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Perplexity’s Bold Windows AI Upgrade Might Run Your PC

Author: Ciente Editorial Team

Perplexity
News

Perplexity’s Bold Windows AI Upgrade Might Run Your PC

Perplexity’s Bold Windows AI Upgrade Might Run Your PC

Perplexity launched an autonomous desktop agent for Windows that controls software and manages local tasks. Here is how it challenges Microsoft and redefines personal computing.

Imagine handing your computer mouse to an AI that actually knows what to do with it.

Perplexity just made that vision a reality.

The company launched a major update to its Windows app, introducing an autonomous desktop agent. This new tool moves far beyond standard web search. It actively navigates your PC, interacts with software, manages local files, and executes complex multi-step workflows across your applications.

Microsoft spent billions building Copilot into Windows.

Yet, Perplexity just outmaneuvered them on their own operating system. Perplexity created a true hands-on co-pilot that operates directly across your desktop screen instead of hiding AI inside sidebars and search boxes.

This move shifts AI from a passive research assistant into an active digital worker. Naturally, handing over screen control and file access requires real consumer trust. Users will rightfully demand tight security controls and complete transparency before letting an agent click buttons on their behalf.

Still, Perplexity executed a brilliant product strategy.

The startup has effectively transformed a simple search tool into an indispensable productivity workspace- forcing AI to do the daily work rather than merely summarize it.

Perplexity offers a glimpse into the future of personal computing.

Pay Pal
News

PayPal Delivers Big Q2 Beat and Sidesteps Buyout Pressure

PayPal Delivers Big Q2 Beat and Sidesteps Buyout Pressure

PayPal raised its 2026 profit guidance after beating Q2 earnings targets. Here is how the payment giant proves its standalone value.

PayPal just sent a loud message to Wall Street: its turnaround plan actually works.

The digital payments giant posted impressive second-quarter results on Tuesday. Adjusted earnings hit $1.38 per share, crushing Wall Street’s $1.28 target. Its revenue grew 3% to $8.68 billion while the total payment volume jumped to $486.4 billion.

PayPal’s full-year profit forecast increased to $5.38 per share following a strong performance. Management is actively cutting operational costs while accelerating growth across Venmo and branded checkout.

These earnings win lands at a pivotal moment.

Stripe and Advent International recently launched a stunning $53 billion buyout offer- valuing PayPal at $60.50 per share. Tuesday’s strong numbers give PayPal’s board major leverage. The company just proved it can generate massive value without accepting a rescue deal.

However, tech buyers and investors watch PayPal closely. Rising competition from Apple Pay, Google Pay, and agile fintech rivals keeps pressure high across the entire industry.

PayPal’s resurgence will benefit everyday consumers and digital merchants. A thriving PayPal keeps processing fees competitive and forces every fintech player to innovate faster. The company now holds all the cards- whether it remains independent or negotiates a deal on its own terms.

Account farming
Blogs

A Guide to Account Farming: The Revenue Strategy Hiding in Your Existing Customer Base

A Guide to Account Farming: The Revenue Strategy Hiding in Your Existing Customer Base

New logos get all the glory. Expansion revenue funds the business. Here’s what account farming actually looks like when it’s done right.

New logo acquisition gets the war room treatment.

SDR headcount. Outbound sequences. ABM campaigns. Intent data subscriptions. Pipeline reviews every Thursday. The whole GTM machine pointed squarely at companies that have never bought from you before.

Meanwhile, the accounts that already cut you a check, already proved your product solves something real for them, already offered you access to their systems and their people? They get a quarterly check-in. Maybe a renewal call sixty days out. And a CSM who’s managing forty other accounts with the same level of attention.

That imbalance is expensive. Not just in missed revenue. In avoidable churn, in shallow relationships, in expansion opportunities that land with competitors because nobody was paying close enough attention to notice them first.

Account farming is the discipline that fixes it. It’s not upselling dressed up in nicer language. It’s a deliberate, structured approach to treating existing customers as the highest-value pipeline segment within the business. Because they are.

What Account Farming Actually Means in B2B Sales

Account farming is the practice of cultivating existing customer relationships to grow revenue over time.

That “over time” part is what most teams mishandle. Account farming isn’t a renewal call. It isn’t an annual QBR. It’s a sustained, proactive motion built around understanding where the customer is headed, where your product helps them get there, and what it takes to deepen the relationship enough that the expansion conversation feels natural rather than opportunistic.

The farming metaphor holds up.

You don’t plant a seed and harvest the next morning. You prepare the soil, water consistently, pay attention to the conditions, and pick the right moment. Teams that treat account farming as a one-touch motion before renewal are not farming. They’re just harvesting on a fixed schedule and wondering why the yields keep dropping.

Genuine account farming requires deeply knowing the account . Not just the main contact. The department heads, the end users, the internal champion, the economic buyer, the skeptic who nearly killed the deal during procurement. The whole organism.

Why Account Farming Outperforms New Logo Acquisition on Nearly Every Metric

Acquiring a new customer costs anywhere between five and seven times more than retaining and growing an existing one.

That’s not a soft argument for investing in customer success investment. That’s a unit economics argument. The cost of generating pipeline, running a full sales cycle, and onboarding a net-new account is significant. An account farming motion runs on a fraction of that spend and compounds every time it works.

Net Revenue Retention tells the full story. An NRR above 110% means the business grows even if it signs zero new logos this quarter. The existing customer base expands faster than it churns. That’s the financial profile investors value most in SaaS, and account farming is what produces it.

Expansion revenue also closes faster. The trust is already there. The product already works. The objections are narrower. A properly farmed account that’s ready to expand doesn’t need a full discovery process. They need someone who understands their current situation well enough to show them what’s possible next. That’s a different, shorter, easier conversation than a cold enterprise deal.

The Account Farming Signals Most CSMs Miss

Not every expansion conversation has to be manufactured. Most of the time, the account tells you it’s ready. The signal just goes unnoticed.

Usage data is the loudest signal in the room.

A team that started with three users and now has twenty-two, all using the product daily, is showing you something. They’re getting value. The product embedded itself. That’s the exact moment to understand what’s next for them and whether there’s a tier or a product line that matches it.

New headcount is another one.

A target account posting six roles in the department that uses your product isn’t just growing. They’re probably going to need more licenses, more capacity, or both. That’s not an assumption. It’s an obvious inference from publicly available information that most CSMs aren’t looking at.

Leadership changes matter too.

A new VP of Sales or a new CRO coming into an account that uses your product is both a risk and an opportunity. If the relationship lived with one person, it’s at risk. If someone gets to that new leader early and builds the relationship from scratch, it’s an opportunity to anchor the account at a higher level than it currently sits.

Silence is also a signal.

An account that used to engage regularly and has gone quiet isn’t necessarily happy. They’re either solving the problem elsewhere, lost faith in the product, or had a change internally that nobody flagged. All three of those scenarios need a response faster than the next scheduled QBR.

How to Build an Account Farming Motion That Actually Scales

Multi-Threading: The Account Farming Move Most Teams Skip

Single-threaded accounts are the most fragile relationships in any book of business.

One contact leaves. A reorg happens. A new decision-maker arrives with different priorities and no relationship with your team. The whole account goes cold, or worse, gets put back into active evaluation by a competitor who got to the new leader first.

Multi-threading means building relationships across the account at multiple levels and multiple functions. Not just the main point of contact. The end users who know exactly what’s working and what isn’t. The department head who owns the budget. The executive who cares about the category-level outcome your product delivers.

Each of those relationships serves a different purpose. End users give you the ground truth on product performance. Department heads give you context on what’s coming next in their roadmap. Executives give you the access to have conversations about strategic expansion that a mid-level contact can’t authorize.

Most CSMs stay comfortable with the contact they know. Account farming requires going wider deliberately, even when nobody asks for it.

The Account Farming QBR That Goes Beyond the Relationship Check-In

The standard QBR format runs something like this. Review usage metrics. Share a few wins. Ask if there are any concerns. Schedule the next one.

That’s a relationship maintenance exercise. It’s not account farming.

A QBR built around account farming looks different. It starts with a forward-looking success plan tied to the customer’s actual goals for the next six to twelve months. Not generic goals like “improve efficiency.” Specific ones. Reduce time-to-hire by 30%. Expand into two new territories by Q3. Consolidate three tools into one platform.

Those specifics matter because they create a map. The rep or CSM can see where the customer is trying to go, where the product currently helps, and where there’s whitespace between the two. That whitespace is the expansion conversation. Not “we have a new product,” but “here’s the gap between where you’re going and what you currently have, and here’s what closes it.”

That’s a fundamentally different QBR. Customers notice the difference immediately.

Where Account Farming Falls Apart

The most common failure mode is treating account farming as a CSM responsibility with no sales involvement.

CSMs are relationship people. At their best, they maintain trust, drive adoption, and flag risk. But converting expansion opportunities into revenue is a sales motion. It requires discovery, commercial negotiation, and stakeholder management across a buying committee. Expecting a CSM to run that process while managing a portfolio of forty accounts is how expansion revenue gets left on the table quarter after quarter.

The fix is a defined handoff. CSMs identify the signal. CSMs qualify the interest. Then the AE or a dedicated expansion rep takes the commercial motion from there. Both functions need to know their lane, and the transition needs to happen fast enough that the window doesn’t close before anyone acts.

The other failure mode is reactive farming. Waiting for the customer to raise a need before exploring it. By then, the customer has often already started evaluating alternatives. The whole advantage of farming, catching the expansion moment before it becomes a competitive situation, disappears when the motion is reactive.

Account Farming in 2026: What AI Changes About the Motion

The practical bottleneck in account farming has always been coverage.

A CSM managing fifty accounts cannot monitor usage signals, track job postings, follow leadership changes, and review product telemetry across all fifty simultaneously. Something gets missed. Usually several things.

AI changes that math. Usage anomalies surface automatically. Expansion signals aggregate in real time. Accounts showing patterns that historically precede churn or growth get flagged before the next scheduled check-in. The CSM shows up to the account conversation with context that would have taken hours to compile manually.

What AI doesn’t change is the judgment layer. Knowing that an account just hired a new VP of Revenue is a signal. Knowing how to approach that person, what to say, and when to say it requires understanding the account, the relationship history, and the commercial dynamic. That’s still human work. AI just makes sure the signal doesn’t get missed while the CSM is busy with something else.

The teams building account farming programs around AI-assisted signal detection and human-led expansion conversations are the ones compounding NRR consistently. The signal tells you when to show up. The relationship quality determines what happens when you do.

Account Farming Is Not a Nice-to-Have.

New logo acquisition will always matter. The business needs growth from new markets and new segments.

But the math on expansion is simply better. Lower CAC. Shorter sales cycles. Higher close rates. More predictable revenue. And a customer base that grows into a moat over time, because deeply farmed accounts don’t switch vendors casually.

The teams that treat account farming as a structured program rather than a loose CSM responsibility win on NRR. The ones that pair it with product telemetry and expansion signals aren’t missing the windows. And the ones that multi-thread early build the kind of account depth that survives leadership changes, reorgs, and competitive pressure.

Start with the accounts you already have. They’re the most underworked pipeline in the business.

OpenAI
News

NVIDIA’s $250 Billion Bet on OpenAI is the Ultimate AI Infrastructure Play

NVIDIA’s $250 Billion Bet on OpenAI is the Ultimate AI Infrastructure Play

NVIDIA is negotiating a massive $250 billion financial backstop for OpenAI’s proposed 10-gigawatt Ohio data center.

The AI infrastructure race just shattered every precedent in corporate finance. NVIDIA is negotiating a staggering $250 billion financial backstop for OpenAI. The proposed guarantee supports a massive 10-gigawatt data center campus in southern Ohio.

SoftBank’s energy subsidiary is building the $500 billion project on a former federal uranium site. Because OpenAI lacks an investment-grade credit rating, lenders need reassurance before writing massive checks. NVIDIA’s corporate guarantee acts as the ultimate credit boost.

From a strategic perspective, the move is brilliant:

  1. For OpenAI: The deal provides computational independence. OpenAI can run its own infrastructure instead of renting server capacity from Microsoft or Amazon.
  2. For NVIDIA: The backstop guarantees long-term chip demand. NVIDIA is also discussing a separate $350 billion deal to finance OpenAI’s actual chip purchases.

Still, the sheer scale of vendor financing raises valid questions. NVIDIA is essentially funding its own biggest customer to buy its own product. That circular risk could ripple through the tech ecosystem if the AI boom slows.

Yet, treating this deal as a red flag misses the bigger picture.

Traditional banks cannot keep pace with the massive capital requirements of frontier AI. Modern chipmakers must become creative financial architects to build next-generation infrastructure.

NVIDIA is stepping up to bridge that gap. The deal proves that building physical AI capacity now requires bold financial innovation alongside advanced silicon engineering.

NVIDIA
News

Market Sentiments Suggest NVIDIA’s Open AI Security Alliance Might Be the Defense Tech We’ve Needed All Along

Market Sentiments Suggest NVIDIA’s Open AI Security Alliance Might Be the Defense Tech We’ve Needed All Along

NVIDIA teamed up with tech giants to launch the Open Secure AI Alliance. Why have open-weight models become such essential cybersecurity tools?

Tech leaders are taking decisive action. NVIDIA just launched the Open Secure AI Alliance with industry heavyweights such as Microsoft, CrowdStrike, Dell, and Hugging Face.

The coalition focuses on a clear mission: building open-source tools, model weights, and datasets to elevate global cyber defense. Building on the Linux Foundation’s security initiatives, the alliance aims to share threat intelligence freely across the tech ecosystem.

The timing carries huge significance. During the recent Hugging Face breach, engineers ran into an unexpected roadblock. Closed commercial AI models repeatedly blocked security teams from running forensic analysis. The models’ built-in safety guardrails could not distinguish between an attack and a legitimate investigation, leaving security researchers stranded.

To contain the threat, Hugging Face engineers deployed an open-weight model on their own private servers. That open model analyzed over 17,000 malicious actions and successfully neutralized the intrusion.

This real-world crisis highlighted a vital truth.

Proprietary AI systems offer great capabilities, but cybersecurity teams require complete transparency. Closed systems create single points of failure. In contrast, open-weight models allow engineers to inspect code, adapt security harnesses, and run deep forensics without artificial restrictions getting in the way.

NVIDIA is backing this initiative with serious resources. The company is contributing open models, model weights, and agent harnesses to help developers build custom security tools.

This alliance is a mature shift in AI governance. Industry leaders are recognizing open-source AI models as vital defensive shields rather than viewing them as a safety risk.

It’s imperative. Open collaboration gives defenders the exact speed and visibility they need to keep systems safe- especially in an era of rapid AI deployment.

Voice of customer
Blogs

Voice of Customer Data Is Everywhere, But Do Companies Know How to Act on It?

Voice of Customer Data Is Everywhere, But Do Companies Know How to Act on It?

Companies religiously collect Voice of Customer (VoC) data, filing it away. The gap between listening and acting is where brands are losing customers.

Companies claim to listen to their customers- ask them how, and they’ll point to a survey. Maybe an NPS score. A customer support ticket. A quarterly review of online ratings.

That’s not a Voice of Customer program. That’s a data collection habit with no feedback loop attached.

Voice of Customer (VoC) is the system process of capturing what customers actually think, feel, and need- and then routing that intelligence into every relatable decision. The distinction sounds subtle. The operational gap is enormous.

Companies with real VoC programs don’t just know what their customers say. They build business decisions around it. They catch problems before they compound. They outgrow competitors because they know what buyers want before those buyers articulate it to anyone else throughout the customer lifecycle.

The ones without a real program? They send surveys, read the scores, nod, and file the results somewhere nobody checks until next quarter.

What Voice of Customer Actually Captures (And What It Misses)

The clinical definition of VoC covers customer feedback about their experiences, expectations, and preferences across every touchpoint with a brand, product, or service.

In practice, it goes further than that.

A well-built VoC program captures explicit feedback, the things customers say directly through surveys, reviews, and interviews.

It captures implicit feedback, the behavioral signals customers leave through how they use a product, where they drop off, what they click and what they skip. And it captures inferred feedback, the patterns sitting underneath both of those that point toward needs the customer hasn’t articulated yet.

Most programs only get the explicit layer. They run a post-purchase survey, track the score, and call that their VoC function. That’s one input out of three, and it’s the least revealing one.

Customers don’t always say what they mean. But they always show it through their customer behavior. The companies that catch the behavioral and inferred layers are working from a complete picture. The ones living off surveys are working from a partial one.

Why Most Voice of Customer Programs Produce Reports Instead of Results

Forrester research puts the number of brands that their customers feel listen and respond to them at a fraction of those that claim to have listening programs. That gap exists for a specific reason.

Most VoC programs are built to collect. Not to act.

The survey goes out. The responses come back. Someone builds a chart. The chart goes into a presentation. The presentation gets reviewed at the quarterly business meeting.

Maybe one or two findings get highlighted. Maybe a product team gets tagged in a Slack message. And then the next quarter starts and the same process repeats, without any clear link between what the data said and what the business actually changed.

That’s not a VoC failure. It’s an implementation failure. The program exists. The infrastructure to act on it doesn’t.

Real Voice of Customer programs wire the feedback directly into the workflows of the people who can change things. Product findings reach the product team in time to influence the next roadmap cycle, not six months after it’s locked. Service friction surfaces in the contact center before it becomes a churn signal. Messaging gaps show up in marketing before a competitor uses them.

The speed and specificity of that routing is what separates a VoC program that moves the business from one that documents its problems.

Voice of Customer Collection Methods Worth Taking Seriously

Surveys and Feedback Forms: Still the Baseline

Surveys aren’t dead. They’re just frequently misused.

The value of a survey isn’t the score it produces. NPS of 42 tells you almost nothing actionable on its own. The value is in the open-ended text underneath it. What did the customer write? Which words do they keep using? Which frustration keeps surfacing across different respondents in different segments?

Written feedback is the richest raw material in VoC. The problem is that most teams automate the score tracking and ignore the text. Flip that priority. The number is a trend line. The words are the insight for improving the customer experience.

Online Reviews: The Voice of Customer Signal Hiding in Plain Sight

Customer reviews on Google, G2, Capterra, and industry-specific platforms are unsolicited, unfiltered, and often more honest than anything a customer puts in a branded survey.

People write reviews when they feel something strongly. They’re motivated by genuine enthusiasm or genuine frustration, which means the signal quality is high.

The challenge is volume and dispersion. Reviews live across multiple platforms, arrive continuously, and rarely get analyzed alongside internal feedback data.

AI-driven sentiment analysis changes this. Tools that pull external review data into the same analysis layer as internal survey responses give a unified read on customer sentiment across every channel. That unification is where CX analytics enables real pattern recognition.

Digital and Omnichannel Analytics: Where Voice of Customer Gets Interesting

Behavioral data tells a different kind of story. Where a customer drops off in the onboarding flow. Which features get used once and abandoned. Which pages attract traffic and produce no conversions. These aren’t things customers say. They’re things customers show during customer activation.

The companies getting the most from behavioral analytics as part of VoC aren’t just tracking clicks. They’re connecting behavioral signals to satisfaction data and asking which behaviors predict which outcomes.

Customers who don’t complete a specific onboarding step churn at twice the rate. Customers who contact support within the first thirty days are three times less likely to renew. Those connections exist in the data.

Finding them requires building the analysis infrastructure to look.

What a Real Voice of Customer Program Looks Like in Practice

Cross-Functional Ownership Is Non-Negotiable

VoC data doesn’t belong to one team. Customer experience owns it nominally. But the findings should reach product, marketing, sales, support, and leadership simultaneously, filtered for what’s relevant to each function.

A product gap found in churn interviews needs to reach the product team. A messaging disconnect showing up in sales call transcripts needs to reach marketing so teams can refine their customer value proposition. A support friction point that keeps surfacing in reviews needs to reach operations.

Each of those is a VoC signal landing in the wrong function or not landing anywhere at all in most companies.

The fix is structural. Not a better dashboard. A defined routing system that tells the VoC program where each type of finding belongs and who’s accountable for acting on it within what timeframe.

Voice of Employee: The Voice of Customer Insight Nobody Talks About

The employees closest to customers carry a layer of VoC insight that no survey captures.

Support reps know which complaints repeat. Sales reps know which objections keep coming up. Customer success managers know which promises don’t survive first contact with the product. That institutional knowledge rarely enters the formal VoC program. It lives in people’s heads, surfaces occasionally in team meetings, and rarely shapes anything strategic.

Connecting employee feedback to customer feedback produces a more complete picture of what’s actually happening at the experience layer. A pattern showing up in customer reviews that also surfaces in support team feedback isn’t a coincidence. It’s a confirmed signal that something needs to change, backed by evidence from both sides of the interaction.

AI and the Voice of Customer Program: Reactive Is No Longer Enough

Traditional VoC programs respond to problems after customers report them. The problem with that model is that by the time the feedback arrives, the experience has already happened. The frustration is already real. In some cases, the customer has already started evaluating alternatives.

AI-enabled VoC tools move the timeline.

NLP-powered sentiment analysis processes thousands of customer interactions simultaneously, identifying patterns that manual review would miss entirely. Predictive analytics models flag accounts showing behavioral signals of disengagement before those accounts submit a cancellation request, helping improve customer success. Real-time alerts surface friction at the moment it occurs rather than three weeks later when the survey batch gets processed.

The companies running predictive VoC programs aren’t just reacting faster. They’re operating in a different risk posture entirely. They catch reputational risks before they escalate. They identify product gaps before competitors exploit them. They recognize customer intent signals before those intentions become decisions.

The gap between reactive VoC and predictive VoC is the gap between managing the experience and designing it.

How Voice of Customer Connects Directly to Revenue

This is where VoC stops being a customer experience metric and starts being a business growth lever.

Forrester data puts it directly: customer-obsessed brands that act on VoC feedback consistently report revenue growth rates 41% higher than competitors who don’t. Customers who feel genuinely heard are 2.4 times more likely to stay with a brand, even after a negative experience, than customers who don’t.

Both of those numbers point to the same thing.

VoC isn’t a satisfaction function. It’s a retention function. And retention is a revenue function.

Every churn event that VoC data could have predicted but didn’t is margin walking out the door because replacing lost customers increases customer acquisition cost. Every product gap that customer feedback surfaced but nobody acted on is an expansion opportunity that went to a competitor instead.

The ROI case for VoC isn’t abstract. It’s in the delta between customer lifetime value at companies with mature listening programs and customer lifetime value at companies running surveys nobody reads.

Building a Culture That Actually Hears the Voice of Customer

Technology is the easy part. Culture is where VoC programs actually succeed or fail.

A program built on strong tools but weak organizational commitment produces the same outcome as no program at all. The data exists. The action doesn’t. Leadership treats the findings as interesting rather than directive. Teams acknowledge the feedback and continue doing what they were doing. The loop never closes.

The companies with mature VoC cultures share a few characteristics.

Leadership treats customer feedback as a primary input into strategy, not a secondary one. Findings connect explicitly to roadmap decisions, budget allocations, and hiring priorities that support long-term lead nurturing. Teams know which customer insights informed which changes, and they communicate that connection back to customers.

Closing the loop with customers, telling them their feedback changed something, is itself a retention signal. It tells the customer that the relationship is real, not performative.

VoC without that loop is just listening. VoC with it is a relationship.