All Those AI ‘Agents’ Are Going to Need Security Credentials

By Jafar Rizvi, CFA, Analyst and Portfolio Manager | July 22, 2025

One of the more immediate consequences of businesses and government entities increasingly trusting artificial intelligence (AI) with their data is security risk. An area of cybersecurity that may see greater demand because of AI is a niche known as identity and access management, which is Israel-based CyberArk’s specialty.

Humans who access an organization’s computer systems are assigned digital identities, which include their login credentials and define what parts of the system they have permission to access. Computer hardware and software also have digital identities—called “machine identities”—which must be protected. Securing human and machine identities involves managing various digital mechanisms, such as application programming interfaces (APIs), which are sets of rules that allow one piece of software to talk to another; digital certificates, which act like virtual identification cards with embedded signatures; and electronic keys that grant access to specific data or functions. CyberArk’s software platform helps organizations manage digital identities and monitor them for suspicious or unauthorized activity.

This is important because AI is creating a new class of machine identities in the form of autonomous and semi-autonomous AI “agents” capable of performing tasks and making decisions previously handled by humans. Already, the number of machine identities is on the rise: According to CyberArk, organizations on its platform had about 40 machine identities for every human identity last year—a ratio that has since doubled.

CyberArk is also poised to benefit from the proliferation of so-called containers and microservices widely used by AI developers. Containers package everything an app needs to run (code, tools, libraries), while microservices focus on discrete functions such as authentication. The company also developed a suite of AI-powered tools called CORA AI that helps its identity-security platform analyze user behavior, detect risky activity, generate audit reports, and provide its clients actionable insights. Management has been a good steward of capital, using the company’s strong balance sheet and free cash flow to invest in products and strategic partnerships that position CyberArk to meet the security challenges created by AI.

While much of the investment community has been focused on what large US companies are doing in AI, this technology may become a powerful equalizer, offering businesses of all sizes across geographies a chance to leapfrog the competition (or risk falling behind).

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Is AI a Threat to Google Search?

By Uday Cheruvu, CFA, Analyst and Portfolio Manager | June 18, 2025

For the past two decades, the primary way of looking up information on the internet has been to Google it. But what about in the future? Will we ChatGPT it? Claude it? Will some other brand of artificial intelligence (AI) develop into the neologism of the coming era?

As people have begun to use AI chatbots in their personal and professional lives, it has introduced the possibility that someday Google search could become obsolete. For instance, Apple executive Eddy Cue said last month that he sees AI applications eventually replacing standard search engines, citing an unusual decline in search activity on Apple’s Safari web browser. Therefore, he said, the iPhone maker is working to add AI search options to Safari, where Google has long been the default search tool. Cue’s comments, which came during testimony in a federal antitrust case against Google’s parent Alphabet, caused Alphabet’s stock to plunge over 7% on May 7.

Source: Datos, SparkToro.

It’s common for commercial searches to draw on Google’s vast amounts of real-time data—e.g., what pizza shops are currently open, how busy they are, and customer reviews. This is a key reason AI chatbots haven’t yet made a dent in commercial search. The datasets used to train AI models aren’t constantly updated, and so they still need to be connected to a search engine to provide the most accurate and relevant live data. For now, AI chatbots are generally better suited to non-commercial searches, such as informational searches, which contain fewer advertising opportunities—for example, “what temperature to bake a pizza.” (However, Morgan Stanley’s survey did find that ChatGPT is gaining ground in travel research, particularly among teens and young adults.)

Over time, these distinctions may become less relevant, especially if people become more habituated to the interaction style and output of AI chatbots. However, it’s not as if these chatbots are on the verge of eating Google’s lunch and Google is standing by doing nothing. Both Google and its parent company are helping lead the way in AI-powered search with their own large language model, Gemini. For example, there are now Gemini-generated summaries at the top of Google search results as well as an AI search mode that can respond to natural language prompts and more complex questions. The company is also incorporating AI image-recognition capabilities into its products. In fact, Alphabet is the only true full-stack AI player because it has proprietary hardware and infrastructure (including chips and data-center technology), software (such as the Android mobile operating system and Google Cloud networking software), one of the most advanced large language models, as well as AI products for users across its ecosystem. Amazon, Meta, or Microsoft can’t offer such a comprehensive set of products.

Although there’s the risk that Google’s AI features will cannibalize its search ads by discouraging clicks to other web pages, the company’s future success depends primarily on one thing: maintaining the utility of a Google search. That means being willing to disrupt its own business to address changing user habits. While the search engine started out as purely an intermediary connecting user intent with other web sites, increasingly Google is not an intermediary but the destination itself. A user can often get all the information they need right on the Google search results page. AI is now supercharging that strategy by giving people even less of a reason to click away. For example, when searching “what temperature to bake a pizza,” Google’s AI overview helpfully suggested different temperature settings depending on the thickness of the crust and advised preheating the oven first and watching for “a golden-brown crust and bubbly cheese”—eliminating the need to scroll through results or read recipes for detailed instructions.

Where Alphabet faces a potentially more serious and immediate challenge is its pending antitrust litigation. The company is currently defending itself in two major federal lawsuits, one targeting its search engine and the other its digital ad business. In the worst-case scenario, Alphabet could be forced to share its search index—the underpinning of the Google search engine—to competitors at a nominal cost. If that were to happen, the collapse of Google’s search dominance could be sudden. In the advertising-related case, the judge could rule that Alphabet needs to share its user data for free with advertisers and publishers. However, both of those outcomes would be extreme and seem highly unlikely.

As for AI, the jury’s still out. But it has been more than two years since ChatGPT first arrived, and there aren’t any signs of Google’s imminent demise. Instead, what’s become evident is that Alphabet has a lot of compelling AI technology, and that Google search remains well ahead of anything else.

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DeepSeek Rattles Markets But Not the Outlook for AI

January 31, 2025

A one-year-old artificial-intelligence (AI) startup born out of a Chinese hedge fund released a powerful AI model on January 20 that is challenging investors’ assumptions about the economics of building such systems.

R1, as the model is called, is an open-source, advanced-reasoning model—the kind that is designed to mimic the way humans think through problems. It was developed by DeepSeek, whose founder, Liang Wenfeng, reportedly accumulated 10,000 of NVIDIA’s graphics processing units (GPUs) while at his quantitative hedge fund, which relied on machine-learning investment strategies. The kicker: DeepSeek says it spent less than US$6 million to train the model that was used as a base for R1—a fraction of the billions of dollars that Western companies such as OpenAI have spent on their foundational models. This detail stunned the market and walloped the share prices of large tech companies and other parts of the burgeoning AI industry on January 27.

The knee-jerk reactions are a reminder that we are still in the early stages of a potential AI revolution, and that no matter the conviction some insiders and onlookers may seem to have, no one knows with certainty where the path will lead, let alone how many twists and turns the industry will encounter along the way. As more details become available, companies and investors will be able to better assess the broader implications of DeepSeek’s achievement, which may reveal that the initial market reaction was overdone in some cases. However, should DeepSeek’s claims that its methods lead to dramatic improvements in cost efficiency be substantiated, it may actually bode well for the adoption of AI tools over time.

Although the techniques employed by DeepSeek are not entirely new, the company’s execution is considered extraordinary for how it significantly reduced training and inference costs. The company reportedly not only used fewer and less-powerful chips but also trained its model in a matter of weeks. There is considerable debate regarding the accuracy of the roughly US$6 million training cost figure that has been reported as well as speculation that DeepSeek may have utilized rival models such as Meta Platforms’ Llama, Alibaba’s Qwen, and OpenAI’s GPT-4 in a process known as distillation to create a student model. Therefore, it may be that the true training cost of DeepSeek’s model is understated.

There is the risk that more efficient AI models may reduce the growth rate of demand for computing power and, in turn, the pace of investment in AI infrastructure. But while investors seem to have equated improved efficiency with a diminished need for AI infrastructure, the history of technology shows that better performance and lower costs typically lead to wider adoption and faster growth over time. AI could follow the same trajectory.

Among the likely beneficiaries of cheaper, more efficient AI are the software providers building applications on top of these models. As more users discover the advantages of AI, increased demand for user-facing software also helps the cloud-services companies that provide the computing power and data storage. However, the commoditization of large language models may negatively affect creators of these models, particularly those that have eschewed open-source technology.

Also, greater computing efficiency may lead to higher volumes of cloud data and thus the need for more data centers and electrical power over time. This would benefit infrastructure providers on the condition that higher demand more than offsets price declines.

The field of AI is still evolving, and further advances in the technology and the methods for developing it are likely. Innovation is always disruptive, but on the whole, DeepSeek’s breakthrough would seem to be a good sign for the industry.

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Salesforce, ServiceNow Benefit from Next Phase of the AI Wave

By Jingyi Li, Analyst and Portfolio Manager | January 14, 2025

By now, it is clear to most investors that the companies benefiting from the move to artificial intelligence (AI) include more than just a couple of chipmakers. One analogy for the increasing size and breadth of the AI industry is a tsunami: As the wave of corporate investment in AI builds, and the underlying hardware, foundational machine-learning models, and early-stage software applications all continue to improve, a broader set of tech providers has been able to benefit from the associated demand.

For example, Alphabet and Meta are among large cloud-services companies—also known as “hyperscalers”—that are building both the physical infrastructure and large-language models that are needed for AI technologies to be adopted by corporations more broadly. While the servers in their data centers continue to require powerful graphics processing units designed by NVIDIA and manufactured by TSMC, these hyperscalers are also designing their own custom chips, called application-specific integrated circuits (ASICs), which they are developing in partnership with chipmaker Broadcom, boosting that company’s growth outlook as well.

Advances in AI technology are also benefiting enterprise-software providers, as the rise of agentic AI in the fourth quarter brought the long-term promise of computers with human-like problem-solving capabilities into sharper focus. Agentic AI is capable of sophisticated reasoning and can automatically come up with ways to solve complex, multi-step problems—a significant step forward by models such as OpenAI’s o3 and Google’s Gemini 2 as compared to the more limited tasks performed by the previous generation of AI technology. Therefore, agentic AI is likely to be more broadly useful in business software, with companies such as Salesforce and ServiceNow well positioned to offer powerful tools based on this technology. At a recent event in San Francisco, Salesforce Chief Executive Officer Mark Benioff said the company is focused on its agentic-AI platform, Agentforce, adding, “The only thing we’re going to do at Salesforce is Agentforce.”

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Out of Our Minds

All Those AI ‘Agents’ Are Going to Need Security Credentials

Is AI a Threat to Google Search?

Source: Datos, SparkToro.

DeepSeek Rattles Markets But Not the Outlook for AI

Salesforce, ServiceNow Benefit from Next Phase of the AI Wave

Disclosures