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.

What did you think of this piece?

The Sticky Business of Smart Labels: Avery Dennison Finds New Sources of Growth

By Safia Williams, CFA, Associate Analyst | June 04, 2025

In the grocery business, some of the biggest challenges exist in the bakery department. Baked goods have a short shelf life, and stores have traditionally relied on inefficient methods to track “sell by” dates and manually apply reduced-priced stickers to packages as they near expiration. That’s why Kroger recently began testing labels embedded with radio-frequency identification (RFID) technology in its bakery sections. By wirelessly tracking this inventory, a supermarket can better manage food freshness and adjust prices without assigning workers to re-tag products.

The company behind Kroger’s RFID pilot program is Avery Dennison, which might surprise investors who are more familiar with its low-tech products such as mailing labels. Before Stan Avery invented self-adhesive labels, or stickers, nearly a century ago, labels had to be moistened with water to make them stick. Avery mailing labels later became a staple of office-supplies stores. Although the company sold its office- and consumer-products division to CCL Industries in 2013, adhesive technology remains the core of its business, used in everything from shampoo- and wine-bottle labels to barcode stickers, shipping labels, reflective films for traffic signage, customized apparel tags—and RFID labels.

Avery Dennison has been in the business of RFID technology since the early 2000s, but the technology didn’t immediately take off due to its high costs and a lack of readily available software to manage RFID data. In 2000, each RFID tag cost more than US$1, compared to 15 cents a decade ago and just 5 cents today. As technological advances and manufacturing scale brought down those costs, prominent retailers such as Macy’s and Marks and Spencer began using RFID-tagging systems, and it wasn’t until they demonstrated the benefits that other chains followed suit. In perhaps the most cutting-edge example, Avery Dennison customer Uniqlo uses RFID tags for all its merchandise, which has enabled the companies to create a touchless self-checkout system that doesn’t require the scanning of individual barcodes. Today, RFID labels generate just 10% of Avery Dennison’s total revenue, but sales of these products are growing significantly faster than the rest of the company’s businesses while also earning superior margins.

According to the company’s estimates, RFID technology has penetrated about 40% of the apparel market, where there are roughly 45 billion taggable units. The larger growth opportunity is in food, where there are over four times as many potential taggable units but very few companies are using RFID systems. In the past, an obstacle to using RFID labels for food was that the chips wouldn’t work under varying temperature and humidity conditions, preventing tagged products from being refrigerated or microwaved. Avery Dennison has since developed solutions to these problems, unlocking a new market.

As it pursues that growth opportunity, the company’s scale and vertical integration in RFID manufacturing are key competitive advantages. Inside an RFID tag are inlay components such as an antenna, microchip, and substrate to hold them together; the company has a 50% share of the RFID inlay industry and more than 1,500 patents to protect the design of its antenna and other tag properties. Its strength in this area has also supported continued investment in R&D to sustain its market leadership over the long run, such as developing designs suitable for customers in other markets, including logistics and health care.

Kroger’s initiative is important because, like the apparel industry, widespread use of RFID technology in the grocery industry will likely hinge on a few success stories among the big chains. The cost of RFID tags is also a key metric that investors should monitor; as that comes down, Avery Dennison can make a more compelling case to customers that the money saved from reducing their food waste and labor needs is worth the investment in an RFID system.

If Avery Dennison’s project for Kroger is successful, and the cost of the tags continues to fall, Kroger may be just the first of many grocers to switch to this method of tracking their perishables.

What did you think of this piece?

For Companies, the Tariff Question Folds Back Into Competitive Advantage

By Sean Contant, CFA, Analyst, and Sergei Pliutsinski, Analyst | May 21, 2025

For investors trying to discern the effects of the Trump administration’s tariff policies on their stocks, there is a quick-and-dirty way to perform their analysis: Look at the countries in which a company manufactures its products, look at the tariffs applied to that country, multiply the top line by the tariff rate, and subtract that from gross profits.

In doing that, investors are assessing the first-order (direct impact on companies) and second-order (impact on demand) effects of the tariffs. That is what investors seemed to do in the wake of Trump’s April 2 announcement that the US would apply tariffs to virtually every country in the world. Shares of sporting-goods manufacturer VF Corporation, which has substantial operations in China and Vietnam, fell 41%. Hong Kong-based power-tools maker Techtronic Industries fell 23%. Polaris, which makes sports vehicles and has its largest factory in Mexico, fell 25%. French electrical-equipment manufacturer Schneider Electric fell 11%.

But the best indicator of how the tariffs will affect corporate profits comes from looking not at the first- or second-order effect, but at a third-order effect: The effect on companies’ competitive position. Companies don’t operate in a vacuum. They compete against each other. Yes, every company doing business in the US has to contend with the tariff issue. But some companies are better equipped to do so than others just by the nature of how their operations and supply chains have been set up. That is where the competitive advantages will become apparent. A foreign-domiciled company may not be at any disadvantage to a US-based one, and vice versa.

This is how we evaluate companies. The cornerstone of our analysis is the competitive-forces framework designed by Harvard University professor Michael Porter. Competitive strategy, as the field has come to be called, looks not just at a company’s internal fundamentals, but also compares them to its peers across its industry.

Consider Schneider and its competitor Eaton Corp. Schneider Electric makes equipment for electricity distribution and industrial controls and is based in Rueil-Malmaison, France. Eaton, which also makes equipment for electricity distribution, has its operations based in Ohio. Ostensibly, that would give Eaton a leg up over Schneider. But these companies have virtually the same manufacturing footprint and supply chain despite the different domiciles. Schneider has about 20 factories in the US. So it isn’t likely to lose any ground to Eaton, at least not due to tariffs.

An even better example is Techtronic and its competitor Stanley Black & Decker. Techtronic is based in Hong Kong and owns the Ryobi and Milwaukee brands. It was a pioneer in battery-powered, cordless power tools. Stanley is based in Connecticut and makes the Black & Decker, Craftsman, and DeWalt brands. Again, one company is based in the US and the other is based overseas. But both companies’ manufacturing operations are based overseas so the real issue is where overseas they are.

Stanley Black & Decker has about 24% of its power-tools manufacturing capacity in Mexico, and 18% in China. Techtronic also has production in Mexico and China. But it is already in the process of moving its China production out of the country, which it plans to complete this year, and importantly already has a substantial portion of its power-tools manufacturing in Vietnam. Stanley is also planning to move production out of China, but is further behind in that process. It will likely take one to two years to complete. Currently, Vietnam’s tariffs are at 10% while Mexico is at 25% for non-USMCA-compliant goods and China’s now at 30%. Therefore, Techtronic has an advantage in already having production in Vietnam and in being further along in moving its production out of China. The advantage could of course shift if the tariffs increase or decrease, but the analytical framework remains in place.

Looking at the tariff question this way doesn’t mean a company won’t be affected by them. The economic consensus is that the steep tariffs will hurt everybody. But examining that potential pain through the lens of competitive advantage provides a clearer picture of how companies will be affected, and which ones will come out stronger.

What did you think of this piece?

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.

What did you think of this piece?

Out of Our Minds

Is AI a Threat to Google Search?

Source: Datos, SparkToro.

The Sticky Business of Smart Labels: Avery Dennison Finds New Sources of Growth

For Companies, the Tariff Question Folds Back Into Competitive Advantage

DeepSeek Rattles Markets But Not the Outlook for AI

Disclosures