Ankur Anand, group CIO at recruitment firm Nash Squared, wants to use technology to help create the key brand for candidates seeking new employment opportunities. While the potential for transformation is significant, Anand recognises it’s a far from straightforward task.
“This is not an easy job,” he says. “This role is all about thinking how you can use your experience and keep yourself up to pace with the advancement that is happening in the technology world. As CIO, you must ensure you understand market insights, so that you know what’s happening in the business, what’s happening in the industry, and then continue to push your stakeholders on future-proofing your organisation.”
Anand joined global technology and talent solutions provider Nash Squared in June 2023, having spent five-and-a-half years at Manpower Group, where he rose to regional CIO and head of transformation for Europe. Before this role, he spent 15 years at financial services giant Citi, where he held a range of senior technology positions. Anand says he’s seen broad changes to digital leadership during his career.
“The role of CIO has expanded so much, and it’s becoming more and more complex. On one hand, you have to deliver what you believe is important for the organisation, which is business-as-usual IT activity. Equally, you have to look at upskilling your people and reskilling them in new areas,” he says.
“You also need to ensure that you know how to focus on meeting the requirements from the board, which are quite challenging at this time, because everybody’s thinking about AI, but not necessarily thinking about what they want to get out of AI. I’ve met a lot of CIOs at Gartner sessions and other events, and everyone seems to be in the same boat.”
Exploiting technology
Anand says his responsibilities at Nash Squared cover two main areas. One is ensuring the firm’s day-to-day technology runs smoothly, which he describes as table stakes for any CIO. The second and more important part is using technology to help the business grow.
“I ensure that technology removes friction,” he says. “We continue to grow the business by using technology because data can have a big impact in our industry. I need to have constant conversations with the business and peers in the industry to understand what’s happening and ensure our technology is ahead of the curve.”
Success isn’t just about implementing technology. Anand says Nash Squared is, first and foremost, a people business. The systems and services he introduces will only have an impact if employees understand how to use these tools effectively.
Being CIO, therefore, also involves a strong cultural change component. Bringing people and technology together is a responsibility that Anand relishes.
“It’s a very broad role,” he says. “On one hand, you are thinking about the business and focusing on how to improve the top line. On the other hand, you have cost pressures. You need to ensure that the bottom line continues to improve. However, as a business in a competitive industry, we must push the value curve far ahead compared to what it used to be five, 10 or 15 years back.”
Anand says the general direction of travel for digital transformation is to deliver value from the firm’s explorations into emerging technology. When he joined the company, the firm didn’t have a CIO. He replaced a chief technology officer (CTO) who focused on keeping the lights on and ensuring the business had basic operational systems. There was an opportunity to do more with digital and data, which led to the creation of the CIO role.
“I started to think about how we could get value from technology,” he says. “I came in, looked at the cost base and optimised operations to ensure that the delivery model was right. We were using some expensive consultants but not driving value from them because we were keeping the lights on. I ensured we had the people with the right skill sets. And through that process, I started shaping the technology strategy.”
Transforming operations
One of the main objectives was to integrate systems and data sets. Nash Squared is an acquisitive business with a long history of purchases. Anand wanted to create a single view of customer data.
“The priority was to get the data journey right across the organisation,” he says. “That was quite an interesting learning period for our business. The insights they started getting as a result of that process were shaping how we would run the company in the future.”
“We continue to grow the business by using technology because data can have a big impact in our industry”
Ankur Anand, Nash Squared
With the right enterprise data foundations in place, Anand considered the culture of change, which meant bringing people along as his team introduced new technology. He says effective change management processes start with buy-in from people at the top of the business. “We had a good experience in this area,” he adds.
While one of the key components for business change is AI, Anand has proceeded with care. “I don’t want to jump into the bandwagon of AI, because there are so many shiny tools, and recruitment is always talked about as one industry where AI is going to do wonders,” he says.
Across the organisation, Anand and his team worked with their line-of-business peers to think carefully about what people wanted to achieve with AI. The core of the approach is improving the day-to-day experiences of employees and job-seeking candidates. He says AI and data are used to improve the company’s understanding of jobseekers’ requirements – and this process involves answering some questions.
“What information do we capture through our day-to-day interactions? How can we convert these day-to-day interactions into structured data? And how do we get that structured data into our people’s hands, so that, whether they are screening a candidate or matching them to a role, AI can enable our staff to make better decisions. The work has been about AI becoming an enabler for our people, rather than automating the jobs of our staff.”
Exploiting data
Anand describes the enterprise application of AI as an ongoing journey: “You finish one project, and then you look at, ‘Where can I do more?’”
The company has focused so far on creating a clear segmentation of its candidate and client base. This segmentation supports a deeper awareness of the requirements of the people who use Nash Squared services. With this insight, Anand says people who work for the recruiter can tighten their understanding of the labour market and new leads and opportunities.
“One important area is data augmentation and enhancement, where we are converting everyday conversations into data,” he says. “Take interpretive insights, which are basically a combination of our own technology, our machine-learning algorithms, along with other AI algorithms. We are also deploying a telephony solution that doesn’t just transcribe conversations but actually converts that discussion into insights.”
Anand explains how these technologies join up. He gives the example of a recruiter talking to a contractor about their skills, industry experiences and anticipated daily rates. The details in these conversations are translated into metadata, pushed into the company’s Bullhorn CRM platform, and used to develop deeper candidate insights to match candidates with job openings.
[Our] work has been about AI becoming an enabler for our people, rather than automating the jobs of our staff Ankur Anand, Nash Squared
Alongside these foundations, the company is developing its own proprietary AI services, semantic models and intellectual property, including via graph technologies. Anand says his team continues to explore ways to use AI to help the firm’s consultants work productively and effectively. Fresh developments are already on the horizon.
“We are also working with a partner who has an existing product that they are enhancing for us,” he says. “It’s an AI pre-screening tool, which we can send out to all our candidates, and, through that, we can identify what skills they have. Then we can think about how we market these candidates. Basically, we’ll have all the metadata needed to manage the talent pool.”
Across all areas of emerging technology, Anand says his team has strived to reduce potential issues.
“These are machine-learning models that are pretty much based on our learning and testing,” he says. “We took an enormous amount of time to ensure the right level of accuracy, so that we can remove bias and hallucinations.”
Developing the brand
Anand explains the direction of travel for Nash Squared, which owns tech recruiter Harvey Nash, over the next two years: “If I can grow my business three times or four times to what we are today, with the same technology, that’s what I want to achieve for our business.”
He says the aim is to create a trusted brand for candidates, which is known for using digital and data to place candidates as quickly and effectively as possible. Anand says this brand should know its candidates’ skills “inside and out” yet recognises it’s a challenging aim.
“That’s a very difficult capability for our business to achieve,” he says. “Many recruitment companies have candidates, but they don’t know much about them. For our employees, the advantages are related to speed – speed gives them the ability to do more for candidates and to earn more, so it makes them happy as well.”
However, if the company gets its approach right, Anand says there’s an opportunity to become the first brand people think of when they consider a tech-enabled recruiter that can help them find the right job opportunities. Just as Uber has used technology to become synonymous with taxi journeys, so Anand wants to create similar name recognition.
“I want to make Harvey Nash the same kind of brand in recruitment,” he says. “So, when someone is looking for a technology contracting job or a permanent IT job, the first name they think of is Harvey Nash.”
OpenAI is throwing its support behind an Illinois state bill that would shield AI labs from liability in cases where AI models are used to cause serious societal harms, such as death or serious injury of 100 or more people or at least $1 billion in property damage.
The effort seems to mark a shift in OpenAI’s legislative strategy. Until now, OpenAI has largely played defense, opposing bills that could have made AI labs liable for their technology’s harms. Several AI policy experts tell WIRED that SB 3444—which could set a new standard for the industry—is a more extreme measure than bills OpenAI has supported in the past.
The bill would shield frontier AI developers from liability for “critical harms” caused by their frontier models as long as they did not intentionally or recklessly cause such an incident, and have published safety, security, and transparency reports on their website. It defines a frontier model as any AI model trained using more than $100 million in computational costs, which likely could apply to America’s largest AI labs, like OpenAI, Google, xAI, Anthropic, and Meta.
“We support approaches like this because they focus on what matters most: Reducing the risk of serious harm from the most advanced AI systems while still allowing this technology to get into the hands of the people and businesses—small and big—of Illinois,” said OpenAI spokesperson Jamie Radice in an emailed statement. “They also help avoid a patchwork of state-by-state rules and move toward clearer, more consistent national standards.”
Under its definition of critical harms, the bill lists a few common areas of concern for the AI industry, such as a bad actor using AI to create a chemical, biological, radiological, or nuclear weapon. If an AI model engages in conduct on its own that, if committed by a human, would constitute a criminal offense and leads to those extreme outcomes, that would also be a critical harm. If an AI model were to commit any of these actions under SB 3444, the AI lab behind the model may not be held liable, so long as it wasn’t intentional and they published their reports.
Federal and state legislatures in the US have yet to pass any laws specifically determining whether AI model developers, like OpenAI, could be liable for these types of harm caused by their technology. But as AI labs continue to release more powerful AI models that raise novel safety and cybersecurity challenges, such as Anthropic’s Claude Mythos, these questions feel increasingly prescient.
In her testimony supporting SB 3444, a member of OpenAI’s Global Affairs team, Caitlin Niedermeyer, also argued in favor of a federal framework for AI regulation. Niedermeyer struck a message that’s consistent with the Trump administration’s crackdown on state AI safety laws, claiming it’s important to avoid “a patchwork of inconsistent state requirements that could create friction without meaningfully improving safety.” This is also consistent with the broader view of Silicon Valley in recent years, which has generally argued that it’s paramount for AI legislation to not hamper America’s position in the global AI race. While SB 3444 is itself a state-level safety law, Niedermeyer argued that those can be effective if they “reinforce a path toward harmonization with federal systems.”
“At OpenAI, we believe the North Star for frontier regulation should be the safe deployment of the most advanced models in a way that also preserves US leadership in innovation,” Niedermeyer said.
Scott Wisor, policy director for the Secure AI project, tells WIRED he believes this bill has a slim chance of passing, given Illinois’ reputation for aggressively regulating technology. “We polled people in Illinois, asking whether they think AI companies should be exempt from liability, and 90 percent of people oppose it. There’s no reason existing AI companies should be facing reduced liability,” Wisor says.
Governments around the world have been struggling to address the rise of industrial-scale scamming operations based in countries like Laos, Myanmar, and Cambodia that have cost victims billions of dollars over the past few years. The operations often have ties to Chinese organized crime, use forced labor to carry out the actual scamming, and rely on vast money laundering networks to collect a profit. They have become so widespread and ingrained in the region that even major international law enforcement collaborationstargeting individual scam centers or kingpins haven’t been able to stem the tide.
The FBI said this week that “cyber-enabled” scam complaints from Americans totaled more than $17.7 billion in reported losses last year—likely a major undercount of the real total, given that many victims don’t report their experiences. Some US officials say that a major barrier to comprehensively addressing the issue is the lack of collaboration with Chinese authorities. China’s efforts to address industrial scamming, they argue, appear aimed at reducing the number of Chinese citizens being impacted rather than comprehensively stopping the activity to protect all victims around the world.
“To its credit, China has cracked down on these operations, but it has done so selectively, largely turning a blind eye to scam centers victimizing foreigners,” Reva Price, a member of the US-China Economic and Security Review Commission said at a Senate hearing last month. “As a result, the Chinese criminal syndicates have been incentivized to shift toward targeting Americans.”
According to research the commission published in March, Beijing’s selective strategy has helped embolden some Chinese scammers, even those working within China, to continue operating so long as they exclusively target foreigners.
Other US-based researchers have come to similar conclusions. From 2023 to 2024, China reported a 30 percent decrease in the amount of money its citizens lost to scams, while the US suffered a more than 40 percent increase, according to congressional testimony last year by Jason Tower, who was then the Myanmar country director for the US Institute of Peace’s Program on Transnational Crime and Security in Southeast Asia. In response to Beijing’s enforcement dynamics, Tower said at the time, “the scam syndicates are increasingly pivoting to target the rest of the world, and especially Americans.”
The United Nations Office on Drugs and Crime noted last year that scam centers have been diversifying their worker pools, shifting from predominantly trafficking Chinese nationals and other Chinese speakers to entrapping people from a broader array of countries and backgrounds who speak various languages. UN researchers attributed this change in part to attackers broadening their targets to include different populations around the world. But they added that the dynamic also seemed to be a reaction to Chinese enforcement and Beijing’s efforts to protect Chinese citizens.
“China is doing more to fight fraud—like orders of magnitude more—than any other country,” says Gary Warner, a longtime digital scams researcher and director of intelligence at the cybersecurity firm DarkTower. “But I would agree that the crackdown by China on people scamming China has squeezed the balloon so to speak and led to more international and American targeting.”
The Chinese government has spent years investing in national safety campaigns warning citizens about the threat of scams and how to avoid falling victim to them. Some of the public discourse attempts to appeal to a sense of national solidarity. There’s a common meme in China, 中国人不骗中国人, literally, “Chinese people don’t deceive Chinese people” that is used to signal trust when swapping restaurant recommendations or job leads. In the context of digital scams, a variant has emerged: “Chinese don’t scam Chinese.”
Standing inside the HumanX conference in San Francisco’s Moscone Center, it’s hard not to feel like you’re at the center of the AI universe. Technology leaders swarm the building, and the headquarters of OpenAI and Anthropic are just down the block. But a 70-person startup headquartered 5,000 miles away in Germany’s Black Forest—a region famous for its ham—has become a top competitor to Silicon Valley’s leading labs in AI image generation.
In December, Black Forest Labs raised funds at a $3.25 billion valuation, after signing deals to power AI image-generation features in Adobe and the graphic design platform Canva. It has even struck agreements with major AI labs like Microsoft, Meta, and xAI to power similar features in their products.
Nearly two years after launch, Black Forest Labs can afford to be picky about who it works with. In 2024, Elon Musk’s xAI tapped Black Forest Labs to power Grok’s first image generator. That partnership put Black Forest Labs on the map but generated a lot of controversy due to the chatbot’s limited safeguards. It ended months later when xAI developed an in-house AI image model.
In recent months, xAI approached Black Forest Labs about licensing the startup’s technology again, sources familiar with the matter tell WIRED. This time around, Black Forest Labs declined, the sources said, deeming it too operationally difficult to partner with xAI, which has a famously chaotic work environment. xAI did not immediately respond to WIRED’s request for comment.
In September, Black Forest Labs struck a $140 million multiyear deal to give Meta access to its AI image-generation technology.
These AI labs want to work with Black Forest Labs because its image generators are among the world’s best, ranking just below OpenAI and Google’s offerings on the third-party firm Artificial Analysis’ benchmarks. The startup also offers some of the most downloaded text-to-image models on Hugging Face, indicating that a lot of AI image tools on the market are likely powered by a free version of Black Forest Labs’ technology.
It’s particularly impressive since the company has historically had far fewer resources than its competitors. This has led it to a more efficient line of research called latent diffusion, which is essentially when an AI model first sketches out a rough blueprint of an image, and then paints in more detail.
Latent diffusion “enabled us to put out very powerful models that took orders of magnitude less resources than our competitor’s models,” said cofounder Andreas Blattmann in an interview with WIRED onstage at HumanX this week.
Despite its success, Black Forest Labs believes image generation is just the beginning. Blattmann said the startup plans to unveil a robot powered by one of its AI models later this year. (He did not reveal what company is making the hardware.) The push is part of a larger opportunity the company sees to build AI that can perceive and take actions in the physical world.
“Visual intelligence is so much more than content creation. Content creation is just the first segue into this entire technology,” said Blattmann. “What I’m personally super excited about—and that’s a pattern throughout this conference—is physical AI.”
Black Forest Labs is also in talks with a handful of hardware companies, to power features in products like smart glasses and robots, sources tell WIRED.
Building in the Black Forest
Blattmann and his cofounders, Robin Rombach and Patrick Esser, made a name for themselves publishing some groundbreaking research on AI image models in 2021. In 2022, they were hired by Stability AI and released Stable Diffusion, a popular open source AI image generator based on their prior research. But two years later, they announced their departure and launched Black Forest Labs.
Rather than move to San Francisco, the trio decided to maintain a headquarters near their hometowns in Freiburg, Germany. Blattmann said the decision has been key to the company’s success.
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