own the operational layer: the workflows, applications, reports, reviews, and automations that reflect their specific environment.

That is where differentiation lives. That is where security teams encode how their organization actually works: who owns which agents, which systems matter most, what access is acceptable, which exceptions are allowed, how risk is prioritized, and what remediation should happen next.

The winning model is not “buy everything” or “build everything.” It is “buy the foundation, build the operating layer.”

Identity is the layer that holds

For AI agents, the foundation has to be identity. Every meaningful agent eventually requires access. It authenticates, uses credentials, invokes tools, and reaches data.

Often, it does not even have an identity of its own and instead borrows one from an employee, which is why the agents already running within enterprises can be indistinguishable from the people they impersonate in your audit logs.

That is why identity is the only control plane that actually governs agentic AI, and why it is the foundation on which to build. It is the one place your team can see and enforce discovery, ownership, access, and lifecycle for every agent at once.

Guardrails, prompt filtering, and behavior controls act on what an agent says. Identity governs what an agent can reach, and reach is what determines blast radius.

A live identity foundation gives security teams the context they need to ask and answer the questions that matter:

• Who owns this agent?
• What is it supposed to do?
• Which identities does it use?
• What systems can it reach?
• Does its access match its intent?
• What happens when it is abandoned, compromised, or changed?

Without that foundation, custom workflows sit on sand. They rely on stale exports, partial inventories, and one-off scripts.

With it, security teams can build operational logic that stays connected to the real environment as agents appear, change, and disappear.

The teams that stay effective

The security playbook built for a knowable environment is not coming back. AI agents made sure of that. The next playbook is more adaptive.

It assumes the environment will keep changing. It assumes no vendor can prebuild every workflow. It assumes security teams need the ability to compose controls, reports, reviews, and remediation paths that fit their own reality.

But it also recognizes that teams should not rebuild the foundation themselves. The teams that stay ahead will not be the ones with the longest tool list or the most generic dashboards. They will be the ones who know which layer to own.

For agentic AI, the answer is clear: build on a live identity foundation and own the operational layer that must adapt. In the agent era, that is how security teams move fast without losing control.

If you’re looking to secure your agentic AI, book a quick technical demo with Token Security to see how they can secure your organization as you scale.

Sponsored and written by Token Security.