AI Whiz Raises $1B to Build ‘Safe’ Superintelligence

OpenAI co-founder Ilya Sutskever’s new venture, Safe Superintelligence (SSI), has secured a cool $1 billion in funding, valuing the startup at a whopping $5 billion. With backing from tech heavyweights like Andreessen Horowitz and Sequoia, SSI is gearing up for a “straight shot to safe superintelligence,” prioritizing R&D over rapid commercialization. This move follows Sutskever’s dramatic OpenAI exit, where boardroom drama “diminished” his role and dissolved his AI safety team.

AI Drug Discovery Faces Reality Check in Clinical Trial

AI is also reshaping drug discovery, but recent clinical trials reveal both its promise and challenges. Recursion Pharmaceuticals, a self-described “TechBio company,” recently announced mixed results from its AI-driven Phase 2 SYCAMORE trial for REC-994, targeting a rare brain disorder. While meeting safety endpoints, efficacy results were inconclusive, highlighting AI’s potential and limitations in pharmaceutical research.

“While artificial intelligence excels at analyzing vast datasets, the scarcity of information on uncommon neurological conditions poses a major challenge,”  Keaun Amani, CEO of Neurosnap, told PYMNTS.

AI Chatbots Speak Your Language, But Can They Close the Deal?

Tech giants are racing to develop multilingual AI chatbots for cross-border eCommerce, with Google’s Gemini and other projects like OpenBuddy and Cohere’s Aya 23 leading the charge. These AI assistants aim to break down language barriers, potentially shifting how businesses engage with international customers.

“Multilingual AI chatbots hold significant potential for SMBs looking to expand into international markets,” Tim Peters, CMO of Enghouse Systems, said. However, experts cautioned that while AI excels at general communication, it may stumble on nuanced cultural contexts and complex negotiations.

AI Strikes Gold in Data Rush, Mining Insights

In today’s digital gold rush, AI-powered data mining is the high-tech prospector, sifting through vast information reserves to unearth valuable insights. Unlike cryptocurrency mining, this process extracts business intelligence from big data, revolutionizing industries and driving corporate decision-making. Companies leverage these advanced techniques to predict consumer behavior and optimize operations, gaining a sharp competitive edge.

Digital ID Breakthrough: Humans Get AI-Proof Badges

Researchers from OpenAI and MIT have unveiled “personhood credentials,” a system to prove you’re human online without sacrificing privacy. This cryptographic solution creates unique digital badges, allowing users to verify their humanity while keeping personal details under wraps. As AI renders traditional verification tools like CAPTCHAs obsolete, this innovation could drive eCommerce trust, social media authenticity and fraud prevention.

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Safe Superintelligence (SSI), co-founded by former OpenAI chief scientist Ilya Sutskever, has secured this massive investment with only 10 employees. The company, launched in June by Sutskever along with Daniel Gross, a former Y Combinator partner who previously led AI efforts at Apple, and Daniel Levy, a former colleague of Sutskever’s at OpenAI, is focusing on developing artificial general intelligence (AGI) with an emphasis on safety.

“At the end of the day, it’s all about increasing profits, reducing losses, and mitigating risk. In many use cases where AI can model the problem or historical data, it can provide significant benefits,” Shoab Khan, chancellor of the Sir Syed CASE Institute of Technology, told PYMNTS.

The funding round saw participation from NFDG, a venture capital firm run by Gross and Nat Friedman, alongside tech investment heavyweights Andreessen Horowitz, Sequoia Capital, DST Global and SV Angel. This substantial investment in such a young company underscores the growing interest and high stakes in the race to develop advanced AI systems.

A Billion-Dollar Bet on Safety-Focused AGI

The company plans to use the funds partly for hiring, seeking to assemble what it calls “a lean, cracked team of the world’s best engineers and researchers.”

The 37-year-old Sutskever brings considerable experience to the venture. After completing his Ph.D. under renowned AI academic Geoffrey Hinton at the University of Toronto, he joined Google in 2013 before co-founding OpenAI in 2015. His departure from OpenAI followed a tumultuous period involving him in CEO Sam Altman’s brief ousting.

While SSI has not yet partnered with any cloud providers or chipmakers, a significant portion of the investment is earmarked for building up computing power. Sutskever has indicated that SSI’s approach to scaling will differ from that of OpenAI, though specifics remain undisclosed.

The focus on safety in AI development comes at a time of increasing discourse about advanced AI systems’ potential risks and rewards. Sutskever’s experience leading a safety team at OpenAI that oversaw AI’s existential risks may inform SSI’s approach, although that team was disbanded shortly after his departure.

Balancing Potential and Limitations

According to Khan, AI in commerce has limitations: “This depends on accurately modeling data probability distribution. In cases where data doesn’t follow a clear distribution or depends on many factors — some of which are difficult to measure, such as predicting bitcoin prices — AI’s effectiveness is limited.”

Despite challenges, there is optimism about AI’s potential in business. “I see substantial advantages for investors in supporting AI for decision-making in commerce by building complex models, incorporating all relevant factors and data, and reshaping the role of human oversight and trust,” Khan said.

Companies pushing the boundaries of AI capabilities increase the potential for transforming business practices. The substantial investment in SSI and similar ventures signals a growing recognition of the potential transformative power of advanced AI systems in the business world.

The $5 billion valuation of SSI, a company just three months old, reflects the high expectations and potential that investors see in advanced AI technologies. This valuation puts SSI in the upper echelons of AI startups, competing with more established players.

Research and development efforts at SSI are just beginning, and the broader implications for commerce and industry remain to be seen. The company’s focus on safety in AGI development could set new standards for the industry, potentially influencing how other companies approach AI development and implementation.

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Recursion Pharmaceuticals, a self-described “clinical stage TechBio company,” recently announced results from its Phase 2 SYCAMORE trial for REC-994, a drug candidate targeting cerebral cavernous malformation (CCM), a rare brain disorder. The trial met its primary safety endpoint but showed mixed efficacy results, illustrating the complexities of translating AI-driven discoveries into clinical success.

The company’s Recursion OS platform uses machine-learning algorithms to analyze vast datasets, aiming to identify new drug candidates more efficiently than traditional methods. This approach represents a growing trend in the pharmaceutical industry to leverage AI in drug discovery. It also shows the challenges.

“While artificial intelligence excels at analyzing vast datasets, the scarcity of information on uncommon neurological conditions poses a major challenge,” Keaun Amani, CEO of Neurosnap, an AI platform used by labs, told PYMNTS. “Limited patient populations make it difficult to gather sufficient data for training accurate AI models.”

AI’s Growing Role in Drug Discovery

Progress in AI-driven drug discovery is evident. Alister Campbell, VP of science and technology at Dotmatics, told PYMNTS that since 2015, AI-native biotechnology companies and their partners have brought 75 candidates to clinical trials, with numbers growing yearly.

“AI use in drug discovery comes in many shapes and forms, from drug repurposing to predicting structures of anti-bodies and proteins using algorithms like AlphaFold, designing small molecule drugs using generative AI methods, using AI to mine vast OMIC datasets providing valuable insights into disease biology, druggable targets, and biomarkers,” Campbell said.

Jo Varshney, founder and CEO of AI drug discovery company VeriSIM Life, told PYMNTS: “Neurological conditions often lack clear, easily measurable indicators in lab tests or clinical assessments, resulting in a data scarcity that limits the effectiveness of AI systems.”

Recursion’s SYCAMORE clinical trial for CCM, which affects approximately 360,000 symptomatic individuals in the U.S. and EU, illustrates these challenges. Dr. Najat Khan, chief R&D officer at Recursion, noted “promising trends in exploratory efficacy endpoints,” particularly at the highest dose. However, the company acknowledged that “improvements in either patient or physician-reported outcomes were not yet seen at the 12 month time point.”

The trial’s outcome reflects broader industry challenges. According to Amani, “Mixed results in clinical studies reveal that while AI has great potential to revolutionize drug discovery, it still faces significant hurdles in accurately predicting drug efficacy. One major challenge is the complexity of biological systems, which AI models often struggle to fully capture.”

Navigating the Path Forward

Experts suggest various approaches to advance AI in drug discovery. Amani envisions developing more complex models capable of analyzing larger biological systems. He suggests “developing all-atom models capable of analyzing larger, more complex biological systems. These models, combined with a growing trend of blending machine learning and physics-based methods, offer the potential to simulate molecular interactions with unprecedented accuracy.”

Campbell proposes combining AI with traditional techniques to identify relevant biological targets and develop drug candidates more efficiently. He suggests a multi-pronged approach to identify clinically relevant biological targets, develop ideal candidates more quickly and cheaply, and reduce the chances of failure due to safety, efficacy and cost issues.

Accessibility of AI tools is also crucial. Amani notes that platforms like Neurosnap have streamlined the process, making it easier for scientists to use these tools. “Accessing AI-based tools for drug discovery can often be technically prohibitive to researchers,” Amani said. “Platforms like Neurosnap have greatly streamlined this process making it easier for scientists to efficiently utilize the tools they need.”

Varshney said developing more sophisticated “knowledge” or mechanistic systems that intricately incorporate detailed aspects of biology could yield more accurate and reliable predictions when integrated with AI.

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The executives have discussed higher prices in early internal talks regarding these LLMs, Reuters reported Thursday (Sept. 5), citing a paywalled article by The Information that is based on an unnamed source.

OpenAI did not immediately reply to PYMNTS’ request for comment.

The company’s ChatGPT Plus currently costs $20 a month, and hundreds of millions of users access the model’s free tier, according to the report.

It was reported Aug. 27 that OpenAI aims to release Strawberry, its next-level artificial intelligence (AI) product, in the fall.

Strawberry will be able to solve problems and tasks that are beyond the capabilities of current AI models. It will be able to solve math problems it has never encountered; perform high-level tasks like developing market strategies and solving complex word puzzles; and perform “deep research.”

This news comes at a time when OpenAI is reportedly considering changing its corporate structure to make that structure simpler and more attractive to financial backers.

The company currently issues investors equity from its for-profit subsidiary, which is governed by its non-profit board whose “principal beneficiary is humanity, not OpenAI investors.”

Responding to claims about the company changing its corporate structure, OpenAI told the Financial Times: “We remain focused on building AI that benefits everyone and as we’ve previously shared we’re working with our board to ensure that we’re best positioned to succeed in our mission. The non-profit is core to our mission and will continue to exist.”

It has also been reported that OpenAI aims to raise “several billion dollars” in a funding round that would value it at above $100 billion.

When employees sold shares in late 2023, the company was valued at $86 billion.

OpenAI’s ChatGPT remains the market leader in the AI space with hundreds of millions of monthly users at a time when there is fierce competition among companies offering AI tools.

Potential investors in the company’s upcoming funding round reportedly include Apple, Nvidia, Microsoft and Thrive Capital, which is said to be leading the round.

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The tech giant on Thursday (Sept. 5) announced it is expanding the artificial intelligence (AI)-powered offering to cover dresses, one of its most-searched apparel categories.

“This feature is made possible thanks to a generative AI technology we created specifically for virtual try-on (VTO), which uses a technique based on diffusion,” the company wrote on its blog. “Diffusion lets us generate every pixel from scratch to produce high-quality, realistic images of tops and blouses on models.”

However, testing the diffusion technique for dresses uncovered a pair of challenges. For one, dresses in many cases feature highly detailed designs. Google compared it to trying to paint an image on a small canvas.

“You can think of our VTO challenge in the same way: Our existing VTO AI model successfully diffused using low-resolution images, but in our testing with dresses, this approach often resulted in the loss of a dress’s critical details — and simply switching to high-resolution didn’t help,” the company said.

To address this, Google researchers employed a “progressive training strategy” for VTO, where diffusion starts with lower-resolution images and gradually trains in higher resolutions to reflect finer details.

In addition, because dresses cover more of a person’s body than tops, Google found that “erasing” and “replacing” the dress on a person would “smudge” their features or obscure important details of their body. Google remedied this problem with a new technique called the VTO-UNet Diffusion Transformer, “which isolates and preserves a person’s important features.”

The news comes as other major players in the eCommerce world are stepping up their virtual try-on programs. For example, Walmart in July released its first annual “Adaptive Retail Report,” featuring results from a survey that looked at consumer interest in technologies such as virtual try-ons and other tools to see how items would appear in the real world.

“This focus on virtual try-on and visualization technologies suggests that Walmart may be looking to step up its existing presence in the space, with its beauty, apparel and accessories digital trial offerings,” PYMNTS wrote at the time.

And as covered here earlier in the year, virtual try-on can help shoppers make sure they get their clothing purchases right the first time, thus eliminating a headache for retailers.

“Virtual try-on helps to reduce returns because you get the best product, which you like [the most],” Wayne Liu, president and chief growth officer at AI and augmented reality (AR) beauty technology company Perfect Corp., told PYMNTS in February.

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A  PYMNTS Intelligence report, “How Generative AI Is Boosting Innovation for Carmakers and Drivers,” examines generative AI’s advancements, along with substantial hurdles, ranging from technical skill shortages to ethical concerns.

Accelerating Innovation in Automotive Design

Generative artificial intelligence (AI) is emerging as a key driver of innovation in automotive design and development. The technology allows engineers to quickly explore numerous design variations, optimizing everything from vehicle aesthetics to performance attributes. This shift is supported by compelling industry data.

Consider 93% of automotive stakeholders agree that generative AI will significantly impact the industry and 75% plan to integrate it into their operations within the year. The generative AI market in the automotive sector is projected to surge from $335 million in 2023 to $2.6 billion by 2033, reflecting a compound annual growth rate (CAGR) of 23%. This anticipated growth underscores a commitment to the technology among research and development departments, with nearly 70% of decision-makers prioritizing its adoption.

In practical terms, generative AI enhances efficiency by automating design iterations and virtual testing. For instance, one German supplier reported a 70% increase in productivity for test vector generation due to AI integration. Additionally, generative AI has driven a 20% to 30% increase in efficiency for testing processes by automating reporting and scenario simulations. These improvements not only speed up product development but also help in meeting stringent regulatory requirements more effectively.

Enhancing the Driver Experience Through AI

Generative AI is transforming the in-car experience by personalizing interactions and anticipating driver needs. This technology is set to redefine how drivers engage with their vehicles, moving beyond traditional performance metrics to include highly tailored user experiences.

General Motors (GM) has spearheaded efforts to integrate generative AI into in-vehicle systems, leveraging Microsoft Azure and OpenAI technologies to develop a chatbot designed to assist with real-time vehicle issues. This system will provide drivers with step-by-step instructions for common problems and potentially schedule maintenance, all through natural language interactions. Similarly, Cerence and Nvidia are collaborating on an automotive-specific large language model (LLM) to facilitate more intuitive human-vehicle communication.

Audi is also making strides by integrating Cerence’s Chat Pro, powered by ChatGPT, across its product lineup to enhance conversational interfaces. Stellantis plans to extend its use of generative AI to voice assistance systems in 17 countries, with capabilities spanning 12 languages. These advancements promise to revolutionize driver-vehicle relationships by making interactions more responsive and contextually aware, potentially leading to vehicles that learn from individual driving behaviors to improve safety and efficiency.

Overcoming Obstacles in AI Adoption

Despite the promising prospects, the road to widespread generative AI adoption in the automotive industry is fraught with challenges. Key obstacles include a shortage of skilled professionals, complex regulatory environments, and significant ethical concerns.

According to the report, 63% of automotive industry stakeholders identify a lack of skilled staff as a major barrier to implementing generative AI. The need for expertise in both automotive engineering and advanced AI technologies is critical, yet finding and retaining such talent remains difficult. Integrating AI with existing legacy systems poses its own set of challenges, requiring both technical adjustments and cultural shifts within organizations.

Ethical and data privacy issues are also paramount. Ensuring that generative AI systems are secure, protect user privacy and adhere to regulatory standards is crucial, given the sensitive nature of vehicle data and the safety-critical applications of the technology. Addressing these concerns is essential for the industry to fully realize the benefits of generative AI and avoid potential pitfalls.

As the automotive industry accelerates toward a future driven by generative AI, it must navigate these technical and ethical challenges. The road ahead is set to transform vehicle design, manufacturing and user experience, creating new benchmarks for innovation and personalization in the automotive industry.

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The Redwood City, California, company said revenue grew 21% year over year to $87.2 million in its fiscal first quarter ending July 31, beating analyst expectations. Subscription revenue, which is the bulk of C3.AI’s business, increased 20% to $73.5 million.

“We’re off to a solid start for fiscal year 25,” said CEO Thomas Siebel on the company’s earnings call. “This quarter marked our sixth consecutive quarter of accelerating revenue growth, reflecting our high levels of customer satisfaction and increasing demand for enterprise AI applications.”

C3.AI, which provides AI software for large organizations across industries like energy, manufacturing and financial services, has positioned itself as a pioneer in the enterprise AI market. The company was founded in 2009, long before the current AI boom, with a vision of developing a software platform to help organizations leverage cloud computing, the Internet of Things, big data and predictive analytics.

“C3.AI is the original enterprise AI, hard stop,” Siebel declared. “We invested thousands of person-years over a decade building the C3 AI platform, the first reference architecture platform for enterprise AI.”

The company offers over 90 prebuilt enterprise AI applications addressing various industry-specific use cases. Siebel said C3.AI is seeing “incredible results” and high customer satisfaction scores as businesses derive tangible value from its AI solutions.

Notably, C3.AI reported strong traction in the public sector, signing 25 agreements with state and local government agencies in the quarter. “We just fell into a gold mine there,” Siebel said, adding that the public sector opportunity was largely unanticipated.

The company closed 71 agreements in Q1, including 52 new pilot projects — a 117% year-over-year increase in pilot count. C3.AI’s model typically involves starting with a three- to six-month paid pilot before converting to a full production contract.

Siebel highlighted success stories with customers like Shell, which he said has over 100 C3.AI applications in development or deployment and estimates $2 billion in annual benefits from the partnership. Another client, Con Edison, is projecting over $3 billion in benefits over 20 years from its smart grid project with C3.AI.

The company is also seeing strong demand for its new generative AI offerings, launched earlier this year. In the quarter, C3.AI signed 40 agreements related to generative AI with cloud partner Google. 

“Our generative AI business is surprisingly diverse, with many candidly unanticipated use cases across the board in a wide range of industries,” Siebel noted.

C3.AI maintained its full-year revenue guidance of $370-390 million, implying 19-27% growth. The company’s stock has been volatile this year amid the broader AI frenzy as investors debate the long-term winners in enterprise AI.

C3.AI faces competition from major cloud providers and other enterprise software vendors rushing to embed AI capabilities. But Siebel argued that many legacy software companies are simply “rebranding their 20th-century software stacks with AI on the box” without truly re-architecting for AI.

He positioned C3.AI’s prebuilt AI applications as a key differentiator, allowing customers to quickly configure and deploy proven solutions rather than undertaking long, complex development projects.

“In this current cacophony of AI market hype, C3.AI is achieving among the highest levels of customer satisfaction for value realized in the enterprise software world,” Siebel claimed.

The company still posted a non-GAAP operating loss of $16.6 million in Q1 as it invests heavily in growth. But Siebel said profitability is now “simply a function of scale” as revenue growth outpaces expense growth.

C3.AI projected it would be cash flow positive in Q4 and for the full fiscal year 2025. The company ended Q1 with $762.5 million in cash and investments.

Looking ahead, Siebel said C3.AI is focused on expanding sales capacity and growing in North America, Europe, and the public sector. However, he cautioned that modeling the business remains challenging given the rapidly evolving AI landscape.

“It’s very difficult for people who are assessing these companies to really understand the complexity of what’s going on in the AI market,” Siebel said. “This is not a simple business, where in this new world of AI, it’s really complex.”

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The system now includes GenOS AI Workbench, a “dedicated development environment for end-to-end application development,” along with enhancements to GenStudio, GenRuntime, and GenUX components, the company said Wednesday (Sept. 4).

“Intuit’s proprietary GenOS is the key to unlocking new opportunities to fuel consumer and small and mid-market business success with GenAI,” Alex Balazs, Intuit’s chief technology officer, said in a news release.

“Over the past year, we’ve increased our pace of innovation by enabling product teams to turn new ideas into live customer experiments in just days, and built out our GenOS to speed time-to-market for ideas that rise to the top.”

According to the release, Intuit introduced GenOS last year and has since continued to invest in the platform, with the company’s software developers, product managers, data scientists, machine learning engineers, and data analysts experimenting with new uses.

“For example, Intuit’s GenOS enabled new capabilities with easy-to-understand explanations of tax calculations, backed by real-time accuracy checks, with Intuit Assist for TurboTax, boosting confidence for millions of individual tax filers this tax season,” the release said.

Intuit, whose other products include QuickBooks, CreditKarma and MailChimp, announced in July it was cutting 1,800 jobs — 10% of its workforce. The company also said in a securities filing about the layoffs that it plans to “hire a nearly equivalent number of employees” during the next year to support its growth areas. 

And in a message to employees included in the company’s securities filing, CEO Sasan Goodarzi said the cuts come as Intuit is increasing its investments in AI.

“We were early to bet on and invest in AI, building one of the largest AI-driven expert platforms to fuel the success of consumers, small and mid-market businesses, and important partners like accountants, financial institutions and marketing agencies who rely on us daily to prosper,” Goodarzi wrote. 

“With the introduction of GenAI, we are now delivering even more compelling customer experiences, increasing monetization potential and driving efficiencies in how the work gets done within Intuit.”

Earlier this year, the company unveiled its new AI-powered “revenue intelligence” technology, which employs what it called “always-on” predictive and generative AI models.

“Intuit’s move is the latest example of a technology company tapping AI to try to make its products more useful and attract customers,” PYMNTS wrote at the time. “AI has been one of the hottest areas of technology investment in recent years as startups and big companies alike race to capitalize on its potential to automate tasks and provide new insights from data.”

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The planned projects include data centers, turbines and generators, and semiconductor manufacturing, Bloomberg reported Wednesday (Sept. 4), citing unnamed sources.

The scope of the planned investment was reported earlier, but details are coming to light, according to the report.

Asked by Bloomberg about the company’s plans for infrastructure spending, an OpenAI spokesperson said that the company believes such facilities are critical for advanced AI and making it more widely available.

“We are exploring opportunities with this goal in mind and look forward to sharing more details at a later date,” the spokesperson said, per the report.

OpenAI has been working to form a coalition of global investors, including other private companies, and to get the U.S. government’s blessing for this effort, according to the report.

Sam Altman, CEO of OpenAI, has said that other companies and other countries allied with the U.S. would benefit from the building of additional AI infrastructure, per the report.

At the same time, the U.S. could have national security concerns regarding investors that have ties to China, the report said.

It was reported in February that Altman was seeking approval from the U.S. government for an initiative aimed at enhancing the global manufacturing of AI chips.

While Altman was already actively engaging with potential investors and partners in the U.S., Middle East and Asia, he emphasized the importance of obtaining approval from Washington before proceeding.

Altman aims to collaborate with the U.S. government on matters of approvals, timing and the structure of the venture.

In April, it was reported that Altman was meeting with government and industry officials in several countries in an effort to support the building of AI infrastructure that would meet the need for chips, energy and data centers.

OpenAI believe that one of the biggest challenges facing the tech industry is the amount of energy it takes to power AI systems.

At that time, Altman had spoken with official in several Western countries and in the United Arab Emirates (UAE).

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Google’s recent expansion of its Gemini AI in India exemplifies the industry’s focus on multilingual capabilities. It’s one of many companies trying to allow chatbots to communicate in different languages.

“Multilingual AI chatbots hold significant potential for SMBs looking to expand into international markets,” Tim Peters, CMO of Enghouse Systems, told PYMNTS. “By providing real-time, accurate translations, these chatbots can help businesses overcome language barriers, making it easier to connect with customers globally.”

Google’s AI assistant, Gemini, has expanded its reach in India by launching a mobile app and multilingual support for nine Indian languages. The move, catering to the country’s mobile-first culture, allows users to interact with Gemini through text, voice or images. The enhanced Gemini Advanced features capabilities like document uploads and data analysis, enabling users to summarize lengthy documents and analyze complex datasets. 

AI’s Promise for Global Trade

Gemini is one of many multilingual chatbots that are under development. The OpenBuddy project has launched a multilingual chatbot aimed at global users. Built on established models like Falcon and LLaMA, OpenBuddy offers a range of AI assistants, from 3 billion to 70 billion parameters, capable of conversing in multiple languages, including English, Chinese and several European and Asian languages. The project emphasizes offline capability and device-level operation and is freely available on popular AI platforms such as HuggingFace and ModelScope. 

Cohere recently introduced Aya 23, a new set of multilingual large language models (LLMs) available in 23 languages. Developed by its nonprofit division, Cohere for AI, Aya 23 comes in two versions — 8 billion and 35 billion parameters — and is designed to democratize AI by supporting diverse languages and communities.

These models outperform others in tasks like summarization and translation and are more accessible to developers thanks to their open weights. Aya 23 represents a shift toward inclusivity in multilingual AI, offering broader language support and better performance across various natural language tasks.

Such multilingual chatbots could allow businesses to engage with customers in their native languages, breaking down communication barriers that have traditionally limited market expansion, Philip Alves, founder and CEO of DevSquad, a software development company, told PYMNTS. 

“By providing 24/7 multilingual support, SMBs can enhance their global presence without the need for large, multilingual teams,” he said.

While the potential benefits are substantial, experts caution about AI’s limitations in nuanced communication. Peters said, “While AI-powered translation can be effective for general communication, there are inherent challenges in using these tools for sensitive business negotiations or complex customer service interactions.” To ensure accuracy and cultural sensitivity, he suggested businesses consider involving human translators or customer service representatives in critical conversations.

Alves elaborated on these concerns: “For me, the biggest concern is the nuance and cultural context that AI may not fully grasp. Misinterpretations in these contexts could lead to misunderstandings or even damage business relationships.” This highlights the need for businesses to develop strategies that combine AI efficiency with human expertise, especially in high-stakes scenarios.

Human Touch in AI Communication

As multilingual AI chatbots continue to evolve, they promise to lower entry barriers for businesses venturing into international markets. However, as these industry experts suggest, success in global eCommerce will likely depend on a balanced approach that leverages AI’s efficiency while preserving the irreplaceable elements of human communication and cultural understanding.

The job market impact of this technology is also a subject of debate. Alves suggested a shift rather than a wholesale replacement: “While chatbots may handle routine inquiries and transactions, there will still be a demand for human translators and customer service reps, particularly for more complex or sensitive interactions.” 

Peters added that there may be “an increased demand for higher-skilled roles that involve training, managing and refining these AI systems,” pointing to potential new career opportunities in the AI-augmented workforce.

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