A post on LinkedIn questioned the idea that Schema.org structured data has an impact on what a large language model outputs. Apparently there are some SEOs who are recommending structured data to rank better in AI search engines.
Patrick Stox wrote the following post on LinkedIn:
“Did I miss something? Why do SEOs think schema markup will impact LLM output?”
Patrick said “LLM output” in the context of an SEO recommendation so it’s likely that it’s a reference to ChatGPT Search and other AI search engines. So do AI search engines get their data from structured data?
LLMs are trained on web text, books, government records, legal documents and other text data (as well as other forms of media, too) which is then used to produce summaries and answers but without plagiarizing the training data. What that means is that it’s pointless to think that optimizing your web content will result in the LLM itself sending referrals to that website.
AI search engines are grounded on search indexes (and knowledge graphs) through Retrieval Augmented Generation (RAG). Search engine indexes themselves are created from crawled data, not Schema structured data.
Perplexity AI ranks web-crawled content using a modified version of PageRank on their search index, for example. Google and Bing crawl text data and do things like remove duplicate content, remove stop words, and other manipulation of the text extracted from the HTML, plus not every page has structured data on it.
In fact, Google only uses a fraction of the available Schema.org structured data for specific kinds of search experiences and rich results, which in turn limits the kind of structured data that publishers use.
Then there’s the fact that both Bing and Google’s crawlers render the HTML, identify the headers, footers and main content (from which they extract the text for ranking purposes). Why would they do that if they’re going to rely on Schema structured data, right?
The idea that it’s good to use Schema.org structured data to rank better in an AI search engine is not based on facts, it’s just fanciful speculation. Or it could be from a “game of telephone” effect where one person says something and then twenty people later it’s transformed into something completely different.
For example, Jono Alderson proposed that structured data could be a standard that AI search engines could use to understand the web better. He wasn’t saying that AI search engines currently use it, he was just proposing that AI search engines should consider adopting it and maybe that post got telephoned into a full-blown theory twenty SEOs later.
Unfortunately, there’s a lot of unfounded ideas floating around in SEO circles. The other day I saw an SEO assert in social media that Google Local Search doesn’t use IP addresses in response to search “near me” search queries. All anyone had to do to test that idea is to sign into a VPN, choose a geographic location for their IP address and do a “near me” search query and they will see that the IP address used by the VPN influenced the “near me” search results.
Screenshot Of Near Me Query Influenced By IP Address
Google even publishes a support page that says they use IP address to personalize search results yet there are people who believe otherwise because some SEO did a correlation study and when questioned we’re back to someone bellowing that Google lies.
Will You Believe Your Lying Eyes?
Schema.Org Structured Data And AI Search Results
“SEOs” recommending that publishers use Schema.org structured data for LLM training data also makes no sense because training data isn’t cited in LLM output, just for output that is sourced from the web, which itself is sourced from a search index that’s from a crawler. As mentioned earlier, publishers only use a fraction of available Schema.org structured data because Google itself only uses a tiny fraction of it. So it makes no sense for an AI search engine to rely on structured data for their output.
Search marketing expert Christopher Shin (LinkedIn profile) commented:
“Thinking the same thing after reading your post Patrick. This is how I interpret it currently. I thought LLM’s typically do not generate responses from search engines serps but rather from data interpretation. Right? But schema data markup would be used by SER{s to show rich snippets etc. no? I think the key nuance with schema and LLMs is that search engines use schema for SERPs whereas LLM’s use data interpretation when it comes to how schema impacts LLM’s.”
People like Christopher Shin and Patrick Stox give me hope that pragmatic and sensible SEO is still fighting to get through the noise, Patrick’s LinkedIn post is proof of that.
Pragmatic SEO
The definition of pragmatic is doing things for sensible and realistic reasons and not on opinions that are based on incomplete information and conjecture.
Speaking as someone who’s been involved with SEO since virtually the birth of it, not thinking things through is why SEOs and publishers have traditionally wasted time with vaguely defined issues, spun their wheels on useless activities like superficial signals of EEAT and so on and so forth. It’s truly dispiriting to point to documentation and official statements and get blown back with statements like, “Google lies.” That kind of attitude makes a person “want to holler.”
With the punishment for Google’s first search antitrust case expected to be delivered in August 2025, the looming question is what will happen now with a new U.S. President and a new set of Department of Justice (DOJ) appointees.
Early signs suggest the Trump administration will largely stay the course of the Biden administration when it comes to antitrust enforcement against large tech companies, including Google.
Their rationale is drastically different from that of the previous administration, but the recent nominations and appointments for the DOJ suggest that President Trump is serious about holding Google accountable, even if their preferred remedies may differ.
Before we get into it, let’s recap what has happened so far.
The U.S. Vs. Google Case
In August 2024, Federal Judge Amit Mehta ruled that Google violated the U.S. antitrust law by maintaining an illegal monopoly through exclusive agreements it had with companies like Apple to be the world’s default search engine on smartphones and web browsers.
Additionally, Google was found guilty of monopolizing general search text advertising because Google was able to raise prices on search advertising products higher than what the government claimed would have been expected or possible in a fair market.
Potential Remedies For Google
The DOJ submitted two filings with their suggestions to remediate Google’s monopolistic actions.
Proposed remedies range from restrictions on deals that feature Google’s search engine as the default on browsers and devices all the way to a breakup of the company by forcing the sale of Google’s browser Chrome.
Other intriguing remedies that have been proposed include syndicating the Google search algorithm to competitors, forced licensing of ad feeds to competitors, and divesting the Android operating system.
The DOJ under Biden made it clear in their most recent filing on November 20, 2024, that divesting Chrome is their preferred option, along with the discontinuation of exclusive agreements with browsers and phone companies.
The implications of divesting Chrome are also the most wide-reaching – not only is Chrome used by nearly two-thirds of the world’s internet users, but we learned through this trial that click data from Chrome is used to train the search algorithms using Navboost, helping Google maintain its competitive edge.
Losing Chrome’s data would almost certainly guarantee a drastically different Google search engine.
Google filed its response to the DOJ, arguing that the proposed remedies are much wider reaching than what the case was about and that America’s global leadership position in tech could be hindered by this.
Instead, they proposed allowing exclusive agreements to be made with companies like Apple and Mozilla, but with the ability to set a different default search engine on different platforms and browsing modes.
It also proposed that Android device manufacturers could preload multiple search engines, as well as preload Google apps without Google Search or Chrome.
Both sides will return to court for the remedies litigation in May 2025, with a ruling expected to be delivered in August 2025.
What Happens Now
Back to the question at hand: What happens once Trump takes office?
The initial signals, including Trump’s nominations for key roles at the FTC and the Department of Justice Antitrust Division, suggest the administration will continue to use a heavy hand against large tech companies facing antitrust troubles like Google. But, their solutions may differ from the current proposed remedies.
Trump’s Relevant Nominees
Trump has nominated several key individuals who will influence antitrust enforcement, particularly concerning Big Tech companies.
These appointments indicate that the crackdown on tech giants will likely continue, in effect, a surprising bipartisan effort. Trump’s key nominees include:
Gail Slater: Nominated to lead the Department of Justice’s Antitrust Division, Slater has a background as a policy advisor to Vice President-elect J.D. Vance and experience in tech policy at the National Economic Council. If confirmed, she would inherit the antitrust case against Google.
Andrew N. Ferguson: Appointed as Chair of the Federal Trade Commission (FTC), Ferguson has expressed intentions to reassess the agency’s approach to mergers and acquisitions, which has been uncommonly strong against mergers and acquisitions, while still maintaining oversight of dominant tech platforms.
Mark Meador: Appointed as an FTC Commissioner, a role previously held by Ferguson, Meador is recognized for his pro-enforcement stance, especially regarding technology companies, in his previous work with the U.S. Senate Judiciary Committee. His previous work includes drafting legislation aimed at addressing competitive practices in the tech industry.
While all three of these nominees are deeply rooted in the Republican party, they are all united in their pro-enforcement stances when it comes to Big Tech.
This is a departure from the typical Republican pro-business, anti-regulation position, signifying Trump’s seriousness in curbing the power of Google and other tech giants.
The Trump Administration’s Views On Google’s Antitrust Case
Trump’s disdain for Big Tech companies, including Google, has been consistent since his first presidency.
Why does he hate Google so much? A couple of reasons seem most likely:
He sees weakening Big Tech companies as a way to promote “free speech” because of their misinformation moderation policies and claims the search results are biased against conservatives.
Despite this seemingly constant position against Google, President Trump has also suggested that breaking Google up may destroy the company rather than help promote fairness and competition.
He has also warned that breaking up Google may make the U.S. appear weaker to foreign powers because “China is afraid of Google.”
Elsewhere in the administration, Vice President Vance has previously called for the breakup of Google and praised the Biden administration’s Federal Trade Commission Chair, Lina Khan, for her aggressive approach to antitrust enforcement.
Whether they decide to take a stance that is pro-breaking Google up remains to be seen, but it appears that they will be taking office with a desire to strengthen competition in this market.
Final Thoughts
There is a lot of time between Trump taking office and the remedies litigation starting up again for the case against Google in May 2025.
The DOJ still needs to argue why they believe Google should be forced to sell Chrome, and if this is no longer the belief of the DOJ appointees, they will need to argue why other remedies make more sense.
It seems reasonable to assume, based on the appointees, that they will be taking some big swings at Google and arguing for the remedies that they believe would be most effective at enhancing competition.
If you are someone who believes action needs to be taken against Google, Trump’s current anti-Google stance may work in your favor regardless of whether you agree with his rationale for it.
A report from Modern Retail shows that people who use ChatGPT and Google Gemini for quick summaries also click the links these tools provide.
This is important for marketers, as it suggests that AI-driven search may change product discovery and online traffic.
While these numbers are self-reported and lack broader data, they offer insight into how consumers engage with AI search results and how brands can benefit.
What Brands Are Observing
Viv, a period care brand, noticed a trend last summer when its website traffic increased by 400%. Marketing director Kelly Donohue linked this to the rise of AI tools.
This spike coincided with a study in Environment International that found harmful heavy metals in popular tampon brands. Viv’s blog posts about product safety gained visibility as people searched for safer options.
The increased traffic resulted in more sales, with Viv reporting a 436% rise from these AI-driven referrals. This indicates that users actively clicked through to learn more and make purchases.
What To Learn From This
Viv’s experience highlights the need for brands to create comprehensive content that answers people’s questions.
Donohue pointed out that platforms like ChatGPT prefer articles with context, sources, and thorough explanations over keyword-heavy material.
Donohue explained,
“These AI tools are specifically scraping through content, but looking for more than just keywords. They’re looking for a cohesive response that they can give to people that includes context, sources, and background.”
In response, Viv focused on transparency and product safety. By creating educational articles, Viv built consumer trust and improved its visibility in AI recommendations.
The effort paid off, Donohue added:
“We ended up selling out of about six months of tampon inventory in three weeks, driven by Google’s AI-powered recommendations.”
Other Brands Report Similar Trends
Joe & Bella is an adaptive apparel brand that has gained more visitors from ChatGPT recommendations.
It makes clothing for older adults and people with mobility challenges, and during the holiday season, it saw an increase in visitors and purchases.
Jimmy Zollo, Joe & Bella’s co-founder and CEO, tells Modern Retail:
“I don’t really know how or what they would have typed or asked ChatGPT to have found us over the holidays.”
Zollo speculated that the company’s ongoing investment in SEO and its blog content likely played a role.
The brand consistently uses keywords like “adaptive clothing” in its search ads and blog posts, which may have helped position it in AI-driven results.
Zollo added:
“It was pretty cool and unexpected, but we need to better understand how to optimize for these searches going forward.”
What This Means for Marketers
These reports show that people engage with links in AI-generated search results rather than just reading summaries.
Dan Buckstaff, chief product officer at Spins, compares this to the early days of SEO.
Buckstaff said:
“Similar to 15 years ago when we were questioning how SEO worked, we’re left with questioning how brands can benefit from AI environments.”
Spins’ 2025 Industry Trends Report indicates that consumers are increasingly using AI tools like ChatGPT and social media platforms like TikTok to discover products.
While advertising on these AI tools is still developing, brands with strong, organized content are benefiting.
Looking Ahead
Consumers are increasingly clicking on links in AI-driven search results, especially younger audiences like Gen Z, who use AI tools for product discovery.
For brands like Viv, this change is crucial for content creation.
Donohue said:
“These searches are top of mind for us now, and the way we’re writing our blogs and the content on our website can play a huge part in people finding us through AI tools.”
The key takeaway is to focus on straightforward, educational content to improve your chances of being recommended by AI-powered search tools.
Forget massive steel tanks—some scientists want to make chemicals with the help of rocks deep beneath Earth’s surface.
New research shows that ammonia, a chemical crucial for fertilizer, can be produced from rocks at temperatures and pressures that are common in the subsurface. The research was published today in Joule, and MIT Technology Review can exclusively report that a new company, called Addis Energy, was founded to commercialize the process.
Ammonia is used in most fertilizers and is a vital part of our modern food system. It’s also being considered for use as a green fuel in industries like transoceanic shipping. The problem is that current processes used to make ammonia require a lot of energy and produce huge amounts of the greenhouse gases that cause climate change—over 1% of the global total. The new study finds that the planet’s internal conditions can be used to produce ammonia in a much cleaner process.
“Earth can be a factory for chemical production,” says Iwnetim Abate, an MIT professor and author of the new study.
This idea could be a major change for the chemical industry, which today relies on huge facilities running reactions at extremely high temperatures and pressures to make ammonia.
The key ingredients for ammonia production are sources of nitrogen and hydrogen. Much of the focus on cleaner production methods currently lies in finding new ways to make hydrogen, since that chemical makes up the bulk of ammonia’s climate footprint, says Patrick Molloy, a principal at the nonprofit research agency Rocky Mountain Institute.
Recently, researchers and companies have located naturally occurring deposits of hydrogen underground. Iron-rich rocks tend to drive reactions that produce the gas, and these natural deposits could provide a source of low-cost, low-emissions hydrogen.
While geologic hydrogen is still in its infancy as an industry, some researchers are hoping to help the process along by stimulating production of hydrogen underground. With the right rocks, heat, and a catalyst, you can produce hydrogen cheaply and without emitting large amounts of climate pollution.
Hydrogen can be difficult to transport, though, so Abate was interested in going one step further by letting the conditions underground do the hard work in powering chemical reactions that transform hydrogen and nitrogen into ammonia. “As you dig, you get heat and pressure for free,” he says.
To test out how this might work, Abate and his team crushed up iron-rich minerals and added nitrates (a nitrogen source), water (a hydrogen source), and a catalyst to help reactions along in a small reactor in the lab. They found that even at relatively low temperatures and pressures, they could make ammonia in a matter of hours. If the process were scaled up, the researchers estimate, one well could produce 40,000 kilograms of ammonia per day.
While the reactions tend to go faster at high temperature and pressure, the researchers found that ammonia production could be an economically viable process even at 130 °C (266 °F) and a little over two atmospheres of pressure, conditions that would be accessible at depths reachable with existing drilling technology.
While the reactions work in the lab, there’s a lot of work to do to determine whether, and how, the process might actually work in the field. One thing the team will need to figure out is how to keep reactions going, because in the reaction that forms ammonia, the surface of the iron-rich rocks will be oxidized, leaving them in a state where they can’t keep reacting. But Abate says the team is working on controlling how thick the unusable layer of rock is, and its composition, so the chemical reactions can continue.
To commercialize this work, Abate is cofounding a company called Addis Energy with $4.25 million in pre-seed funds from investors including Engine Ventures. His cofounders include Michael Alexander and Charlie Mitchell (who have both spent time in the oil and gas industry) and Yet-Ming Chiang, an MIT professor and serial entrepreneur. The company will work on scaling up the research, including finding potential sites with the geological conditions to produce ammonia underground.
The good news for scale-up efforts is that much of the necessary technology already exists in oil and gas operations, says Alexander, Addis’s CEO. A field-deployed system will involve drilling, pumping fluid down into the ground, and extracting other fluids from beneath the surface, all very common operations in that industry. “There’s novel chemistry that’s wrapped in an oil and gas package,” he says.
The team will also work on refining cost estimates for the process and gaining a better understanding of safety and sustainability, Abate says. Ammonia is a toxic industrial chemical, but it’s common enough for there to be established procedures for handling, storing, and transporting it, says RMI’s Molloy.
Judging from the researchers’ early estimates, ammonia produced with this method could cost up to $0.55 per kilogram. That’s more than ammonia produced with fossil fuels today ($0.40/kg), but the technique would likely be less expensive than other low-emissions methods of producing the chemical. Tweaks to the process, including using nitrogen from the air instead of nitrates, could help cut costs further, even as low as $0.20/kg.
New approaches to making ammonia could be crucial for climate efforts. “It’s a chemical that’s essential to our way of life,” says Karthish Manthiram, a professor at Caltech who studies electrochemistry, including alternative ammonia production methods.
The team’s research appears to be designed with scalability in mind from the outset, and using Earth itself as a reactor is the kind of thinking needed to accelerate the long-term journey to sustainable chemical production, Manthiram adds.
While the company focuses on scale-up efforts, there’s plenty of fundamental work left for Abate and other labs to do to understand what’s going on during the reactions at the atomic level, particularly at the interface between the rocks and the reacting fluid.
Research in the lab is exciting, but it’s only the first step, Abate says. The next one is seeing if this actually works in the field.
Correction: Due to a unit typo in the journal article, a previous version of this story misstated the amount of ammonia each well could theoretically produce. The estimate is 40,000 kilograms of ammonia per day, not 40,000 tons.
The United States and China are entangled in what many have dubbed an “AI arms race.”
In the early days of this standoff, US policymakers drove an agenda centered on “winning” the race, mostly from an economic perspective. In recent months, leading AI labs such as OpenAI and Anthropic got involved in pushing the narrative of “beating China” in what appeared to be an attempt to align themselves with the incoming Trump administration. The belief that the US can win in such a race was based mostly on the early advantage it had over China in advanced GPU compute resources and the effectiveness of AI’s scaling laws.
But now it appears that access to large quantities of advanced compute resources is no longer the defining or sustainable advantage many had thought it would be. In fact, the capability gap between leading US and Chinese models has essentially disappeared, and in one important way the Chinese models may now have an advantage: They are able to achieve near equivalent results while using only a small fraction of the compute resources available to the leading Western labs.
The AI competition is increasingly being framed within narrow national security terms, as a zero-sum game, and influenced by assumptions that a future war between the US and China, centered on Taiwan, is inevitable. The US has employed “chokepoint” tactics to limit China’s access to key technologies like advanced semiconductors, and China has responded by accelerating its efforts toward self-sufficiency and indigenous innovation, which is causing US efforts to backfire.
Recently even outgoing US Secretary of Commerce Gina Raimondo, a staunch advocate for strict export controls, finally admitted that using such controls to hold back China’s progress on AI and advanced semiconductors is a “fool’s errand.” Ironically, the unprecedented export control packages targeting China’s semiconductor and AI sectors have unfolded alongside tentative bilateral and multilateral engagements to establish AI safety standards and governance frameworks—highlighting a paradoxical desire of both sides to compete and cooperate.
When we consider this dynamic more deeply, it becomes clear that the real existential threat ahead is not from China, but from the weaponization of advanced AI by bad actors and rogue groups who seek to create broad harms, gain wealth, or destabilize society. As with nuclear arms, China, as a nation-state, must be careful about using AI-powered capabilities against US interests, but bad actors, including extremist organizations, would be much more likely to abuse AI capabilities with little hesitation. Given the asymmetric nature of AI technology, which is much like cyberweapons, it is very difficult to fully prevent and defend against a determined foe who has mastered its use and intends to deploy it for nefarious ends.
Given the ramifications, it is incumbent on the US and China as global leaders in developing AI technology to jointly identify and mitigate such threats, collaborate on solutions, and cooperate on developing a global framework for regulating the most advanced models—instead of erecting new fences, small or large, around AI technologies and pursing policies that deflect focus from the real threat.
It is now clearer than ever that despite the high stakes and escalating rhetoric, there will not and cannot be any long-term winners if the intense competition continues on its current path. Instead, the consequences could be severe—undermining global stability, stalling scientific progress, and leading both nations toward a dangerous technological brinkmanship. This is particularly salient given the importance of Taiwan and the global foundry leader TSMC in the AI stack, and the increasing tensions around the high-tech island.
Heading blindly down this path will bring the risk of isolation and polarization, threatening not only international peace but also the vast potential benefits AI promises for humanity as a whole.
Historical narratives, geopolitical forces, and economic competition have all contributed to the current state of the US-China AI rivalry. A recent report from the US-China Economic and Security Review Commission, for example, frames the entire issue in binary terms, focused on dominance or subservience. This “winner takes all” logic overlooks the potential for global collaboration and could even provoke a self-fulfilling prophecy by escalating conflict. Under the new Trump administration this dynamic will likely become more accentuated, with increasing discussion of a Manhattan Project for AI and redirection of US military resources from Ukraine toward China.
Fortunately, a glimmer of hope for a responsible approach to AI collaboration is appearing now as Donald Trump recently posted on January 17 that he’d restarted direct dialogue with Chairman Xi Jinping regarding various areas of collaboration, and given past cooperation should continue to be “partners and friends.” The outcome of the TikTok drama, putting Trump at odds with sharp China critics in his own administration and Congress, will be a preview of how his efforts to put US China relations on a less confrontational trajectory.
The promise of AI for good
Western mass media usually focuses on attention-grabbing issues described in terms like the “existential risks of evil AI.” Unfortunately, the AI safety experts who get the most coverage often recite the same narratives, scaring the public. In reality, no credible research shows that more capable AI will become increasingly evil. We need to challenge the current false dichotomy of pure accelerationism versus doomerism to allow for a model more like collaborative acceleration.
It is important to note the significant difference between the way AI is perceived in Western developed countries and developing countries. In developed countries the public sentiment toward AI is 60% to 70% negative, while in the developing markets the positive ratings are 60% to 80%. People in the latter places have seen technology transform their lives for the better in the past decades and are hopeful AI will help solve the remaining issues they face by improving education, health care, and productivity, thereby elevating their quality of life and giving them greater world standing. What Western populations often fail to realize is that those same benefits could directly improve their lives as well, given the high levels of inequity even in developed markets. Consider what progress would be possible if we reallocated the trillions that go into defense budgets each year to infrastructure, education, and health-care projects.
Once we get to the next phase, AI will help us accelerate scientific discovery, develop new drugs, extend our health span, reduce our work obligations, and ensure access to high-quality education for all. This may sound idealistic, but given current trends, most of this can become a reality within a generation, and maybe sooner. To get there we’ll need more advanced AI systems, which will be a much more challenging goal if we divide up compute/data resources and research talent pools. Almost half of all top AI researchers globally (47%) were born or educated in China, according to industry studies. It’s hard to imagine how we could have gotten where we are without the efforts of Chinese researchers. Active collaboration with China on joint AI research could be pivotal to supercharging progress with a major infusion of quality training data and researchers.
The escalating AI competition between the US and China poses significant threats to both nations and to the entire world. The risks inherent in this rivalry are not hypothetical—they could lead to outcomes that threaten global peace, economic stability, and technological progress. Framing the development of artificial intelligence as a zero-sum race undermines opportunities for collective advancement and security. Rather than succumb to the rhetoric of confrontation, it is imperative that the US and China, along with their allies, shift toward collaboration and shared governance.
Our recommendations for policymakers:
Reduce national security dominance over AI policy. Both the US and China must recalibrate their approach to AI development, moving away from viewing AI primarily as a military asset. This means reducing the emphasis on national security concerns that currently dominate every aspect of AI policy. Instead, policymakers should focus on civilian applications of AI that can directly benefit their populations and address global challenges, such as health care, education, and climate change. The US also needs to investigate how to implement a possible universal basic income program as job displacement from AI adoption becomes a bigger issue domestically.
2. Promote bilateral and multilateral AI governance. Establishing a robust dialogue between the US, China, and other international stakeholders is crucial for the development of common AI governance standards. This includes agreeing on ethical norms, safety measures, and transparency guidelines for advanced AI technologies. A cooperative framework would help ensure that AI development is conducted responsibly and inclusively, minimizing risks while maximizing benefits for all.
3. Expand investment in detection and mitigation of AI misuse. The risk of AI misuse by bad actors, whether through misinformation campaigns, telecom, power, or financial system attacks, or cybersecurity attacks with the potential to destabilize society, is the biggest existential threat to the world today. Dramatically increasing funding for and international cooperation in detecting and mitigating these risks is vital. The US and China must agree on shared standards for the responsible use of AI and collaborate on tools that can monitor and counteract misuse globally.
4. Create incentives for collaborative AI research. Governments should provide incentives for academic and industry collaborations across borders. By creating joint funding programs and research initiatives, the US and China can foster an environment where the best minds from both nations contribute to breakthroughs in AI that serve humanity as a whole. This collaboration would help pool talent, data, and compute resources, overcoming barriers that neither country could tackle alone. A global effort akin to the CERN for AI will bring much more value to the world, and a peaceful end, than a Manhattan Project for AI, which is being promoted by many in Washington today.
5. Establish trust-building measures. Both countries need to prevent misinterpretations of AI-related actions as aggressive or threatening. They could do this via data-sharing agreements, joint projects in nonmilitary AI, and exchanges between AI researchers. Reducing import restrictions for civilian AI use cases, for example, could help the nations rebuild some trust and make it possible for them to discuss deeper cooperation on joint research. These measures would help build transparency, reduce the risk of miscommunication, and pave the way for a less adversarial relationship.
6. Support the development of a global AI safety coalition. A coalition that includes major AI developers from multiple countries could serve as a neutral platform for addressing ethical and safety concerns. This coalition would bring together leading AI researchers, ethicists, and policymakers to ensure that AI progresses in a way that is safe, fair, and beneficial to all. This effort should not exclude China, as it remains an essential partner in developing and maintaining a safe AI ecosystem.
7. Shift the focus toward AI for global challenges. It is crucial that the world’s two AI superpowers use their capabilities to tackle global issues, such as climate change, disease, and poverty. By demonstrating the positive societal impacts of AI through tangible projects and presenting it not as a threat but as a powerful tool for good, the US and China can reshape public perception of AI.
Our choice is stark but simple: We can proceed down a path of confrontation that will almost certainly lead to mutual harm, or we can pivot toward collaboration, which offers the potential for a prosperous and stable future for all. Artificial intelligence holds the promise to solve some of the greatest challenges facing humanity, but realizing this potential depends on whether we choose to race against each other or work together.
The opportunity to harness AI for the common good is a chance the world cannot afford to miss.
Alvin Wang Graylin
Alvin Wang Graylin is a technology executive, author, investor, and pioneer with over 30 years of experience shaping innovation in AI, XR (extended reality), cybersecurity, and semiconductors. Currently serving as global vice president at HTC, Graylin was the company’s China president from 2016 to 2023. He is the author of Our Next Reality.
Paul Triolo
Paul Triolo is apartner for China and technology policy lead at DGA-Albright Stonebridge Group. He advises clients in technology, financial services, and other sectors as they navigate complex political and regulatory matters in the US, China, the European Union, India, and around the world.
OpenAI spent $1.76 million on government lobbying in 2024 and $510,000 in the last three months of the year alone, according to a new disclosure filed on Tuesday—a significant jump from 2023, when the company spent just $260,000 on Capitol Hill. The company also disclosed a new in-house lobbyist, Meghan Dorn, who worked for five years for Senator Lindsey Graham and started at OpenAI in October. The filing also shows activity related to two new pieces of legislation in the final months of the year: the House’s AI Advancement and Reliability Act, which would set up a government center for AI research, and the Senate’s Future of Artificial Intelligence Innovation Act, which would create shared benchmark tests for AI models.
OpenAI did not respond to questions about its lobbying efforts.
But perhaps more important, the disclosure is a clear signal of the company’s arrival as a political player, as its first year of serious lobbying ends and Republican control of Washington begins. While OpenAI’s lobbying spending is still dwarfed by its peers’—Meta tops the list of Big Tech spenders, with more than $24 million in 2024—the uptick comes as it and other AI companies have helped redraw the shape of AI policy.
For the past few years, AI policy has been something like a whack-a-mole response to the risks posed by deepfakes and misinformation. But over the last year, AI companies have started to position the success of the technology as pivotal to national security and American competitiveness, arguing that the government must therefore support the industry’s growth. As a result, OpenAI and others now seem poised to gain access to cheaper energy, lucrative national security contracts, and a more lax regulatory environment that’s unconcerned with the minutiae of AI safety.
While the big players seem more or less aligned on this grand narrative, messy divides on other issues are still threatening to break through the harmony on display at President Trump’s inauguration this week.
AI regulation really began in earnest after ChatGPT launched in November 2022. At that point, “a lot of the conversation was about responsibility,” says Liana Keesing, campaigns manager for technology reform at Issue One, a democracy nonprofit that tracks Big Tech’s influence.
Companies were asked what they’d do about sexually abusive deepfake images and election disinformation. “Sam Altman did a very good job coming in and painting himself early as a supporter of that process,” Keesing says.
OpenAI started its official lobbying effort around October 2023, hiring Chan Park—a onetime Senate Judiciary Committee counsel and Microsoft lobbyist—to lead the effort. Lawmakers, particularly then Senate majority leader Chuck Schumer, were vocal about wanting to curb these particular harms; OpenAI hired Schumer’s former legal counsel, Reginald Babin, as a lobbyist, according to data from OpenSecrets. This past summer, the company hired the veteran political operative Chris Lehane as its head of global policy.
OpenAI’s previous disclosures confirm that the company’s lobbyists subsequently focused much of last year on legislation like the No Fakes Act and the Protect Elections from Deceptive AI Act. The bills did not materialize into law. But as the year went on, the regulatory goals of AI companies began to change. “One of the biggest shifts that we’ve seen,” Keesing says, “is that they’ve really started to focus on energy.”
In September, Altman, along with leaders from Nvidia, Anthropic, and Google, visited the White House and pitched the vision that US competitiveness in AI will depend on subsidized energy infrastructure to train the best models. Altman proposed to the Biden administration the construction of multiple five-gigawatt data centers, which would each consume as much electricity as New York City.
Around the same time, companies like Meta and Microsoft started to say that nuclear energy will provide the path forward for AI, announcing deals aimed at firing up new nuclear power plants.
It seems likely OpenAI’s policy team was already planning for this particular shift. In April, the company hired lobbyist Matthew Rimkunas, who worked for Bill Gates’s sustainable energy effort Breakthrough Energies and, before that, spent 16 years working for Senator Graham; the South Carolina Republican serves on the Senate subcommittee that manages nuclear safety.
This new AI energy race is inseparable from the positioning of AI as essential for national security and US competitiveness with China. OpenAI laid out its position in a blog post in October, writing, “AI is a transformational technology that can be used to strengthen democratic values or to undermine them. That’s why we believe democracies should continue to take the lead in AI development.” Then in December, the company went a step further and reversed its policy against working with the military, announcing it would develop AI models with the defense-tech company Anduril to help take down drones around military bases.
That same month, Sam Altman said during an interview with The Free Press that the Biden administration was “not that effective” in shepherding AI: “The things that I think should have been the administration’s priorities, and I hope will be the next administration’s priorities, are building out massive AI infrastructure in the US, having a supply chain in the US, things like that.”
That characterization glosses over the CHIPS Act, a $52 billion stimulus to the domestic chips industry that is, at least on paper, aligned with Altman’s vision. (It also preceded an executive order Biden issued just last week, to lease federal land to host the type of gigawatt-scale data centers that Altman had been asking for.)
Intentionally or not, Altman’s posture aligned him with the growing camaraderie between President Trump and Silicon Valley. Mark Zuckerberg, Elon Musk, Jeff Bezos, and Sundar Pichai all sat directly behind Trump’s family at the inauguration on Monday, and Altman also attended. Many of them had also made sizable donations to Trump’s inaugural fund, with Altman personally throwing in $1 million.
It’s easy to view the inauguration as evidence that these tech leaders are aligned with each other, and with other players in Trump’s orbit. But there are still some key dividing lines that will be worth watching. Notably, there’s the clash over H-1B visas, which allow many noncitizen AI researchers to work in the US. Musk and Vivek Ramaswamy (who is, as of this week, no longer a part of the so-called Department of Government Efficiency) have been pushing for that visa program to be expanded. This sparked backlash from some allies of the Trump administration, perhaps most loudly Steve Bannon.
Another fault line is the battle between open- and closed-source AI. Google and OpenAI prevent anyone from knowing exactly what’s in their most powerful models, often arguing that this keeps them from being used improperly by bad actors. Musk has sued OpenAI and Microsoft over the issue, alleging that closed-source models are antithetical to OpenAI’s hybrid nonprofit structure. Meta, whose Llama model is open-source, recently sided with Musk in that lawsuit. Venture capitalist and Trump ally Marc Andreessen echoed these criticisms of OpenAI on X just hours after the inauguration. (Andreessen has also said that making AI models open-source “makes overbearing regulations unnecessary.”)
Finally, there are the battles over bias and free speech. The vastly different approaches that social media companies have taken to moderating content—including Meta’s recent announcement that it would end its US fact-checking program—raise questions about whether the way AI models are moderated will continue to splinter too. Musk has lamented what he calls the “wokeness” of many leading models, and Andreessen said on Tuesday that “Chinese LLMs are much less censored than American LLMs” (though that’s not quite true, given that many Chinese AI models have government-mandated censorship in place that forbids particular topics). Altman has been more equivocal: “No two people are ever going to agree that one system is perfectly unbiased,” he told The Free Press.
It’s only the start of a new era in Washington, but the White House has been busy. It has repealed many executive orders signed by President Biden, including the landmark order on AI that imposed rules for government use of the technology (while it appears to have kept Biden’s order on leasing land for more data centers). Altman is busy as well. OpenAI, Oracle, and SoftBank reportedly plan to spend up to $500 billion on a joint venture for new data centers; the project was announced by President Trump, with Altman standing alongside. And according to Axios, Altman will also be part of a closed-door briefing with government officials on January 30, reportedly about OpenAI’s development of a powerful new AI agent.
Brick-and-click retailers can struggle to balance local and ecommerce search engine optimization. A physical store wants to target folks in a geographic area and drive in-person visits, while an ecommerce site aims for buyers anywhere.
Different goals, however, don’t necessarily mean there is a problem.
Imagine a furniture retailer with dozens of stores in California and Florida. The CEO could message the marketing team concerned that ecommerce represents less than 20% of total revenue. Her concern the next day could be a new Miami store not appearing on a local Google search.
It feels like a conflict — from link building to content marketing. But it doesn’t have to be. A brick-and-click marketing team can balance the priorities by planning day-to-day SEO activities and developing processes.
3-Part SEO
Marketers often say that SEO has three areas of focus: technical, on-page, and off-page.
Omnichannel marketing teams balance local and ecommerce SEO.
Technical SEO focuses on site speed, URL structure, microdata, and general crawlability. The same technical practices that work for local SEO also help promote products.
Teams of content marketers and on-page optimizers frequently work hand-in-glove to ensure a site ranks for key products, categories, and locations. There is no reason local and ecommerce efforts cannot live in harmony.
Off-page SEO includes backlinks, brand mentions, and filling out and maintaining business profiles, which feed into Google’s local pack and map results. Off-page efforts fit naturally into local optimization even if the focus is ecommerce.
In Action
Sharing tasks for ecommerce and local SEO helps both. Here are priorities, workflows, and automation to streamline the load.
Prioritize setup and integration. Many SEO tasks require initial time-consuming effort followed by less intense maintenance.
For example, optimizing a Google My Business page requires claiming, adding contact info, images, and videos, and encouraging reviews — a lot of upfront work. Keeping the My Business page fresh is much easier.
Similarly, the furniture retailer with dozens of stores might want to set up location-specific landing pages on its website. Each page will have images from the local store, a Google map, store hours, and a greeting from the store manager. Building the pages takes more effort than maintaining them.
Thus a marketing team that prioritizes setup is building the SEO infrastructure to manage selling products online and driving physical foot traffic.
Develop standard operating procedures. Many omnichannel retailers approach SEO by the project. The CEO says to promote the new Miami store, and the team focuses on that effort.
Unfortunately, this sort of project-first approach has three potential problems. It’s (i) reactionary instead of strategic; (ii) creates redundancy, as every project starts anew, and (iii) overlooks critical maintenance.
A better approach is to build a set of standard operating procedures, such as (i) how blog articles are optimized, (ii) the SEO process for adding products or pages, and (iii) a schedule for maintenance and updates.
Use AI to generate content. Working on ecommerce and local SEO simultaneously requires more on-page content.
Developing that extra content may be fairly easy in 2025. Imagine our furniture store. Its content team might produce a blog post targeting the keyword phrase “top Scandinavian design trends for 2025.”
The initial human-written draft could be an AI prompt, generating regional variations like “top Scandinavian design trends for South Florida.”
The primary article would serve as a hub linking to and receiving links from pages of each region.
Automate repetitive tasks. Finally, automation can expedite many aspects of SEO maintenance and improvements. Zapier, generative AI platforms, and similar tools can quickly complete repeat functions and even run SEO audits.
Fundamentals
My impetus for this article was a real-world consultation with a furniture chain. The business focused on the differences between local and ecommerce SEO instead of the overall goal.
Although attracting online buyers and driving in-store traffic may seem different, the SEO fundamentals are the same.
What do owners, freelancers, managers, and employees have in common? They all communicate! Here are 10 titles for 2025 to improve writing and speaking in any medium or circumstance — storytelling, marketing, persuasion, networking, negotiating, and more.
In this award-winning new bestseller, Duhigg explores how conversations work, examining how our experiences, values, and emotions affect how we speak and listen. He combines stories from contexts as diverse as the jury box and couples counseling with research findings and advice to teach the skills and tools to make us heard, hear others clearly, and connect with anyone.
Stratton, a messaging consultant for B2B tech firms, explains how to ditch boring jargon about your product’s features and create compelling messages that convey how it will solve problems and benefit customers. Stratton offers teaching exercises and techniques based on real-world examples for conveying your product’s value and positioning your company as a market leader.
Shleyner shares the insights on storytelling, persuasion, and creativity that have made him “the copywriter’s copywriter,” his newsletter “the gold standard” in the industry, and gained the admiration of marketing writers such as Ann Handley, Brian Clark, and Cameron Day. The book’s micro-lessons cover mindset: “Thinking Like a Copywriter” and execution: “Writing Like a Copywriter,” showing readers how to connect with people whether writing social media content, landing pages, ad campaigns, or a dating profile.
This book, which mixes theatrical experience and business strategy, upends conventional public speaking advice. Ganino, a high-end speaking coach, author, director, and former TEDx producer, shares his “Mike Drop Method” framework for engaging audiences and thriving in the spotlight, whether delivering a presentation, leading a meeting, or giving a keynote speech.
Sparrow, an Emmy-award-winning TV, radio, and podcast host, sums up communication with catchphrases “Live It, Tell It, Sell It” and “Be Brave, Be Free, Be You.” Her book offers down-to-earth advice, inspiring stories, and practical tips to help readers build connections with their network, improve their leadership, and impact their community.
Miller’s million-selling branding bible earned lavish praise from readers, including Seth Godin and the governor of Tennessee. This revised and updated edition delves further into using the author’s seven universal story elements to clarify a message, cut through the competitive noise, and express unique value, no matter the audience — voters, fans, consumers, or anyone.
Guruswamy, a product development executive and current chief technical officer of Kickstarter, offers a practical guide to difficult but necessary conversations. Whether it’s notifying a client of a product delay or explaining performance improvement to an employee, this book offers practical guidance and example scripts that enable managers to give bad news effectively and empathetically.
Known as “the father of evidence-based bargaining,” Harvard Business School Professor Bazerman combines a refresher on essential, time-tested negotiating techniques with a practical guide on adapting them to today’s situations.
Coming next month, this book applies scientific principles to help readers make decisions aligned with their values. The author is a physician and organizational psychologist who researches trust, conflicts of interest, disclosure, and compliance. An instructor at prestigious U.S. and U.K. universities, she explores why people “go along to get along” and how they can speak up and do what’s right instead of what others expect.
This book already ranks high in multiple Amazon categories, even though it won’t be available till March. The author is a lawyer, writer, and speaker whose videos, newsletter, and podcast have garnered huge followings. He offers practical advice, actionable strategies, and useful phrases for turning difficult conversations into meaningful dialogues in business and life.
Google has updated its documentation to provide clearer guidance on its site reputation abuse policy.
The changes are meant to you better understand what qualifies as a violation and how to stay compliant.
While the updates don’t change how the policy is applied, they make the rules easier to follow by incorporating more detailed explanations from a recent blog post FAQ.
What Changed?
The updated documentation now includes content directly pulled from Google’s November blog post about site reputation abuse.
That blog post introduced a Q&A section to clarify the policy. Google has now added this FAQ guidance to its official spam policies documentation.
In a statement, Google explained:
“We updated the site reputation abuse policy to include guidance from our blog post’s FAQ on site reputation abuse. These are editorial changes only, no change in behavior.”
This means the policy hasn’t changed—it’s just been rewritten to make it easier to understand.
What Is Site Reputation Abuse?
Site reputation abuse happens when third-party content is published on a well-established website to take advantage of that site’s ranking signals.
Essentially, it occurs when someone uses a reputable site as a shortcut to boost rankings for unrelated or low-quality content rather than earning those rankings independently.
For example:
A news site hosting coupon pages from a third-party service purely to benefit from the site’s strong rankings in Google.
An educational site publishing sponsored reviews about payday loans.
A movie review site hosting unrelated pages about essay writing services or buying social media followers.
However, not all third-party content is considered abuse. Forums, user-generated content, syndicated news articles, and editorial pieces are generally acceptable if they’re not designed to manipulate search rankings.
Why Does This Matter?
These updates make it easier to determine whether your content violates the policy.
For example, Google’s FAQ now clarifies common scenarios, such as:
Third-party content: Simply having third-party content isn’t a violation unless explicitly published to exploit a site’s rankings.
Freelance and affiliate content: Freelance content or affiliate pages are acceptable if they’re not used to manipulate rankings. Affiliate links, when tagged appropriately (e.g., with “nofollow” or “sponsored” attributes), don’t violate the rules.
The FAQ also explains how to address violations. You can fix the issue by removing or relocating problematic content, submitting reconsideration requests in Search Console, and following Google’s spam guidelines.
This is a good reminder to review your content practices to ensure they align with Google’s policies. If you host third-party content, make sure it adds value for users and doesn’t just serve to piggyback off your site’s reputation.
Advancing your in-house SEO career can be incredibly lucrative and fulfilling. But most advice is theoretical, too high-level, and comes from people who haven’t done it.
I had the good fortune of a very fruitful in-house career, leading large organizations at companies like Atlassian, G2, or Shopify.
Over the recent years, I have had the honor of helping companies like Ramp, Hims, Nextdoor, and many others hire top-tier talent and design effective teams.
But my experience is subjective, so I asked four of the most accomplished SEO pros in the world to share their insights as well (you can find their full answers at the end of the Memo):
Image Credit: Kevin Indig
Thank you so much for sharing your valuable insights!
The 5 Core Competencies Of SEO
SEO professionals need five core competencies to succeed in the long-term, that I broke down into three skills each.
I created the framework based on John’s, Malte’s, Jordan’s, Tom’s, and my own experience. Each skill is critical. You cannot just be strong in four. You need to be strong in all of them to succeed in the long term.
Image Credit: Kevin Indig
Skill 1: Communication
Communication is made up of alignment, collaboration, and outward communication.
Creating internal alignment means helping everyone understand what matters in SEO to get buy in, but also contemplate what’s happening in a crisis. For example, when an algorithm update hits your site.
Since SEO is a recommended discipline, it’s critical to collaborate effectively with supporting teams like engineering, design, content, etc., and adjacent teams like legal or procurement.
Outward communication, the way you present yourself and the company at events or on social media, matters it comes to hiring new talent and raising your company’s reputation.
Skill 2: Learning
Learning breaks down into adaptability, experience, and filtering information.
Adaptability is important because Google’s algorithms and design change a lot. Just think about the shift we’re going through with AI search right now. So, you need to be able to shift gears, leave old mental models behind, and develop new ones. You can learn about SEO, but doing it is a different kind of beast. To learn, you can have one or more side projects to tinker with or analyze and reverse engineer other sites.
It’s also important to at least know the basics of other disciplines because they all impact SEO: copywriting, positioning and messaging, conversion optimization, design, web development, and product development.
Lastly, get good at filtering information. What do you read? How do you learn from experiments, and how well are you connected to the industry so you can learn from peers?
Skill 3: Business Savviness
Business savviness breaks down into planning, focus, and execution.
Planning is a crucial skill for almost anything in life. You need to be good at setting goals, priorities, timings, and responsibilities. Planning also includes knowing what resources you need and pitching for them. Also, develop proficiency in forecasting and projecting impact.
Focus is the skill of working on the most important projects while tuning out the noise. It’s measuring the right data to know whether you’re successful and to report upwards and sideways.
Good execution is really hard. In my experience, it comes down to good project management but also understanding how your business and industry work.
Technicality doesn’t mean technical SEO but the skills of automation, data analysis, and a general technical understanding.
Automation is about doing work more efficiently while controlling for dependencies and liabilities. This skill is rapidly becoming more important as AI gets better. It used to be about proficiency with Excel, Google Sheets, SQL, web analytics, etc. But in the future, a lot of it will come down to prompt engineering and workflow automation.
Data analysis is the skill of getting and analyzing data, i.e., knowing which data to look at and how to interpret it well.
A good technical understanding comes down to learning how Google works but also being “technical” enough to talk to engineers and product managers. For example, you want to learn what tech stack your company’s site and application is built on, how the engineering team works, etc.
Leadership is the result of advocacy, hiring, and relationship building. To be clear, you should develop leadership qualities, whether you have management responsibility or not.
Advocacy means representing SEO where it matters. It demands you to proactively find out where conversations happen that impact SEO and how to influence them.
Good hiring skills come down to whether you have a high bar and if you can bring in good talent. Who do you know, and how do you evaluate them for the job?
Relationship building is critical for rapport with your manager and peers. You need allies and “friends” to lean on and learn from. Part of this is getting good at coaching others and finding a good coach.
The five core competencies offer you a helpful overview of what you need to develop. But without understanding how to apply them, they’re only half as useful.
General Vs. Specific Skills
Everybody needs to be proficient in the five core competencies, but you need to adjust the emphasis of your skills based on the industry and business model of the company you work for.
I have three tips for you:
Learn more about technical SEO and product development when you work on larger sites, usually in B2C. Get better at demand generation and content marketing for smaller sites, usually in B2B. The reason is that you want to align your skills with the biggest growth levers of the business.
Develop expertise in SERP Features that matter for your industry. For example:
News: top stories.
Ecommerce: product grids.
SaaS: video carousels.
SMB: Map Packs.
Tailor your skills to the size and maturity of a company. For example, in startups it’s more important to execute fast while you need to invest more time into creating alignment at large enterprises.
Hard Vs. Soft Skills
Hard skills are not as important as soft skills in SEO because you need to constantly adapt to Google changes and learn new hard skills as tech and consumer behavior evolve.
I recommend writing down and refining your mental model about how Google works and what drives success.
Forcing yourself to explain and think about why things are the way they are allows you to truly refine your approach to SEO.
You need to balance two things at the same time: being confident in your approach but open to new insights. Jeff Bezos: “Strong opinions, loosely held.”
Career Planning
This is hard, but most people never think about where they want to be and what it takes to get there.
But without focus, it’s easy to dabble in too many areas and waste time. What are you optimizing for?
Think about your endgame and what you need to get there. Remember, you can always change your goal. But have one.
I love Ray Dalio’s five-step framework for endgame planning 1:
Have clear goals.
Identify and don’t tolerate the problems that stand in the way of your achieving those goals.
Accurately diagnose the problems to get at their root causes.
Design plans that will get you around them.
Do what’s necessary to push these designs through to results.
I want to finish by leaving you with some top-notch resources you can use to keep developing yourself.
1. Malte suggests Learning SEO by Aleyda Solis, probably the most comprehensive repository of SEO learning material.
I present to you the raw inputs I got from John, Tom, Jordan, and Malte:
What core skills and knowledge areas are essential for success in SEO today, and how do you recommend developing them?
Jordan Silton: If I were recreating my personal career path, I would emphasize technical expertise, data analysis, communication skills, and business acumen.
However, SEO roles today are so varied across different business types, industries, and strategies that a multitude of skills are valuable and relevant.
Malte Landwehr: I think SEO has become so diverse that there is no longer one set of skills.
A technical SEO needs very different skills from a content-marketing-focused SEO. A director of SEO needs very different skills from a principal SEO consultant. The SEO work for a B2B SaaS looks totally different from the SEO work for a marketplace or aggregator. News SEO is completely different from ecommerce SEO.
If I had to pick the traits that helped me the most, I would say:
The ability to simultaneously hold multiple, contradicting frameworks and mental models in your head. Two SEOs might tell you two completely different models, how they implement SEO. Both might be wrong – but you might still learn something from both approaches.
Embrace uncertainty. When reverse engineering the Google algorithm, there are many unknowns. You need to get comfortable with that.
ELI5 & ELIPhD. You need to be able to explain SEO to everyone. During your career, you might talk to a CEO, CFO, CMO, CTO, CPO, Head of Web Product, Product Manager, Content Editor, Software Developer, Analyst, and many other roles. Each of these people needs different information. And to convince them, you need to tell different stories. You must develop the ability to talk to each of them.
John Shehata: Today’s SEO landscape has evolved from a generalist approach to a more specialized one. We now see technical SEOs, content SEOs, commerce SEOs, and many more.
The most critical skill right now is adaptability. Google’s algorithms are becoming more sophisticated, advanced, and complex, requiring SEOs to maneuver through frequent changes and quickly pivot strategies when necessary.
Developing this skill involves staying informed through industry updates, engaging with the community, and experimenting to see what works in real time.
Equally important is the ability to think with a business mindset. Historically, SEOs have been focused heavily on driving traffic, but generating traffic for traffic’s sake is no longer enough.
SEOs today need to align their strategies with business goals and revenue streams, focusing on attracting the right audience that converts rather than casting the widest net possible. This shift requires optimizing content not just to attract visitors but to support key business objectives.
Additionally, leveraging AI is essential – not just for automating tasks but for enhancing your analysis and decision-making.
AI can streamline workflows, handle complex data analysis, and support content optimization, allowing SEOs to focus on strategic tasks.
To build these skills, SEOs should learn about AI tools, experiment with them, and stay updated on new developments.
However, none of these skills will be fully effective without strong communication abilities. Being able to translate complex SEO insights into clear, actionable recommendations for non-technical stakeholders is invaluable.
This involves bridging the gap between technical teams and business units, ensuring that all departments are aligned and moving toward shared objectives.
Lastly, data analytics is a foundational skill that ties everything together. A deep understanding of data helps uncover hidden opportunities and supports informed, strategic decisions.
Mastery of tools like Google Analytics, BigQuery, and Looker Studio will allow SEOs to extract meaningful insights that can shape strategies, validate recommendations, and ultimately drive better business outcomes.
Tom Critchlow: This will be no surprise to those who know me, but business skills are critical.
The ability to first understand the full revenue profile and mechanics of the companies you work with, and then being able to communicate confidently, credibly, and clearly.
SEO is more than ever a cross-functional activity and so what we consider “soft skills” are actually critical to be able to convince teams, stakeholders, clients and organizations to invest appropriately in SEO.
Of course, you need some knowledge of SEO too! I think the ideal career experience is a role that allows you to invest in your technical and analytical SEO skills while getting a front-row seat to the wider business context and communication.
What pivotal experiences contributed most to your professional growth?
Jordan Silton: I’ve been fortunate to keep learning different roles, and each shift into a new context accelerated my growth.
Starting in paid search/SEM taught me to monitor KPIs, optimize for ROI, and use an experimental approach to improvement.
Evolving a reporting team into a data science and experimentation team expanded my understanding of how teams and metrics connect across the entire business.
Becoming a product leader was transformational in teaching me how to build consensus and influence to move a business forward.
Malte Landwehr: For me personally, it was a combination of three things:
I started tinkering with websites in my early teen year. I did everything on my own, from repairing corrupted SQL databases, to editing .htaccess files, creating content, attracting visitors, and former partnerships for monetization. This allowed me to understand the full picture of running a website.
I studied Computer Science with a focus on graph algorithms, web scraping, machine learning, information retrieval, and NLP. This allowed me to form a deep understanding of Google’s algorithms and patents.
I worked in Management Consulting. One thing I oversaw was making sure our PowerPoint slides can be read on a BlackBerry in the backseat of a car. This gave me the skills to talk to the C-level and craft proper proposals.
John Shehata: My career growth has been shaped by a diverse range of experiences.
Coming from a technical background as a software engineer and transitioning to marketing has given me a strong foundation.
One key moment was learning to translate complex SEO concepts into a language that editorial, PR, and marketing teams could understand, which helped bridge the gap between SEO needs and business objectives.
Another pivotal decision early in my career was to become a well-rounded marketer instead of specializing only in SEO.
I gained expertise in social media when platforms like Twitter and Facebook were in their infancy, built one of the first social media teams for a major news publisher, and developed a deep understanding of newsletters and partnerships. This diverse experience allowed me to eventually lead global audience development strategies for large organizations.
Managing cross-functional teams was another formative experience.
Working closely with development and engineering teams taught me to speak their language, advocate for SEO needs, and propose technical solutions that accelerated our initiatives.
While working with Editorial teams taught me how to respect the craft and appreciate all the due diligence that goes into writing content.
Working with all these different teams and understanding their strengths and needs, strengthened my ability to push back when necessary and collaborate effectively, which is crucial for driving SEO projects forward within complex organizations.
One of the most fulfilling aspects of my career has been mentoring and team building. I’ve had the privilege of hiring hundreds of SEOs and mentoring some of the best SEOs in the industry, helping them develop their own skills and grow into leadership roles.
Watching them succeed has been one of the most rewarding parts of my journey.
Finally, a turning point in my career was the conscious effort I made to build my personal brand.
Early on, I had supportive managers who encouraged me to refine my public speaking skills and present within the company.
I took these opportunities seriously, which eventually led to my first speaking engagement at SES 18 years ago, the largest SEO conference at the time with thousands of attendees.
From there, I focused on establishing my presence both online and offline, which not only advanced my career but also opened doors for me to promote my own software solutions.
Building a personal brand has proven invaluable in expanding my influence and credibility in the industry.
Tom Critchlow: My first job in digital was as an account manager for a digital agency. The first week on the job the account director and the SEO director both quit!
So, I was left speaking directly to clients about SEO with zero experience. Great way to learn both sides of the equation.
After that, working at Distilled, my brother Will taught me everything I know. I am forever indebted to his guidance.
What are the biggest mistakes you made or have seen others make in developing their career?
Jordan Silton: Most of my early career success was predicated on finding an issue or problem or opportunity and shining a light on it to get others to rally and fix it.
That approach worked well in a world of technical audits and a focus purely on what to do, rather than how to get it done.
I wish I had understood earlier how crucial it is to build up the people and relationships along the way.
In larger organizations (and small ones, too), success is almost exclusively driven by teamwork and communication rather than individual expertise.
Recognizing the value of people in the process transformed my approach, and I believe it has made me a more effective leader.
Malte Landwehr: For a long time, I underestimated the impact a good coach can have. Mindset and manifestation sound like a scam. But they work – also beyond career topics.
John Shehata: One mistake I made early on was focusing too much on rankings as a primary metric. While rankings are a great indicator, they are not the ultimate measure of success.
As I matured, I evolved to focus on traffic, and ultimately how SEO metrics align with overall business goals.
Now, my primary focus is on understanding how each SEO activity impacts revenue and long-term business growth.
I’ve also seen many SEOs panic over algorithm updates. While these changes can be disruptive, a better approach is to remain calm, evaluate the impact, and create both immediate and long-term action plans.
Sometimes, Google reverses its changes, so it’s important not to overreact.
Another common mistake is made by managers transitioning into director roles. Many struggle with balancing tactical and strategic thinking. They might dive into tactical details when speaking with C-level executives instead of focusing on strategy.
Mastering the art of switching between tactical and strategic conversations is crucial for career growth at this level.
Tom Critchlow: Not giving yourself access to context. Whatever role you’re in, if you’re not in the room where budgets are discussed and decisions are made, then you’re missing so much context.
So much of this comes down to your manager and how much they invite you into conversations “above your pay grade,” so to speak.
How do you think strategically about your career?
Jordan Silton: My favorite question about career aspirations is, “What’s your endgame?”
While it’s not crucial to stick to the same endgame, having a clear vision of what you want to achieve is vital.
My aspiration has been fairly consistent in helping businesses turn themselves around and accelerate growth, but my approach has evolved.
Initially, I thought that meant becoming a management consultant, but I was able to reframe this early in my career by realizing that agencies had significant leverage in this area.
This mindset guided my career decisions, including transitioning in-house to gain insight into internal business dynamics and knowing when to leave a successful, industry-leading business to explore opportunities with companies focused on reimagining and rebuilding their brands.
Having an end state to point toward – no matter how much you zigzag to get there – helps ground you in your professional journey.
Malte Landwehr: I am in the incredibly lucky and privileged position that I found something that I thoroughly enjoy doing, happen to be very good at it, and that companies are willing to pay a lot of money for.
I just show up every day at work and focus on whatever task sounds reasonable (and fun) to me.
John Shehata: I’ve always focused on becoming a well-rounded digital marketer rather than a specialist. My strategy was to gain experience across different channels – SEO, social media, newsletters, partnerships, etc. – so I could integrate these areas into a cohesive strategy.
This approach has paid off as I moved into senior leadership roles, where I was able to oversee not just SEO but broader audience development strategies.
Now, as the founder of an SEO software company, my focus has shifted significantly.
Running a SaaS startup requires wearing many hats – product development, sales, support, and client relations – each demanding its own set of skills.
My strategy now is centered on building long-term relationships, and deeply understanding my customers, identifying their pain points, and positioning our software as a long-term solution rather than just a tactical tool.
This means continuously evaluating how our products can deliver real value and helping publishers see the impact through clear, actionable insights.
It’s a constant balance between addressing immediate customer needs and aligning those solutions with their long-term business goals.
In addition, I place a strong emphasis on long-term skill building. I focus on developing skills that I anticipate will be critical in the next 5 to 10 years, such as AI, automation, and business development.
Staying ahead of the curve is essential in such a fast-evolving industry, and it’s important to proactively build expertise in emerging areas.
Another crucial element of my strategy is networking. Building a strong network has consistently opened new doors and opportunities for collaboration.
It’s not just about who you know, but ensuring that the people in your network know the value you bring to the table.
By fostering genuine relationships and contributing to the community, I’ve been able to establish connections that have proven invaluable throughout my career journey.
Tom Critchlow: There’s a great post on a 40-year career that uses a framework of “pace, people, prestige, profit and learning” where different career/life stages require different focus. I like that a lot.
Personally, I’ve always been motivated by learning primarily – the ability to learn new skills and new industries.
Can you suggest any resources or material for career growth?
Jordan Silton: Three books that come to mind immediately are “Turn the Ship Around! A True Story of Turning Followers Into Leaders” by L. David Marquet, “The Checklist Manifesto” by Atul Gawande, and “Never Split the Difference: Negotiating As If Your Life Depended On It” by Christopher Voss and Tahl Raz.
Each of these challenges traditional norms and presents innovative approaches grounded in science and contemporary insights.
Additionally, I’m excited about what Evan LaPointe is building at CORE Sciences. His team leverages clinical insights from neuroscience to evolve business thinking, addressing the many counterproductive norms that persist in the workplace. It’s time to upgrade our understanding, thinking, and practices for better outcomes.
Malte Landwehr: https://learningseo.io/ is the only resource you need to advance your SEO career.
John Shehata: The resources you should focus on depend on where you are in your career.
For early-stage professionals, I recommend mastering tactical skills using resources like Aleyda’s Learning SEO, Moz’s Beginner’s Guide to SEO, WIX, Semrush, or Ahrefs’ Academy.
As you progress, start exploring strategic resources like Kevin’s Growth Memo newsletter.
For more experienced professionals and SaaS owners, I suggest diving into leadership books like “Leaders Eat Last” by Simon Sinek or exploring resources that help you develop a business mindset, Rand Fishkin’s “Lost and Founder,” case studies from Harvard Business Review.
Additionally, staying connected with the SEO community through conferences, webinars, and podcasts is invaluable for continuous learning and networking.
One thing that applies across all stages is the need to stay updated.
SEO and digital marketing are constantly evolving, and keeping a pulse on the latest Google algorithm updates, industry changes, and new tools is crucial to maintaining a competitive edge.
Beyond reading, mentorship is a powerful tool for career growth. Finding a mentor in your field, or becoming one for others, accelerates learning in ways that books and courses alone cannot.
Teaching and guiding others not only solidifies your own understanding but also deepens your expertise.
Finally, hands-on experience is irreplaceable. No amount of reading or watching tutorials can substitute for real-world application.
Create your own projects, build websites, do your own affiliate content, and test different strategies.
Experimenting firsthand is the best way to learn what works and, just as importantly, what doesn’t.
Ultimately, it’s the combination of learning, mentorship, and practical application that will propel your career forward.
Tom Critchlow: I mean, I’m biased, but I think a lot of the SEO MBA archives are relevant!
In particular, the SEO skills maturity matrix is my most popular all-time post and looks at career progression, specifically balancing the “hard” and “soft” skills you need as you grow.
