Category: 3. Business

By Christine Ji

Surging demand for Cisco’s AI networking equipment and a ‘multi-billion-dollar’ refresh in its legacy business sparked upbeat results and guidance

Cisco reported $1.3 billion in AI-infrastructure orders from hyperscaler customers for the first quarter.

Cisco Systems Inc. on Wednesday delivered a strong earnings beat for its October quarter, thanks to continuing artificial-intelligence demand for its products, as well as an upgrade cycle in its legacy business.

Shares of Cisco (CSCO) jumped 7.5% in after-hours trading, as the company’s outlook also topped expectations.

The latest results showed that the provider of networking equipment and services grew revenue by 8% in its fiscal first quarter, to $14.9 billion. That exceeded FactSet consensus estimates for $14.8 billion.

Cisco has taken advantage of the AI boom with its custom-designed Silicon One chips, which power the company’s advanced routers and switches. Increased demand from “Magnificent Seven” AI-infrastructure builders, neoclouds and sovereign clouds have all led to incremental AI revenue.

The company reported that its networking product orders grew at a double-digit rate for the fifth consecutive quarter, and AI infrastructure orders from hyperscaler customers totaled $1.3 billion.

Adjusted earnings for the quarter were $1 a share, beating consensus estimates of 98 cents a share and marking a 10% year-over-year increase. Cisco also returned $3.6 billion to shareholders through $2 billion in stock buybacks and $1.6 billion in dividends. The company declared a quarterly dividend of 41 cents.

“We had a solid start to fiscal 2026, and Cisco is on track to deliver our strongest year yet,” Chuck Robbins, the company’s chair and CEO, said in a statement. “The widespread demand for our technologies highlights the critical role of secure networking and the value of our portfolio as customers move quickly to unlock the potential of AI.”

For the current quarter, Cisco guided for revenue of between $15 billion and $15.2 billion, well above the $14.6 billion consensus view. For the full year, Cisco anticipates revenue between $60.2 billion and $61.0 billion, exceeding Wall Street analyst expectations of $59.6 billion.

The strong quarter could prove to be a fresh catalyst for the stock. Shares of Cisco have rallied 25% year to date, but the stock has only risen 5% since Cisco last reported on Aug. 13.

Although the company has been a beneficiary of the AI-infrastructure boom, Citi analyst Atif Malik expressed skepticism prior to the report, writing in a note last week that there are “intensifying competitive pressures in switching and routing.” Malik still rated Cisco’s stock a buy, with a price target of $80.

Meanwhile, UBS analyst David Vogt upgraded the stock to buy from neutral and increased his price target to $88 from $74 last week. Not only does Cisco face surging AI infrastructure demand, but Vogt also saw catalysts in an upcoming cycle of office network upgrades and momentum in the security business.

Vogt’s predictions were proved right on Wednesday, as Cisco reported that a “major multi-year, multi-billion-dollar campus networking refresh cycle” is under way, with all campus networking technologies seeing accelerated order growth in the first quarter.

Also read: 10 stocks that let you invest like Nvidia in the next hot AI trade

-Christine Ji

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  11-12-25 2035ET
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Leveraging Thunes’ trusted Direct Global Network, the collaboration will deliver secure, real-time cross-border payments for millions of Mashreq customers.

SINGAPORE and DUBAI, UAE, Nov. 12, 2025 /PRNewswire/ — Thunes, the Smart Superhighway to move money around the world, announces a powerful new collaboration with Mashreq, a leading financial institution in the MENA region, to launch real-time mobile wallet payouts for millions of Mashreq customers.

Powered by Thunes’ trusted Direct Global Network, the partnership will enable Mashreq to connect directly to mobile wallets across Asia, Africa, and Europe and deliver faster, more affordable international transfers across key growth markets.

As Mashreq continues to expand across the Middle East and North Africa, this alliance marks a major step in transforming cross-border payments. Initially covering the bank’s top 30 payment corridors, the service will extend to additional countries over time. Together, Thunes and Mashreq are advancing digital banking innovation and making global money movement more seamless and inclusive.

As the world’s second-largest remittance hub, the UAE is expected to processUSD 47 billion in outward flows in 2025, a figure which is set to grow by 4.7% annually. By joining forces with Thunes, Mashreq is supporting this growth and paving the way for fast, transparent, and efficient cross-border payments.

Kartik Taneja , Head of Payments & Consumer Lending at Mashreq, said: “By expanding our reach to mobile wallets through Thunes’ trusted Direct Global Network, we’re empowering millions of people to send funds to around 45 new destinations instantly and more affordably than ever before. With mobile wallet users projected to exceed five billion globally in the next few years, this capability is critical to serving our customers in key growth markets and driving greater financial connectivity. We are redefining what’s possible in international payments, and Mashreq is proud to be leading this transformation.”

Simon Nelson , Chief Commercial Officer at Thunes, added: “We are delighted to welcome Mashreq as a Member of our Direct Global Network. This alliance further strengthens our position in the Middle East, amplifying our mission to enable more consumers to take part in the global economy through instant, borderless payments. Trusted by leading banks and financial institutions worldwide, Thunes provides the secure and reliable Network needed to move money with confidence. Together with Mashreq, we’re enabling a new wave of payment innovation and connectivity that will reshape how money moves worldwide.”

ENDS

Contact:

WE Communications, [email protected] 

Anna Birdsall-Strong, Director of Brand and Public Relations, [email protected] 

SOURCE Thunes

Kathryn Huberty: Welcome to Thoughts on the Market. I’m Katy Huberty, Morgan Stanley’s Global Head of Research, and I’m joined by Stephen Byrd, Global Head of Thematic Research, and Jeff McMillan, Morgan Stanley’s Head of Firm-Wide AI.

 

Today and tomorrow, we have a special two-part episode on the number one question everyone is asking us: What does the future of work look like as we scale AI?

 

It’s Tuesday, November 4th at 10am in New York.

 

I wanted to talk to you both because Stephen, your groundbreaking work provides a foundation for thinking through labor and economic impacts of implementing AI across industries. And Jeff, you’re leading Morgan Stanley’s efforts to implement AI across our more than 80,000 employee firm, requiring critical change management to unlock the full value of this technology.

 

Let’s start big picture and look at this from the industry level, and then tomorrow we’ll dig into how AI is changing the nature of work for individuals. 

 

Stephen, one of the big questions in the news – and from investors – is the size of AI adoption opportunity in terms of earnings potential for S&P 500 companies and the economy as a whole. What’s the headline takeaway from your analysis?

 

Stephen Byrd: Yeah, this is the most popular topic with my children when we talk about the work that I do. And the impacts are so broad. So, let’s start with the headline numbers. We did a deep dive into the S&P 500 in terms of AI adoption benefits. The net benefits based on where the technology is now, would be about little over $900 billion. And that can translate to well over 20 percent increased earnings power that could generate over $13 trillion of market cap upon adoption. And importantly, that’s where the technology is now.

 

So, what’s so interesting to me is the technology is evolving very, very quickly. We’ve been writing a lot about the nonlinear rate of improvement of AI. And what’s especially exciting right now is a number of the American labs, the well-known companies developing these LLMs, are now gathering about 10 times the computational power to train their next model. If scaling laws hold that would result in models that are about twice as capable as they are today. So, I think 2026 is going to be a year in terms of thinking about where we’re headed in terms of adoption. So, it’s frankly challenging to basically take a snapshot because the picture is moving so quickly.

 

Kathryn Huberty: Stephen, you referenced just the fast pace of change and the daily news flow. What’s the view of the timeline here? Are we measuring progress at the industry level in months, in years?

 

Stephen Byrd: It’s definitely in years. It’s fast and slow. Slow in the sense that, you know, it’s taken some companies a little while now and some over a year to really prepare. But now what we’re seeing in our CIO survey is many companies are now moving into the first, I’d say, full fledged adoption of AI, when you can start to really see this in numbers.

 

So, it sort of starts with a trickle, but then in 2026, it really turns into something much, much bigger. And then I go back to this point about non-linear improvement. So, what looks like, areas where AI cannot perform a task six months from now will look very different. And I think – I’m a former lawyer myself. In the field of law, for example, this has changed so quickly as to what, AI can actually do. So, what I expect is it starts slow and then suddenly we look at a wide variety of tasks and AI is fairly suddenly able to do a lot more than we expect.

 

Kathryn Huberty: Which industries are likely to be most impacted by the shift? And when you broke down the analysis to the industry and job level, what were some of the surprises?

 

Stephen Byrd: I thought what we would see would be fairly high-tech oriented sectors – and including our own – would be top of the list. What I found was very different. So, think instead of sectors where there’s fairly low profit per employee, often low margin businesses, very labor-intensive businesses. A number of areas in healthcare staples came to the top. A few real [00:04:00] estate management businesses. So, very different than I expected.

 

The very high-tech sectors actually had some of the lowest numbers, simply because those companies in high-tech tend to have extremely high profit per employee. So, the impact is a lot less. So that was surprising learning. A lot of clients have been digging into that.

 

Kathryn Huberty: I could see why that would’ve surprised you. But let’s focus on banking for a moment since we have the expert here. Jeff, what are some of the most exciting AI use cases in banking right now?

 

Jeff McMillan: You know, I would start with software development, which was probably the first Gen AI use case out of the gate. And not only was it first, but it continues to be the most rapidly advancing. And that’s probably, mostly a function of the software, you know, development community. I mean, these are developers that are constantly fiddling and making the technology better.

 

But productivity continues to advance at a linear pace. You know, we have over 20,000 folks here at Morgan Stanley. That’s 25 percent of our population. We have more people building software than we have financial advisors. And, you know, the impact both in terms of the size of that population and the efficiencies are really, really significant.

 

So, I would start there. And then, you know, once you start moving past that, it may not seem, you know, sexy. It’s really powerful around things like document processing. Financial services firms move massive amounts of paper. We take paper in, whether it be an account opening, whether it be a contract. Somebody reads that information, they reason about it, and then they type that information into a system. AI is really purpose built for that.

 

And then finally, just document generation. I mean, the number of presentations, portfolio reviews, you know, even in your world, Katy, research reports that we create. Once again, AI is really just – it’s right down the middle in terms of its ability to generate just content and help people reduce the time and effort to do that.

 

Kathryn Huberty: There’s a lot of excitement around AI, but as Stephen mentioned, it’s not a linear path. What are the biggest challenges, Jeff, to AI adoption for a big global enterprise like Morgan Stanley? What keeps you up at night?

 

Jeff McMillan: I’ve often made the analogy that we own a Ferrari and we’re driving around circles in a parking lot. And what I mean by that is that the technology has so far advanced beyond our own capacity to leverage it. And the biggest issue is – it’s our own capacity and awareness and education.

 

So, you know what keeps me up at night? it’s the firm’s understanding. It’s each person’s and each leader’s ability to understand what this technology can do. Candidly, it’s the basics of prompting. We spend a lot of time here at the firm just teaching people how to prompt, understanding how to speak to the machine because until you know how to do that, you don’t really understand the art of the possible. I tell people, if you have $100 to spend, you should start spending [$]90, on educating your employee base. Because until you do that, you cannot effectively get the best out of the technology.

 

Kathryn Huberty: And as we look out to 2026, what AI trends are you watching closely and how are we preparing the firm to take advantage of that?

 

Jeff McMillan: You and I were just out in Silicon Valley a couple of weeks ago, and seemingly overnight, every firm has become an agentic one. While much of that is aspirational, I think it’s actually going to be, in the long term, a true narrative, right?

 

We’ve already built several agents ourselves. And what I would describe them as true agents – ones that actually are able to plan and act and reason on their own and execute tasks, multi-threaded. With humans still in the loop but are able to do more than just respond to a question. And we’re starting to scale. And I think that step where we are right now is really about experimentation, right? I think we have to learn which tools work, what new governance processes we need to put in place, where the lines are drawn. I think we’re still in the early stage, but we’re leaning in really hard.

 

We’ve got about 20 use cases that we’re experimenting with right now. As things settle down and the vendor landscape really starts to pan out, we’ll be down position to fully take advantage of that.

Kathryn Huberty: A key element of the agentic solutions is linking to the data, the tools, the application that we use every day in our workflow. And that ecosystem is developing, and it feels that we’re now on the cusp of those agentic workflow applications taking hold.

 

Stephen Byrd: So, Katy, I want to jump in here and ask you a question too. With your own background as an IT hardware analyst, how does the AI era compare to past tech or computing cycles? And what sort of lessons from those cycles shape your view of the opportunities and challenges ahead?

 

Kathryn Huberty: The other big question in the market right now is whether an AI bubble is forming. You hear that in the press. It’s one of the questions all three of us are hearing regularly from clients. And implicit in that question is a view that this doesn’t look like past cycles, past trends. And I just don’t believe that to be the case.

 

We actually see the development of AI following a very similar path. If you go back to mainframe and then minicomputer, the PC, internet, mobile, cloud, and now AI. Each compute cycle is roughly 10 times larger in terms of the amount of installed compute.

 

The reality is we’ve gone from millions to billions to trillions, and so it feels very different. But the reality is we have a trillion dollars of installed CPU compute, and that means we likely need $10 trillion of installed GPU compute. And so, we are following the same pattern. Yes, the numbers are bigger because we keep 10x-ing, but the pattern is the same. And so again, that tells us we’re in the early innings. You know, we’re still at the point of the semiconductor technology shipping out into infrastructure. The applications will come.

 

The other pattern from past cycles is that exponential growth is really difficult for humans to model. So, I think back to the early days when Morgan Stanley’s technology team was really bullish, laying the groundwork for the PC era, the internet era, the mobile era. When we go back and look at our forecasts, we always underestimated the potential. And so that would suggest that what we’ve seen with the upward earnings revisions for the AI enablers and soon the AI adopters is likely to continue.

 

And so, I see many patterns, you know, that are thread across computing cycles, and I would just encourage investors to realize that AI so far is following similar patterns.

 

Jeff McMillan: Katy, you make the point that much of the playbook is the same. But is there anything fundamentally different about the AI cycle that investors should be thinking about?

 

Kathryn Huberty: The breadth of impact to industries and corporates, which speaks to Stephen’s work. We have now four times over mapped the 3,700 companies globally that Morgan Stanley research covers to understand their role in this theme.

 

Are they enabling AI? Are they adopting? Are they disrupted by it? How important is it to the thesis? Do they have pricing power? It’s very valuable data to go and capture the alpha. But I was looking at that dataset recently and a third of those nearly 4,000 companies we cover, our analysts are saying that AI has an impact on the investment thesis. A third. And yet we’re still in the early innings. And so, what may be different, and make the impact much bigger and broader is just the sheer number of corporations that will be impacted by the theme.

 

Let’s pause here and pick up tomorrow with more on workforce transformation and the impact on individual workers.

 

Thank you to our listeners. Please join us tomorrow for part two of our conversation. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

 

Before the sun comes up, and before his shift even starts, Devin Samaha is already working up a sweat—scaling the stairs of Chevron’s Houston, Texas, headquarters, sometimes with a sandbag slung over his shoulder.

But for him, this isn’t just a workout. It’s a ritual rooted in his Marine Corps days.

“Exercise is how I stay sharp,” said Samaha, an exploration drilling engineer.

Samaha added that his military service instilled an unwavering sense of integrity, a relentless drive for perfection, and an enthusiastic commitment to any mission, which he brings to every task and opportunity at Chevron.

“The discipline, adaptability and mission-focused leadership I honed in uniform—combined with a deep sense of camaraderie—enable me to build strong, collaborative teams that prioritize safety and deliver projects with precision under pressure,” Samaha said. “The problem-solving skills, resilience and enthusiasm for new challenges forged in high-stress environments drive innovative solutions, foster a supportive workplace culture and ensure the highest standards of safety and excellence.”