Cloudflare

Cloudflare Stock Soars 17% as AI Agents Rewrite the Internet

Cloudflare Stock Soars 17% as AI Agents Rewrite the Internet

Cloudflare crushed Q2 earnings and raised its 2026 forecast.

Cloudflare just showed the rest of the tech market how to turn AI hype into hard cash.

The web infrastructure leader blew past Wall Street targets for its second quarter on Thursday. Cloudflare pulled in $696.1 million in revenue- a massive 36% jump YoY- while investors sent its shares surging over 17% in after-hours trading.

Management did not stop with a single quarter beat. The company has raised its full-year revenue guidance to nearly $2.87 billion.

The question is: Why did Cloudflare suddenly catch fire? Look at how internet traffic actually works today. Human beings do not make up all web activity anymore. AI answer engines, autonomous bots, and automated agents now scour the web every second. CEO Matthew Prince calls this transition a fundamental rewrite of the internet for machine-to-machine traffic.

AI agents demand instant response times, tight security, and processing power right at the network edge. Cloudflare runs servers in hundreds of cities worldwide, placing its infrastructure right next to end users and data streams. When developers build autonomous agents or AI-native apps, they naturally plug into Cloudflare’s Workers platform to speed up calculations.

Critics often complain about heavy infrastructure costs dragging down tech margins. Yet Cloudflare proves that positioning your servers directly in the path of AI traffic pays off handsomely. The company predicted the AI transition and built the tollbooth for the agentic web.

Cloudflare will keep collecting the toll as long as software bots keep navigating the internet on our behalf.

Sales Engagement Platforms

Leveraging the Sales Engagement Platforms for Pipeline Growth: A Guide

Leveraging the Sales Engagement Platforms for Pipeline Growth: A Guide

Sales engagement platforms promise pipeline. Most teams get automated noise instead. What precisely separates the platforms that move deals from the ones that merely move fast?

Every serious B2B sales team runs a sales engagement platform. That’s not a competitive advantage anymore. That’s the floor.

The reps at the company beating you this quarter aren’t using a better platform. They’re running better sequences, with better targeting, built on a clearer understanding of who they’re reaching and why. The platform is just the infrastructure. What runs on it is what actually matters.

Here’s the uncomfortable part.

Most teams treat the platform as the strategy. They buy Outreach, Salesloft, or Apollo, build a sequence library, and call it an outbound motion. The numbers look busy. Open rates, click rates, tasks completed per rep per day. Pipeline doesn’t move. The team blames the market. That usually happens because they never develop a structured outbound sales playbook before investing in technology.

The market isn’t the problem. The problem is that the platform got purchased before anyone figured out what it was supposed to do. This piece is about what sales engagement platforms are actually designed to accomplish, where most implementations go sideways, and what the teams extracting real value from them are doing differently.

What a Sales Engagement Platform Actually Does

A sales engagement platform coordinates outreach across multiple channels, automates repetitive execution, and tracks how prospects respond to each touch.

That’s the mechanical definition. The functional one is simpler: it removes the administrative work between a rep and a conversation. The rep shouldn’t be manually logging calls, remembering to follow up on a thread from two weeks ago, or copy-pasting the same email to thirty prospects with slightly different names. The platform handles that, allowing reps to focus on activities that directly improve sales performance. The rep handles the judgment calls, the relationship work, the conversations that actually require a human.

What a sales engagement platform doesn’t do is fix bad messaging, fix poor targeting, or replace a rep who doesn’t understand the buyer. These platforms amplify whatever motion you’ve already built.

A sharp, well-researched outbound process gets sharper and faster. A generic, spray-and-pray approach gets louder and more annoying. Same tool. Completely different outcomes.

That distinction is where most teams lose the plot.

Why Most Teams Run Their Sales Engagement Platform the Wrong Way

The Volume Trap That Sales Engagement Platforms Make Easy to Fall Into

Platforms make high-volume outreach easy. That’s a feature. Most teams turn it into a problem.

When sending 500 emails takes the same effort as sending 50, the instinct is to send 500. More touches, more coverage, more chances of hitting someone at the right moment. The logic sounds reasonable. The results rarely are.

Buyers receive more cold outreach now than at any point in B2B sales. Their tolerance for generic sequences has hit zero. An email that starts with “I noticed you recently…” gets deleted before the second sentence. Not because the rep isn’t trying. Because the buyer has seen that exact opening 70 times this month, which their pattern recognition filters out automatically.

Volume without precision doesn’t generate more pipeline. It generates more noise. And noise trains buyers to ignore you. A well-planned sales cadence is far more effective than simply increasing outreach volume. The rep who sends fifty well-researched, genuinely relevant messages consistently outperforms the rep sending five hundred templates. The platform makes both possible. The team culture and sequencing strategy determine which one actually happens.

The Personalization Problem Sales Engagement Platforms Create by Accident

Most sales engagement platforms include merge fields. First name. Company name. Industry. Job title. Teams use these and call it personalization.

It isn’t.

Swapping {{first_name}} for “Jordan” doesn’t make a generic email personal. It makes it a generic email with a name. Buyers read the rest of the message and still feel the impersonality. The merge field actually makes it worse because it creates an expectation of relevance that the content immediately fails to deliver.

Real personalization in a sales engagement platform context means the message references something specific to that account, that person, or that moment. Effective sales personalization relies on relevance rather than placeholders. A business trigger. A recent hire in a relevant role. A competitor move that creates a specific pressure point. Content they’ve engaged with. A problem pattern common in their segment that you’ve seen in similar accounts.

This level of specificity requires research. Most reps don’t do it for every prospect.

The teams that do it well build the research into the workflow, either through intent data integrations, enrichment tools feeding the CRM, or a focused approach that limits sequence volume to accounts worth the preparation time. Many also rely on B2B sales prospecting tools to improve research quality.

Core Features That Separate Good Sales Engagement Platforms from Expensive Ones

Not all platforms are built the same. The capabilities that separate the useful ones from the overpriced ones cluster around a few specific areas.

Multichannel Sequencing in Sales Engagement Platforms

Email alone doesn’t work. That’s not an opinion at this point. Response rates on cold email have been declining consistently for years. The teams still treating email as the only outreach channel aren’t running an outbound motion. They’re running a hope strategy.

Good sales engagement platforms coordinate sequences across email, phone, LinkedIn, direct mail, and increasingly SMS and video. A strong sales sequence combines these channels to keep outreach relevant instead of repetitive. The sequencing logic matters as much as the channels. Which touch comes first? How long between steps? When does a LinkedIn connection request land relative to the first email? When does a call make sense versus another digital touch?

The answers vary by ICP, by deal size, by industry.

A platform that lets the team build and test different sequence structures, track which configurations produce the most replies, and adjust based on actual data is doing the job. A platform that offers multichannel in name but makes cross-channel analytics difficult is selling a capability the team can’t fully use.

Sales Engagement Platform Analytics That Go Beyond Vanity Metrics

Open rates are a vanity metric. They tell you the subject line cleared the spam filter and the preview text was intriguing enough to click. They tell you nothing about whether the email generated a conversation worth having.

The analytics layer a sales engagement platform provides should answer specific operational questions. Which sequence step generates the most replies? Which personas respond to which messaging frames? These insights become more actionable when teams consistently track the right sales metrics. Which reps are booking meetings from which channel combinations? Where in the sequence do prospects typically go cold?

Those questions point to fixable problems. “Our open rate is 40%, but our reply rate is 2%” points to a body copy problem. “We see strong engagement through step three and then complete silence” points to either a timing problem or a step four message that kills the momentum the earlier touches built.

Teams that use platform analytics this way treat every sequence as a running experiment. They change one variable at a time. They track what shifts. Consistent sales analysis helps them turn those experiments into repeatable improvements. They build up a body of institutional knowledge about what their specific ICP responds to. That knowledge compounds. A sequence library built on six months of that discipline performs completely differently from one built on gut instinct.

How Sales Engagement Platforms Are Changing in 2026

AI’s Growing Role Inside Sales Engagement Platforms

Every major sales engagement platform now ships with AI capabilities. The growing role of AI reflects the broader evolution of sales teams across modern B2B organizations. The range of what that actually means is enormous.

At the shallow end, AI rewrites subject lines and suggests follow-up timing based on historical open patterns. Marginally useful. Nothing that changes how a team fundamentally operates.

At the meaningful end, AI is doing things that genuinely shift the workload.

Research summarization that surfaces the three most relevant account-level details a rep should reference before reaching out. Real-time reply analysis that classifies inbound responses and suggests the next best action. Sentiment tracking across an entire sequence that flags accounts showing frustration signals before they disengage completely. Sequence performance attribution that isolates which variables are driving results and which are neutral.

The platforms investing heavily in the meaningful end are changing the math on rep capacity. A rep who used to spend twenty minutes researching each account before a personalized outreach now gets that summary in two. They don’t drop the personalization. They drop the time cost of producing it. That’s a real efficiency gain that shows up in how many high-quality touches a rep can execute in a week.

What AI isn’t doing yet, and probably won’t for a while, is replacing the judgment call. Whether to call or email a specific prospect on a specific day. Whether an objection in a reply signals a stall or a genuine concern worth engaging. Whether an account that’s gone quiet is worth a breakup message or another value-add touch. Those calls still belong to the rep.

The platform feeds the decision. The rep makes it.

How to Choose a Sales Engagement Platform Without Getting Burned

The platform market is crowded, and the demo-to-reality gap is significant. What looks seamless in a vendor demo often runs into real-world friction the moment the team tries to use it at scale.

A few things to stress-test before signing anything.

CRM integration quality is the first filter. The platform and the CRM need to sync bidirectionally, in near real-time, without a rep having to manually reconcile activity logs. Choosing the right CRM solution is just as important as selecting the engagement platform itself. If the integration requires a middleware tool to function properly or if syncing breaks under high volume, the data quality problems that follow will undermine every downstream use case.

Reporting flexibility matters more than the number of reports available. A platform with forty pre-built dashboards but no ability to build custom views forces the team to answer questions the vendor anticipated rather than the ones the business actually has. Custom reporting, exportable data, and API access for teams that want to pull platform data into their own analytics stack are non-negotiable for any organization that takes performance seriously.

Ease of sequence building tells you something about the product philosophy. Platforms that make creating a new sequence a multi-step process with rigid structural requirements slow down iteration. The teams winning with these tools iterate quickly. A platform that makes rapid iteration difficult is working against the thing that drives results.

Adoption history in similar teams is the most honest signal available. Not case studies on the vendor’s website. Conversations with reps and ops leaders at companies with a similar GTM motion who’ve been running the platform for twelve-plus months. What broke that the demo didn’t show? What did they wish they’d known before switching? Those conversations save procurement decisions that look smart on paper and fail in practice.

What a Sales Engagement Platform Looks Like When It’s Working

Pipeline that’s predictable. Sequences that the team actively refines based on performance data. Reps who spend more time in conversations and less time on administrative tasks in between. That consistency leads to stronger sales pipeline analysis and more reliable forecasting. Analytics that surface specific problems rather than confirm things already visible from gut instinct.

The platform becomes invisible when it works well. Reps aren’t thinking about the tool. They’re thinking about the prospect. The coordination, the logging, the follow-up scheduling, the channel switching all run underneath the conversation without demanding conscious attention.

That’s the standard worth measuring against. Not which platform has the most features. Not which vendor produces the most impressive benchmark report. Whether the reps using it spend more time doing the part of the job that requires a human, and less time doing the part that doesn’t.

Most teams aren’t there yet. Not because the platforms aren’t capable of it. Because the sequencing strategy, training, ICP clarity, and analytics discipline required to get there didn’t get built alongside the platform subscription. A comprehensive sales enablement strategy is what ties these elements together.

The platform is the infrastructure. Everything it runs on is the work.

OpenAI

OpenAI Fires Back at Apple Stating “Dismiss the Suit, Fix Your AI First” Amid Legal Tensions

OpenAI Fires Back at Apple Stating “Dismiss the Suit, Fix Your AI First” Amid Legal Tensions

OpenAI asks a federal judge to toss Apple’s trade secrets lawsuit, calling the claims a distraction from Apple’s own AI struggles.

Tech legal battles rarely get this blunt. OpenAI just asked a federal judge to throw out Apple’s high-stakes trade secrets lawsuit, and it did not hold back.

Apple sued OpenAI last month, accusing the ChatGPT creator and two former Apple employees of stealing hardware secrets to build consumer AI devices. But OpenAI called the lawsuit “baseless,” “pretextual,” and “rotten to its core” in a sharp motion to dismiss.

OpenAI argues that Apple uses litigation to cloak its own talent drain and slow AI development.

Top engineers leave Apple because they want to build cutting-edge tech at OpenAI. In its filing, OpenAI made its stance clear: it has no desire for Apple’s trade secrets because it is building something entirely new.

To make matters worse for Cupertino, OpenAI exposed basic clerical blunders by Apple’s legal team. Apple claimed OpenAI ignored early outreach, but records show Apple’s outside lawyers emailed the wrong person after confusing two similar last names. They even cited a phone call with OpenAI’s general counsel that never actually happened.

Apple has reason to feel nervous. As OpenAI hires veteran hardware leaders like Tang Tan, it threatens the iPhone’s dominance over future AI hardware. Yet weaponizing trade secret law to stop employee departures sets a dangerous precedent for Silicon Valley talent mobility.

Apple needs to build better AI products and give top talent a reason to stay rather than dragging former employees through court, as OpenAI does. The reality is competition drives innovation, but both tech giants should compete on product merit rather than courtroom maneuvers.

SpaceX

SpaceX Eyeing AT&T, Verizon, and T-Mobile, Plans to Become a Telecom Giant

SpaceX Eyeing AT&T, Verizon, and T-Mobile, Plans to Become a Telecom Giant

SpaceX has unveiled plans to build a hybrid mobile network and take over the telecom market.

SpaceX isn’t satisfied with launching rockets and powering rural internet. The company now wants to become your primary cell phone carrier.

During SpaceX’s first earnings call as a public company, President Gwynne Shotwell and CEO Elon Musk unveiled plans for a full-scale mobile network. They plan to overtake AT&T, Verizon, and T-Mobile- a trio that generates $600 billion in annual revenue.

SpaceX has recently spent $19.6 billion to acquire 65 MHz of wireless spectrum from EchoStar. SpaceX planned a clever shortcut rather than building thousands of expensive cell towers. Engineers will mount small cellular base stations directly onto millions of existing Starlink dishes across the country.

Combining those rooftop stations with next-generation Starlink V2 satellites creates a fast hybrid network. Ground units handle heavy data traffic, while orbiting satellites plug remaining dead zones. Shotwell claims this architecture will deliver service up to 100 times better than current direct-to-cell safety features.

Skeptics point out that 65 MHz is a fraction of the spectrum legacy carriers hold. Yet SpaceX holds a unique hardware advantage. Traditional carriers spend billions building and leasing tall towers, but SpaceX has an advantage- it already owns millions of dish mounts on rooftops with clear views of the sky.

This ambitious pivot transforms Starlink from a rural utility into a primary telecom threat. Legacy carriers should worry. SpaceX is turning its satellite footprint into the ultimate ground-to-space cellular grid.

Snap Ads

The Snap Ads MCP Server Is Now Live: Who Checks the Inference?

The Snap Ads MCP Server Is Now Live: Who Checks the Inference?

Snap has turned campaign reporting into a conversation. MCPs could change the analyst’s job, but they cannot make the model’s conclusions true.

What has Snap launched?

On August 3, 2026, Snap launched an official, Snap-hosted MCP server that connects the Snap Ads API to Claude, ChatGPT and Gemini.

An advertiser can now ask an AI tool to summarize the last seven days of performance, identify the campaigns that changed most week over week, surface diagnostics or find patterns across the last 90 days. No exporting reports. No preparing spreadsheets. No writing API queries.

At launch, the connection is read-only. An Organization Admin must approve each AI agent, every user must authorize access individually, and the agent cannot see more than that user can already access. Snap says write capabilities are coming later, and admins will be able to decide which agents are allowed to act.

That caution matters. It is easier to let a model read a budget than to let it move one.

ChatGPT Ads will make this market bigger

ChatGPT’s advertising business strengthens the argument. In May, OpenAI expanded its ads pilot with a self-serve Ads Manager, CPC bidding, technology partners and conversion measurement. Its current advertiser documentation lists impressions, clicks, spend, CTR, average CPC, average CPM and conversions. It also supports pixel-based measurement, a Conversions API and UTM parameters for outside analytics tools.

So ChatGPT Ads is not waiting for tracking. The measurement layer already exists.

There is an important privacy distinction, too. OpenAI says advertisers receive aggregated performance information—not access to individual conversations. “Tracking” here should not be confused with handing a brand someone’s chat history.

OpenAI has also published an Ads API covering campaigns, ads, product feeds, conversions and insights. It has not announced a first-party Ads MCP—at least not yet. But once an API exists and MCP clients are widespread, third-party wrappers, cross-platform reporting agents and specialist optimization tools become a very predictable market.

ChatGPT is therefore an accelerant, not the original cause. The deeper shift is that media buying is moving from dashboards into conversations. Every platform will want its data inside the agent a marketer already uses, and every agent will want enough advertising data to compare channels. The commercial pressure runs both ways.

The analyst gets faster and more necessary

This is where the change becomes two-pronged.

The easy part of analysis is about to become much easier. Finding spend, ROAS, CTR, delivery problems or week-over-week movement can happen through one question. An analyst will spend less time locating the number, cleaning the export and moving it into another tool.

But finding a number is not the same as knowing what it means.

“Which campaign changed most?” is a descriptive question. “Which creative caused the lift?” is a causal one. The first can be answered from a table. The second requires assumptions about attribution, audience overlap, seasonality, budget changes, the counterfactual and sometimes an actual experiment.

MCP removes data friction. It does not remove epistemic friction.

Model bias is a valid concern, but it is only part of the problem. NIST’s Generative AI Risk Profile identifies harmful bias, confident falsehoods and human over-reliance as separate risks. A model can choose the wrong date range, accept a platform’s attribution model without questioning it, aggregate away an important segment or turn a correlation into a causal story. Then it can state that story beautifully.

That fluency is the danger. It can launder a chain of hidden analytical choices into one confident paragraph.

The research supports the concern. InfiAgent-DABench, a benchmark built around end-to-end CSV analysis, found that state-of-the-art models still struggled with data-analysis tasks. The gap becomes larger when the job moves from calculation to causality. In the 2025 CauSciBench preprint, the best-performing configuration—OpenAI o3 with chain-of-thought prompting—still recorded a 48.96% mean relative error on causal-analysis problems derived from real research papers.

Ad analysis is not scientific research. But the lesson transfers: access to the data is not proof that the selected method or interpretation is correct.

The analyst of the future will therefore do less fetching and more auditing:

  • Ask the agent to show the source query, account, time window and metric definition.
  • Separate what the data observed from what the model inferred.
  • Demand alternative explanations, not just the most fluent one.
  • Test causal claims with A/B tests or lift studies where possible.
  • Keep budget and campaign changes behind human approval.

This is why Snap’s decision to begin with read-only access is the correct order of operations. First make the data conversational. Then build the controls required before the model can act.

MCP makes search cheap. It does not make inference true.

The analyst is not disappearing. The job is moving from finding the number to questioning assumptions that now seem validated.

Merchant

India to Reintroduce Merchant Fees on UPI

India to Reintroduce Merchant Fees on UPI

India is paving the way to bring back merchant fees on large UPI transactions. Will charging big retailers make the world’s best payment network stronger?

India built the world’s undisputed king of real-time payments by making it completely free. The government is now quietly paving the way to bring merchant fees back into the ecosystem. And honestly, it’s about time.

According to a recent report, Indian authorities are opening the door for banks and fintech companies to charge a small Merchant Discount Rate (MDR) on UPI and RuPay transactions over ₹2,000 ($24) at large merchants. Small vendors and everyday peer-to-peer transfers will remain strictly free.

The zero-cost model worked like magic when the government scrapped MDR in 2020. UPI volume exploded into tens of billions of transactions every month- converting a cash-heavy country into a digital powerhouse overnight.

Yet zero fees created an invisible, dangerous problem behind the scenes.

Payment apps and banks spent billions building servers, fighting fraud, and processing transactions, while government subsidies covered barely 11% of their actual operating costs. You cannot run world-class financial infrastructure on goodwill and pocket change forever.

By targeting only large retailers on higher-value transactions- likely a tiny 0.05% to 0.07% fee- the government hits the absolute sweet spot. Everyday shoppers scan QR codes for tea and groceries without paying a single extra rupee. Small neighborhood shops keep every bit of their earnings. But payment giants such as PhonePe and Razorpay gain a sustainable revenue model to improve security, faster processing, and innovate.

This policy change does not signal a retreat from digital payments.

It rather shows a mature market growing up. India proved that free payments can ignite a digital revolution. Now, it is proving that a fair, sustainable business model keeps that revolution running smoothly for decades to come.