AI Business

OpenAI has reached a significant milestone in its revenue growth, with the company’s enterprise business now generating more income than its consumer-facing ChatGPT service. This shift was revealed by OpenAI’s Chief Financial Officer (CFO) Sarah Friar during a meeting with investors on Friday. According to those present at the gathering, Friar stated that the enterprise segment has accelerated faster than anticipated and has surpassed the 60-40 revenue split between the two business areas.

The majority of OpenAI’s revenue now comes from its AI tools for businesses, which have seen significant growth in recent months. This is a notable achievement, as just earlier this year, Friar had indicated that parity between the enterprise and consumer segments was expected by the end of 2026. However, it appears that the company has exceeded its own projections.

OpenAI’s annualized revenue run rate stands at $40 billion, with the enterprise segment showing a particularly strong performance. In July alone, the company saw a 20% increase in its monthly revenue growth, while business customers experienced an even more substantial surge of 32%. These numbers demonstrate the growing demand for OpenAI’s AI tools and services among businesses.

The meeting was held despite recent turmoil within OpenAI’s leadership team, which has seen several high-profile departures. Revenue chief Denise Dresser stepped down just eight months into her tenure, citing a desire to pursue other opportunities. Longtime executive Brad Lightcap also announced his departure after an eight-year stint at the company.

However, it seems that these changes have not disrupted OpenAI’s momentum. President and co-founder Greg Brockman attended the meeting with investors and expressed gratitude for Dresser’s contributions in building the enterprise foundation. He also welcomed Dali Rajic, who has taken over as operating chief from Dresser, and praised her experience at cybersecurity company Wiz.

The executives addressed questions about the rise of open-source Chinese models and their potential impact on OpenAI’s business. Brockman downplayed concerns, suggesting that there is a misconception surrounding the cost-effectiveness of these models. He emphasized that OpenAI’s focus remains on delivering high-quality AI tools and services to its customers.

Friar highlighted the company’s efforts to adapt to changing customer behavior, particularly in regards to tokenmaxxing – a practice where companies allowed employees to accumulate large AI bills without demonstrating sufficient output. She noted that enterprise customers have shifted their focus towards cost per unit of intelligence, with OpenAI’s newest model showing significant improvements in efficiency.

Additionally, Friar discussed the growth of advertising on ChatGPT, which is approaching a $1 billion run rate. The company began testing ads within its service in February and has seen promising results. Overall, OpenAI appears to be making strides in various areas, including revenue growth, customer adoption, and innovation.

Temporary staffing agencies are set to benefit from a significant boost in efficiency, thanks to the acquisition of Pixi by zvoove. The Munich-based software and AI solution provider has acquired the intellectual property behind Pixi, an AI-powered onboarding and support agent that automates administrative work for temporary workers.

Pixi uses WhatsApp as its primary channel, allowing employees to complete their own onboarding and manage day-to-day administrative questions directly through a chat interface. This streamlined process captures master data without requiring recruiter involvement, freeing up staff time for more high-value tasks.

The acquisition adds another powerful AI agent to zvoove’s platform, which already provides software solutions for the temporary staffing, cleaning services, and personal security industries. With Pixi on board, customers can expect a significant reduction in administrative workloads, allowing them to focus on building stronger relationships with clients and candidates.

Pixi’s capabilities are centered around natural-language processing, enabling it to understand and respond to employee queries based on customer-specific ERP data. This includes handling personal details, contracts, assignments, and working hours – all without the need for manual intervention or paper forms.

The acquisition marks a significant step forward in zvoove’s mission to digitize and optimize processes for its customers. By automating administrative work through AI-powered tools like Pixi, staffing agencies can reduce costs, increase efficiency, and improve profitability.

According to Oliver Muhr, CEO of zvoove Group, the addition of Pixi will have a direct impact on customer productivity: ‘Every process that runs without a phone call or paper form translates directly into time saved and expanded margin.’ This sentiment is echoed by Felix Huemer, CEO of zvoove Switzerland, who notes that Pixi’s automated onboarding capabilities will free up staff to focus on more human-centric tasks.

zvoove Group has established itself as the leading provider of software and AI solutions for its industry. With over 6,500 customers across Europe and Latin America, it manages a significant portion of the temporary workforce – approximately 3 million workers, with annual payroll exceeding EUR 24 billion. The company’s platform also handles over 3 million applications per year.

The acquisition is set to bring Pixi to zvoove’s customer base in Switzerland, marking an important milestone for the company and its customers. With this move, zvoove continues to push the boundaries of AI-powered automation in staffing agencies, streamlining processes and driving efficiency across the industry.

Paul Redding, head of MSP partnerships at NinjaOne, recently addressed a room full of managed service providers (MSPs) at The Channel Company’s XChange August event in Washington, D.C. His message was clear: the channel’s economics have shifted, and MSPs are facing a profitability paradox. Despite bringing in more revenue, rising security, compliance, tooling, and customer demands are eating into profits and making growth harder to monetize.

The complexity of environments has grown exponentially as MSPs add more technology, tools, and applications. However, they haven’t been able to pass on the increased costs to their customers. Redding noted that nobody is comfortable raising prices by 30 percent or more, which would be necessary to offset these rising expenses. This paradox is a pressing concern for MSPs looking to maintain profitability.

One of the main drivers behind this shift in economics is the proliferation of separate tools and interfaces. NinjaOne aims to eliminate this complexity by consolidating technology stacks and maximizing built-in automation. Redding emphasized that every single agent added increases risk factors, requires additional clicks, multifactor authentication, and time – all of which can be detrimental to profit margins.

Automation is a crucial aspect of addressing the profitability paradox. By automating services, MSPs can streamline their operations, reduce costs, and improve efficiency. This allows them to focus on high-value tasks rather than getting bogged down in manual processes. Redding stressed that specialization is also key – by focusing on specific vertical markets, MSPs can develop deep expertise and build stronger relationships with customers.

Gabriel Sierra, founder of Consultiply, agreed that many MSPs are looking to consolidate technology stacks and maximize built-in automation rather than layering multiple platforms onto the same customer environment. He noted that having one customer with five different tools to achieve a single goal is unproductive and inefficient. Redding’s message resonated with Sierra, who emphasized the importance of reducing the number of systems technicians must manage and making better use of existing automation within platforms.

Redding also highlighted the role of artificial intelligence (AI) as an efficiency tool rather than an immediate replacement for technicians. He sees AI as a means to improve productivity, reduce costs, and enhance customer satisfaction – ultimately leading to increased revenue growth. The ultimate goal is for NinjaOne to become a partner in MSP growth rather than just another vendor.

The difference between a vendor and a partner lies in their approach to business relationships. A vendor sells products or services without considering the long-term implications, whereas a partner takes a more holistic view – mitigating risk, improving efficiency, and growing revenue. Redding’s vision for NinjaOne is to become a trusted partner that helps MSPs make more money by streamlining operations, reducing costs, and increasing customer satisfaction.

In conclusion, the profitability paradox facing MSPs requires a comprehensive approach that addresses automation, specialization, and centralization. By consolidating technology stacks, maximizing built-in automation, and focusing on specific vertical markets, MSPs can reclaim their growth trajectory and maintain profitability in an increasingly complex environment.

Google has announced that its artificial intelligence (AI) assistant app, Gemini, has reached a significant milestone with over one billion monthly active users. This marks the 14th Google product to hit this mark, according to CEO Sundar Pichai’s announcement via X.

The rapid growth of Gemini is notable, especially when compared to other AI-powered chatbots like OpenAI’s ChatGPT, which also reached one billion monthly active users in June. However, it’s worth noting that today’s figure specifically refers to the standalone Gemini app and does not include users from other channels or Google’s AI Mode in Search.

Google has been steadily integrating Gemini across its various products, including Search, Workspace, Android, and its standalone app. The company’s efforts have paid off, with over 1 billion monthly active users globally using AI Mode in Search alone. However, the new milestone specifically highlights the growth of the Gemini app itself.

The company continues to roll out new models and features for Gemini, including the latest version, Gemini 3.5 Flash. This update is designed to improve coding and autonomous AI-agent tasks, further enhancing the capabilities of the chatbot.

According to Google’s data, a significant portion of users are taking advantage of the voice feature when interacting with Gemini, with 63% using this method directly. Additionally, the app generates over 150 million images every day, making it a key player in AI-generated content.

The growth of Gemini is not limited to Google’s own products; the company has also seen significant adoption on iOS, with more than 100 million active users. This milestone comes shortly after Google’s Q2 2026 earnings call, where the company celebrated having over 950 million monthly users and saw daily active users triple in the past year.

The announcement precedes Google’s upcoming Made by Google event, which is expected to showcase new features powered by Gemini across Pixel devices.

Google has announced significant changes to its artificial intelligence (AI) efforts, marking a new chapter in the company’s momentum. The updates come from Google and Alphabet CEO Sundar Pichai, who shared them with Google DeepMind teams. Among the key developments are new roles for Demis Hassabis and Koray Kavukcuoglu, both of whom have been instrumental in driving AI advancements at Google.

Sundar Pichai highlighted the company’s remarkable progress in delivering its full AI stack. With an impressive array of talent, world-class compute resources, and products that bring AI to a wide audience, Google has established itself as a leader in the field. The Gemini models have been particularly successful, with high demand from developers and businesses alike. Additionally, the Gemini app has reached 950 million+ monthly users, demonstrating its widespread adoption.

The company’s AI research continues to yield groundbreaking breakthroughs, such as last week’s advancements in Gemini Robotics. However, Sundar emphasized that there is still much work to be done to accelerate progress and stay at the forefront of AI development. This requires a focused effort on the AI frontier, where Google must continue to innovate and push boundaries.

One key aspect of this focus is shaping the future of Artificial General Intelligence (AGI) and science. Demis Hassabis has been instrumental in driving this agenda, having described AGI as being within reach. Sundar Pichai announced that Demis will take on a new role as Chair of Google DeepMind and Chief Scientist of Alphabet, allowing him to devote his full attention to shaping the future of AGI.

Demis Hassabis expressed his excitement about this development in an internal message to employees. He emphasized that humanity is at a pivotal moment in history, with AI poised to bring about significant advancements. However, he also stressed the importance of getting the next steps right to ensure that these developments benefit humanity as a whole.

In his new role, Demis will continue to work closely with Sundar on strategic and global AGI matters. He will also advise Koray Kavukcuoglu, who has been appointed SVP of Google DeepMind, in addition to his existing role as Chief AI Architect of Google. This transition marks a significant shift for the company, with Demis handing over day-to-day operational responsibilities at GDM.

Koray’s appointment is seen as a natural progression given his extensive experience and expertise within the company. He has been instrumental in driving breakthroughs such as WaveNet and DQN, and will now oversee Gemini model development, Frontier AI research, and the Gemini app and developer teams. Sundar Pichai expressed confidence in Koray’s ability to lead GDM into its next chapter.

Another significant development is Jeff Dean’s decision to step back from his role at Google. After 27 years of service, he has chosen to pursue new opportunities, including launching an independent public benefit corporation with Sanjay Ghemawat. This initiative aims to accelerate discoveries in machine learning (ML), science, and engineering.

Google will continue to collaborate with Jeff and Sanjay on a research framework for ML systems and related infrastructure advances. Sundar Pichai praised their contributions to the company’s success, highlighting their role in driving significant technology transitions from early search infrastructure to modern AI era neural networks.

The updates mark an exciting new chapter for Google’s AI efforts. With its comprehensive stack of AI tools for businesses, including Gemini models that are in high demand among developers and businesses, the company is well-positioned to lead in this space. Sundar Pichai emphasized that with these changes, Google will continue to drive momentum and stay focused on the AI frontier.

Demis Hassabis reflected on GDM’s unique culture, which has been instrumental in its success. He thanked employees for their dedication and effort, highlighting the company’s achievements so far. With a strong foundation in place, Demis expressed confidence that the best is yet to come for Google DeepMind.

Sundar Pichai concluded by emphasizing the dynamic nature of this moment in AI history. With significant opportunities ahead, he encouraged continued focus on driving momentum and staying at the forefront of AI development.

When Mark Zuckerberg talks about the future, he envisions a world where everyone has access to personal digital helpers that can understand their goals and work tirelessly to achieve them. These agents will live in messaging platforms like WhatsApp and Messenger, which are already serving as users’ primary interface with Meta AI. Right now, around one million businesses have adopted Meta’s business AI agents on these platforms.

Meta isn’t alone in this vision. Google recently highlighted the importance of custom AI agents in its Search update, while Anthropic’s Claude has seen a surge in subscriptions due to its agentic coding tool, Claude Code. Engineers praise Claude for making it easier to build and train advanced models that can truly assist users.

The company is putting its money where its mouth is, with massive investments in infrastructure. This includes the development of a $14 billion data center located in El Paso, Texas, which Meta is building in collaboration with BlackRock. The sheer scale of this investment underscores just how much Meta believes in AI’s potential to drive revenue.

Zuckerberg thinks that personalized agents will be the foundation for his next wave of products and revenue lines – but also acknowledges there’s a significant financial risk involved. He points to Reality Labs’ substantial losses as an example of what could go wrong if this strategy doesn’t pan out.

The success or failure of Meta’s AI strategy depends on user adoption and how well its models perform. If the company can deliver agents that genuinely assist with daily tasks – from scheduling meetings to managing finances – it could revolutionize the way billions interact with technology. However, this will require significant investment in computational power to train and run advanced models.

Meta isn’t alone in pursuing AI-driven services; rivals are also betting on personalized digital helpers as a core part of everyday communication. The market for AI assistants has the potential to become multi-trillion-dollar, making it an attractive opportunity for companies like Meta and Google. But with significant financial risk involved, this bet will only pay off if these investments succeed.

The strategy hinges on Meta’s massive messaging user base – WhatsApp alone reaches billions of people globally. If Zuckerberg’s prediction holds, chat platforms could turn into revenue-generating hubs for both consumers and businesses. The company is counting on its AI agents to make communication more efficient by automating tasks that currently consume users’ time.

Palantir Technologies (NASDAQ:PLTR) has been at the center of a recent stock market swing, with some investors dismissing it as ‘just an LLM wrapper’ after a sharp decline. However, a closer look at the company’s financials reveals that its underlying business remains strong.

The stock price surge in 2025 was nothing short of meteoric, increasing by roughly 135% and peaking near $207 per share in November. This valuation placed Palantir on par with companies like Amazon and Google, with a price-to-sales ratio nearing 110. However, this year has been less kind to the stock, with shares plummeting around 40% from that peak.

But what’s striking is that despite the decline in stock value, Palantir’s underlying numbers haven’t slipped. The company’s earnings releases and SEC filings paint a picture of a business that continues to grow its revenue, improve margins, and retain customers at an impressive rate.

The ‘wrapper’ critique suggests that Palantir’s AI Platform (AIP) is model-agnostic, plugging into OpenAI, Anthropic, and open-source models rather than building its own frontier LLM. However, this misses the point of what truly sets Palantir apart from other AI tools for business – its Ontology.

Palantir’s Ontology is a decision framework that ties enterprise data, business logic, operational rules, security controls, and real-world actions into a single governed system. This allows it to answer questions like ‘what should we do next?’ rather than just providing historical analysis. It’s this capability that has governments, manufacturers, healthcare companies, and industrial customers expanding their spending with Palantir.

UBS analysts have also pushed back on the idea that Palantir’s platform is easily copied, citing its data integration and governance capabilities as advantages current frontier models don’t offer on their own. While competition in AI tools for business is indeed intensifying, with Databricks, Snowflake (NASDAQ:SNOW), hyperscalers, and AI labs racing to build similar enterprise tools, Palantir’s edge isn’t guaranteed forever.

However, there’s an interesting twist – commoditized foundation models may actually help Palantir’s case. As AI itself becomes cheap and interchangeable, value shifts to whoever can securely connect it to messy enterprise systems, compliance needs, and real-world workflows – exactly what Palantir has spent two decades building.

The stock remains expensive, trading around 130-140 times trailing earnings with a price-to-sales ratio of 56. This leaves little room for missteps, but investors should be cautious not to let short-term sentiment dictate their view of the company’s fundamentals.

Ultimately, smart investors shouldn’t let a stock chart determine how they view a business. Prices influence sentiment far more than fundamentals do in the short run. Over the long run, however, earnings growth, customer adoption, and competitive execution usually have the final say – and those remain the numbers worth watching.

A former Microsoft engineering leader and founder of his own data and AI consulting firm has a clear vision for the future of artificial intelligence. Rob Collie spent 13 years at Microsoft, where he was one of the founding engineers behind Power BI, a platform that helps clients visualize data and infuse insights into other Microsoft apps. He’s now written a book about organizational AI strategy called ‘Fair Game,’ which will be published on August 11.

After years of studying how companies approach AI, Collie has come to a stark conclusion: most businesses are doing it wrong. They’re not just buying more AI-powered software or rushing to build their own large language models; they’re trying to use these tools without adapting them to their specific needs and workflows. This is the ‘shallow end’ of AI adoption, according to Collie.

The problem with off-the-shelf AI solutions is that they don’t automatically understand a company’s internal processes, strategy, or institutional knowledge. They need context, which can be provided by connecting them to business data, workflows, and software. This approach allows companies to generate significant returns without having to train their own models from scratch.

Collie likens commercial large language models (LLMs) to Lego bricks: they’re powerful tools that can be combined in various ways to create something new and useful. However, instead of building a new model, businesses should focus on customizing existing ones by feeding them the right context at the right time. This is where the real opportunity lies.

But who are the people best suited to lead this effort? Collie identifies ‘crafters’ as key players in AI adoption. These individuals are not necessarily software developers or data professionals but rather problem-solvers embedded within businesses. They’re often responsible for automating tedious work, creating dashboards, and writing scripts – tasks that require a deep understanding of the company’s operations.

Collie estimates that about 1 in 16 people fit this ‘crafter’ profile, which is more than the number of professional software developers. These individuals are not limited by their technical skills; AI can now help them write real software and tackle complex problems that IT teams never had time for. By combining these crafters with AI tools, businesses can unlock significant value.

When looking for AI leaders, Collie suggests asking questions like ‘Who’s the Excel guru?’ or ‘Who keeps inventing clever solutions to make the business run?’ These individuals are already thinking like builders and have a deep understanding of their company’s operations. They’re not necessarily experts in AI but know how to use it effectively.

Collie also emphasizes that companies shouldn’t wait for a top-down AI strategy before getting started. Organizations operate on thousands of individual workflows, which cannot be redesigned from headquarters alone. Successful businesses will start with small customization wins close to the business and learn from those successes as they expand their efforts.

In conclusion, Collie’s vision for AI adoption is centered around customization and empowering crafters within organizations. By focusing on adapting existing models to specific needs and workflows, companies can unlock significant returns without having to build their own large language models from scratch.

The award-winning team behind Google DeepMind’s groundbreaking protein structure prediction tool, AlphaFold, has been disbanded. The move follows a significant shift in the company’s research strategy, with most key members reassigned and several others leaving the organization altogether.

In recent months, nearly a quarter of the full-time researchers involved in the project have departed from Google DeepMind. This includes Nobel laureate John Jumper, who announced his departure for Anthropic in June, along with two AlphaFold colleagues who followed him to the rival research lab.

The reorganization is part of a broader shift at Google DeepMind, which has been redirecting its resources towards Gemini-focused projects. According to Pushmeet Kohli, vice president of research at Google DeepMind, ‘Our strategy over the last nine years has been to focus on grand challenges… The strategy has evolved.’

AlphaFold’s impact cannot be overstated. Developed by Google DeepMind in 2018 and recognized as a solution to the protein folding problem in 2020, the tool predicts the three-dimensional structure of proteins from their amino acid sequences in minutes rather than years. This capability has revolutionized fields such as drug discovery, vaccine development, and research into neurodegenerative diseases like Alzheimer’s and Parkinson’s.

The AlphaFold team took advantage of decades’ worth of accumulated work on protein structures to train its AI model. By leveraging this existing knowledge, the researchers were able to create a tool that could predict the structure of proteins with unprecedented accuracy. The resulting database has been made available for free to academic researchers worldwide, providing them with access to over 200 million predicted structures.

The Nobel Prize in Chemistry awarded to Demis Hassabis and John Jumper in 2024 marked a significant milestone for AlphaFold’s creators. However, the recognition also seems to have had an unexpected consequence: several key team members are now leaving Google DeepMind or being reassigned within the company.

According to one DeepMind employee, ‘The three departing researchers were core members whose exits caused surprise inside the company.’ They have landed at Anthropic, a rival research lab that has recently opened a workbench focused on biology and drug discovery. This development raises questions about what kind of projects are now being prioritized by Google DeepMind.

The shift towards Gemini-focused projects is part of a broader effort to automate parts of the scientific process itself. By building systems powered by Gemini, researchers aim to assist scientists broadly and eventually streamline their work. However, this new strategy has also led to concerns about the industry’s emphasis on short-term results over long-term research goals.

The reorganization at Google DeepMind is not without controversy. Some see it as a natural consequence of AlphaFold having achieved its goal: solving the protein folding problem to a useful standard and making the database public and permanent. Others, however, view it as a sign that the industry now rewards short-term gains over sustained research efforts.

The outcome has significant implications for researchers weighing their options in the field. With nearly a quarter of AlphaFold’s original authors leaving Google DeepMind, many are left wondering what kind of projects will be prioritized by the company going forward.

Warren D’Souza, Ph.D., M.B.A., a renowned expert in artificial intelligence (AI), data science, and digital transformation, has joined UT Southwestern as its first Chief Artificial Intelligence Officer. Dr. D’Souza brings with him a clear vision to establish UTSW as a national leader for the responsible use of AI in academic medicine. He aims to integrate AI into all aspects of the institution’s mission-driven work, from clinical care and research to education and administrative functions.

In his new role, Dr. D’Souza will oversee the development and implementation of AI strategies that prioritize patient safety, data security, and transparency. His goal is to make UT Southwestern a model for other academic medical centers to follow in using AI responsibly. According to Dr. D’Souza, ‘My hope is that UT Southwestern will become a national model for how an academic medical center can use AI responsibly – not as a standalone technology initiative but deeply embedded as a mission-driven capability that advances excellence, innovation, and trust.’

Dr. D’Souza comes from the University of Maryland Medical System, where he served as Senior Vice President and Chief Innovation Officer. He was also co-Director of the University of Maryland Institute for Health Computing and founded a center dedicated to digital health and AI innovation and incubation. In this role, Dr. D’Souza oversaw the integration of AI-enabled tools into clinical and operational workflows, resulting in measurable impact.

Dr. D’Souza’s background is rooted in medical physics, having earned his Ph.D. from the University of Wisconsin – Madison and an M.B.A. from Duke University. He has written over 100 peer-reviewed articles and holds multiple patents related to novel computational approaches in radiation therapy delivery. His extensive experience in AI, data science, and digital transformation makes him an ideal candidate for this position.

Center Times Plus asked Dr. D’Souza about his vision for the future of AI at UT Southwestern and how it will impact the institution’s mission to educate, discover, and heal. When discussing what guided him to a career focused on AI, data science, and digital transformation, Dr. D’Souza explained that he has always been drawn to the wonders of science and technology. He said, ‘That passion led me to be curious about their incredible impact on humans.’

Early in his career as a medical physicist, Dr. D’Souza saw how data, computation approaches, and complex modeling could directly improve patient care and clinical decision-making. Over time, this interest expanded from individual tools and models to broader questions of how digital transformation can help healthcare organizations harness these technologies to become more precise, efficient, and learning-oriented. He was particularly drawn to the opportunity to translate complex technology into practical solutions that could benefit patients, clinicians, researchers, educators, and technical and administrative teams.

Dr. D’Souza acknowledged that he did not fully anticipate how quickly AI would become transformative. ‘I knew AI had extraordinary potential,’ he said, ‘but I do not think many of us fully anticipated how quickly it would become transformative.’ He noted the significant impact of generative AI models, which have made these tools accessible to a broader audience, including students, clinicians, scientists, administrators, and the public.

The pace of adoption has been remarkable, with AI no longer being an abstract future concept but rather powerful technology that can help solve real-world problems. Dr. D’Souza emphasized the importance of human judgment and institutional values in guiding the responsible use of AI. He stated, ‘The greatest challenges are ensuring that AI is deployed responsibly, safely, and equitably while still allowing innovation to flourish.’

In healthcare, decisions made using AI can have a significant impact on clinical decision-making, patient outcomes, trust, and privacy. Dr. D’Souza emphasized the need for strong governance, transparency, evaluation, and commitment to mission in institutions that succeed with AI adoption. He believes that pairing technical excellence with these essential components will be crucial for success.

Dr. D’Souza highlighted some of the most fascinating uses of AI in healthcare, including its ability to help clinicians detect patterns earlier, make better decisions, and reduce daily workloads. AI is already being used to interpret medical images (e.g., CT and MRI), identify patients at risk due to their health issues, support earlier disease diagnosis, evaluate biomedical learner/trainee competency, and optimize administrative functions.

In research, AI is being harnessed to analyze large multimodal datasets, accelerate therapeutic discoveries, generate hypotheses, and uncover relationships that would be difficult to detect manually. Dr. D’Souza emphasized that the most promising applications of AI are not simply automation but rather augmentation of human expertise, enabling teams to act with greater speed, precision, and insight.

Dr. D’Souza believes that AI will help advance UT Southwestern into the future by supporting earlier detection, more personalized treatment, safer workflows, and better patient outcomes in clinical care. In research, it can accelerate discovery by connecting data, prospective therapeutics, and expertise across disciplines in new ways. Additionally, AI has the potential to transform education at UTSW, enabling a more effective teaching, training, and preparation of clinicians, scientists, and health leaders for a technology-enabled world.

Dr. D’Souza’s appointment marks an important step towards integrating AI into all aspects of UT Southwestern’s mission-driven work. He is committed to making the institution a leader in responsible AI adoption, ensuring that these technologies are used safely, effectively, and ethically.

The decision to disband the AlphaFold team is a major change in Google DeepMind’s research priorities. This move will reassign researchers working on the protein-folding project to other scientific initiatives.

Google DeepMind’s shift towards integrating foundational AI across multiple fields indicates a broader push to combine cutting-edge technology with diverse expertise. Alphabet wants its top talent focused on areas like Gemini and applied science, where AI can drive innovation and progress in various disciplines.

The AlphaFold development won’t stop; it will continue as part of larger scientific programs that span areas such as enzyme design, nuclear fusion, genomics, and drug discovery. This integration suggests Alphabet sees potential in applying AI techniques to multiple fields for driving real-world results.

Meanwhile, the reorganization is happening with a stable stock price, currently trading around $336.71, after experiencing a 71.8% gain over the past year. Despite recent fluctuations in shorter-term performance, Alphabet’s share price has gone up 6.8% so far this year.

The reallocation of DeepMind talent points to a closer link between Alphabet’s core AI platforms and long-running scientific efforts. This tighter connection could impact how Alphabet positions Gemini and related tools across healthcare, energy, and research-focused customers, potentially leading to more integrated solutions for large enterprise and government clients.

By integrating the AlphaFold team into broader efforts around Gemini and applied science, Alphabet suggests it values a single, large-scale model platform that can be reused across various workloads. This move also indicates where Alphabet wants its scarce AI talent focused, which could influence how investors view returns from research-driven initiatives compared to product-focused spending.

The reorganization may help Alphabet compete with major players in the field like Microsoft and OpenAI, as well as Meta. These companies are racing to supply general-purpose AI systems that support specialist use cases such as drug discovery and industrial design.

Integrating AlphaFold into larger programs could challenge the idea that highly specialized AI projects remain clearly ring-fenced within Alphabet’s research initiatives. This shift supports the existing narrative that Alphabet is concentrating resources on AI platforms capable of driving user engagement and monetization across Search, YouTube, and Cloud.

This move may affect how investors think about returns from research-driven initiatives compared with more product-focused spending. The reorganization highlights heavy AI infrastructure spending but doesn’t explicitly factor in the execution risk associated with restructuring high-profile research teams while competitors invest heavily in AI-specific research paths.

Reassigning the AlphaFold team introduces an execution risk if restructuring slows scientific progress or reduces Alphabet’s ability to retain leading research talent. Peers like Microsoft and Meta continue building their own AI research groups, which could pose a challenge for Alphabet in retaining top researchers.

A heavier emphasis on Gemini-centric work increases concentration risk if customers prefer specialized models or if competing AI platforms gain share in scientific computing and cloud contracts. However, embedding AlphaFold into wider scientific programs may support better reuse of models across various use cases within Google Cloud’s offerings.

Embedding AlphaFold into broader scientific initiatives could help Alphabet offer more integrated AI solutions to large enterprise and government customers. This shift may influence Google Cloud’s position relative to Amazon Web Services and Microsoft Azure in the market for cloud computing services, particularly when it comes to data analysis tools and AI solutions for businesses.

To understand this shift’s implications, track how Alphabet talks about Gemini usage in science and healthcare during management meetings. It is likely that they will link these projects directly to Google Cloud deals and Isomorphic Labs partnerships as progress unfolds.

The reorganization’s success will depend on customer wins that rely on both Gemini and AlphaFold-style tools. Any data points on this front could serve as crucial indicators of Alphabet’s ability to keep pace with competitors like Microsoft, OpenAI, and Amazon in the area of AI for drug discovery and materials science.

Google DeepMind’s shift towards integrating foundational AI across multiple fields may have far-reaching implications for how Alphabet positions itself in the market. This change is particularly significant when it comes to data analysis tools and AI solutions for businesses that rely on large-scale model platforms like Gemini and AlphaFold.

The reorganization indicates where Alphabet wants its scarce AI talent focused, which could be pivotal in driving user engagement and monetization across various workloads within Search, YouTube, and Cloud. Alphabet’s ability to keep pace with competitors will depend on how effectively it executes this plan.

This shift may help Alphabet understand better what customers need from integrated AI solutions that combine Gemini-like capabilities with AlphaFold-style tools for more specialized applications. This could lead to more streamlined offerings tailored to large enterprise and government clients in the data analysis space, making Alphabet a strong player in the market.

San Francisco-based enterprise AI startup Freehand has secured a significant funding boost, raising $75 million in its latest Series B round. This substantial investment will enable the company to scale its autonomous AI agents, which are designed to manage complex supply chain spend and back-office operations for large enterprises.

The financing was co-led by Battery Ventures and NewRoad Capital Partners, with participation from Nexus Venture Partners and former U.S. Commerce Secretary Penny Pritzker. With this latest capital injection, Freehand has now raised a total of $100 million. While the company declined to reveal its valuation, CEO Nitin Jayakrishnan described it as ‘a significant step up’ from their previous Series A funding round in March 2024.

The deal comes at a time when tariffs, taxes, and immigration policy are straining the traditional outsourcing model that has dominated supply chains for decades. Additionally, there is an uptick in venture funding to supply chain and logistics-related startups, with Crunchbase data indicating that 2026 is on pace to deliver its strongest year since 2022, with $6.2 billion raised by such companies in the first half of this year across 350 deals.

Freehand was founded in February 2024 by Jayakrishnan and Abhijeet Manohar, two enterprise logistics veterans who previously co-founded Pando, a SaaS transportation management system (TMS) and procure-to-pay system. As AI transformation accelerated, the pair stepped away from operational roles at Pando to launch Freehand as an independent entity focused on agentic AI.

Their experience building enterprise supply chain software convinced them that existing back-office paradigms were ripe for disruption. ‘We had been in this fairly archaic industry for the last six to eight years,’ Jayakrishnan noted, adding that instead of trying to catch up with outdated technology, they could leapfrog into a more modern paradigm.

While spend management platforms like Ramp focus on corporate cards and employee travel expenses, Freehand targets complex supply chain operations. Its AI agents manage non-standard spending across logistics, raw materials, parts, and labor by reading contracts, policies, emails, and internal data to verify bills and track operational milestones.

For large global businesses, keeping track of supplier bills is a daunting task, especially when dealing with complex shipping routes like the Red Sea and the Strait of Hormuz. Contracts are detailed, and checking whether large bills match actual work has historically required big back-office teams.

When billing discrepancies arise, Freehand’s AI agents negotiate adjustments directly with suppliers while maintaining strategic vendor relationships. ‘If it is not correct,’ Jayakrishnan explained, ‘then negotiating with the supplier becomes a matter of adjusting the bill to reflect what was actually done.’

By shifting from manual oversight to agentic automation, Freehand believes its technology allows enterprises to reduce their reliance on third-party offshore outsourcing and give internal employees more room to perform higher-value strategic work. The company counts some 50 customers, including Meta, Johnson & Johnson, Pfizer, and Cardinal Health.

Its platform autonomously processes billions in payments across 60 to 70 countries and hundreds of currencies without human supervision. Some benefits of its technology include recovering 5% to 10% of total spend; completing operational workflows 5x to 7x faster; and reducing overall procure-to-pay cycle times by more than 70%. ‘We are, for a lot of companies, their first global rollout of AI deployments at scale that impacts daily transactions and operations,’ Jayakrishnan noted.

Dharmesh Thakker, general partner at Battery Ventures, emphasized the potential for supply chain and logistics management to be significantly improved through automation. He highlighted the ‘enormous room’ for AI-driven efficiency gains in this sector, particularly when it comes to repetitive workflows like freight audits and payments.

NVIDIA, the leading technology company in artificial intelligence (AI), has partnered with the Korea Advanced Institute of Science and Technology (KAIST) to launch a joint research lab focused on developing AI models tailored to South Korea’s industries. The new laboratory will be established at KAIST’s Kim Jaechul Graduate School of AI in Seoul, aiming to advance agentic AI for the country’s future needs.

The collaboration brings together NVIDIA’s expertise in full-stack AI solutions with KAIST’s world-class scientific talent and research infrastructure. This partnership is expected to create a comprehensive academic AI research program that will drive innovation in Korea’s technology sector.

According to Bill Dally, chief scientist and senior vice president of research at NVIDIA, ‘Korea is home to leading AI researchers and has one of the most advanced technology ecosystems worldwide.’ The joint lab aims to provide a foundation for next-generation AI research, accelerating the development of models and agent systems tailored to Korea’s industries, language, and future.

Hyunwoo Kim, an incoming faculty member at KAIST’s Kim Jaechul Graduate School of AI, will lead the joint NVIDIA-KAIST lab upon joining the institution. He emphasized that ‘AI research is entering a new era requiring frontier talent, large-scale infrastructure, and deep collaboration across academia and industry.’ The partnership between NVIDIA and KAIST aims to address these needs by fostering ambitious work in Korea’s AI sector.

The joint lab will be equipped with state-of-the-art computing resources from local NVIDIA Cloud Partners. This infrastructure will provide researchers with direct access to the latest NVIDIA AI technology, enabling them to develop models optimized for the Korean language and specific use cases. The collaboration also includes funding for at least 10 KAIST researchers annually, who will receive internship opportunities at NVIDIA.

Additionally, NVIDIA plans to hire exceptional Korean researchers for full-time positions, creating new pathways for Korea’s top AI talent to pursue ambitious research and build long-term careers in the field. This initiative is expected to deepen global collaboration between academia and industry while attracting and retaining top AI scientists in Korea.

The $300 million partnership will include a significant investment of $50 million per year over an initial five-year period, providing substantial compute contributions for researchers at KAIST. The joint lab’s priorities will focus on developing models optimized for the Korean language and specific use cases using NVIDIA Nemotron open models to advance the country’s AI capabilities.

The collaboration between NVIDIA and KAIST marks a significant step towards accelerating AI innovation in Korea. By establishing this joint research laboratory, both parties aim to create a pipeline from academic discovery to enterprise and national AI deployments, ultimately driving economic growth and technological advancements in South Korea.

Anthony Pompliano, a well-known expert in tech and business development, has pointed out that Meta excels at humanizing work, surpassing many large technology companies. This approach is not only evident in its products but also in how it communicates with users.

Pompliano recalled his time at Facebook when he noticed that product experiences couldn’t quantitatively change user emotions until the message was signed ‘From all of us at Facebook.’ After this humanized touch, emotional metrics began to shift. This approach was then scaled across various products and communications.

Today, Meta’s emphasis on ‘We bet on humanity’ stands in stark contrast to AI model labs that often discuss replacing jobs and controlling digital weapons. As a result, capital and user attention are more easily drawn to communicators capable of emotional connection.

Pompliano has extensive experience with business development at Facebook early in his career, later shifting to cryptocurrency investment and media, founding the Pompliano Company. He is adept at extracting insights from internal practices related to communication and user psychology.

Meta’s strategy involves expanding the humanizing technique of ‘collective signing’ across its entire product line and brand narrative. This approach has been validated through early A/B testing and reduces regulatory and public opinion pressure costs by creating emotional resonance. Additionally, it hedges against technological threat narratives with its ‘bet on humanity’ approach to attract advertisers and retain users.

Similar cases can be seen in Apple’s long-term emphasis on storytelling and designer stories, Nike’s personal narratives of athletes, and OpenAI’s early slogan shifting from a focus on benefiting all humanity to more technical expressions. Currently, tech communication is undergoing a phase of differentiation between AI threat narratives and humanized counter-narratives.

This shift represents a change in pricing power: user attention and brand trust are moving from pure technical capability to emotional connection capability. The mechanism behind this is that collective signing and ‘betting on humanity’ reduce feelings of alienation, making communication itself a key differentiator rather than an accessory.

Artificial intelligence startup Anthropic is gearing up for a potential initial public offering (IPO) later this year, with bankers lining up investor meetings in advance of the share sale. According to an individual familiar with the plans, Anthropic’s IPO preparations are progressing rapidly as its advisors sound out demand from prospective investors before embarking on a formal roadshow.

The meetings suggest that Anthropic is moving closer to listing on public markets, which could happen as soon as October, although the exact timing remains uncertain. This would mark another significant milestone for the AI industry, following June’s massive SpaceX IPO and further opening up the public markets to companies at the forefront of the AI boom.

Anthropic confidentially filed its IPO prospectus with the Securities and Exchange Commission last month but has yet to disclose when it plans to debut on the stock market. The company was founded in 2021 by a group of executives and researchers who defected from OpenAI over concerns about the direction of their former employer, and has since found early success selling its AI assistants to enterprises.

Anthropic’s popular coding assistant, Claude Code, is one key factor behind its rapid growth, with the company closing a $65 billion funding round at a valuation of $965 billion in May. This puts it above OpenAI’s valuation for the first time and marks another significant milestone for the startup, which has been fueling the AI spending boom.

The involvement of top Wall Street banks Goldman Sachs, Morgan Stanley, and JPMorgan Chase in Anthropic’s IPO planning underscores their growing interest in the AI sector. The three firms are among the biggest on Wall Street by revenue, and their participation highlights the significant role that AI is playing in driving growth for financial institutions as they seek to satisfy investors clamoring for ways to fund or invest in this rapidly expanding theme.

Anthropic’s potential IPO would also mark a shift towards greater transparency and accountability within the industry. The company has been at the center of controversy over export controls on its technology, with the Trump administration recently lifting restrictions on certain products. However, it remains unclear how this will impact Anthropic’s future plans or operations.

CNBC’s Jim Cramer has weighed in on the current state of the stock market, dismissing concerns that it is experiencing a bubble similar to the one preceding the dot-com crash. According to Cramer, companies like SpaceX are exceptions rather than representative of the broader market.

While some investors have raised eyebrows at the rapid gains made by semiconductor and AI-related companies, Cramer argues that these outliers do not reflect the overall market conditions. He points out that there is ‘some froth’ in certain areas but emphasizes that this does not accurately represent what drives the market’s performance.

The past year has seen significant stock price increases as enthusiasm for artificial intelligence continues to grow. Companies like Micron and Sandisk have experienced massive gains, with their shares rising by over 243% and 644%, respectively. This surge in prices has led some investors to question whether the market is becoming overheated, drawing comparisons to the dot-com boom of the late 1990s.

Cramer disagrees with this assessment, citing lower interest rates as a key factor contributing to the current market conditions. He also notes that corporate earnings are stronger than they were during the tech bubble and points out that valuations are more reasonable compared to those seen in the past.

The latest consumer price index report came in below expectations on Tuesday, which has eased concerns about potential rate hikes by the Federal Reserve. Cramer believes this development reduces the likelihood of a series of significant interest rate increases, similar to what occurred before the dot-com crash.

New Fed Chair Kevin Warsh’s comments on Tuesday also provided reassurance for investors. According to Cramer, Warsh did not indicate that he would tighten monetary policy if inflation remains at current levels. This suggests that the market is unlikely to experience a sharp correction in the near future.

Cramer also draws attention to valuations of major companies trading at what he considers attractive multiples despite reporting strong results. Bank of America, Goldman Sachs, and JPMorgan all reported substantial earnings and revenue beats on Tuesday, with their shares trading at roughly 12-18 times forward earnings. Cramer’s Charitable Trust owns shares in these companies.

These valuations are significantly lower than those seen during the peak of the dot-com era, when the S&P 500 traded at over 25 times forward earnings. According to FactSet data, this multiple is now around 20, which Cramer considers more reasonable but not cheap by any means.

Cramer also points out that several technology companies are trading at relatively low multiples compared to their historical norms. SK Hynix and Micron have shares priced at roughly four and six times forward earnings estimates for 2027, respectively. Nvidia’s multiple is similar to the broader market despite its dominant position in artificial intelligence.

What characterizes this market, according to Cramer, is the relatively low valuation of many large-cap stocks.

Siri, Apple’s virtual assistant, is set for a major overhaul as the company seeks to dominate the market for personal assistants. The revamped AI will leverage its massive user base and deep integration with personal data stored on devices to compete directly with ChatGPT and other top-tier AI assistants.

The key to Siri’s success lies in its ability to tap into users’ existing ecosystems, providing a seamless experience that rivals those of competitors. According to analysts at eMarketer, Apple’s scale is unmatched, with billions of active devices already in users’ hands. This extensive reach allows the company to infuse personalized AI features designed around its users.

Apple’s approach differs from the general free-for-all model employed by other companies. Instead, it focuses on creating a safe and highly personalized experience that leverages users’ trust. As analyst Gadjo Sevilla noted in a recent episode of ‘Behind the Numbers,’ Apple is not competing directly with ChatGPT but rather offering its own brand of AI features.

The upgraded Siri will be able to handle complex requests like adding photos to emails or showing specific images from user accounts. This level of functionality was demonstrated at Apple’s developer conference last month, where the company showcased the assistant’s capabilities in action. The revamped AI is expected to launch two years after initial promises and will incorporate Google’s Gemini AI model and cloud technology.

Apple’s control over both hardware and software provides opportunities for AI integration that third-party developers can’t replicate. However, this advantage initially extends only to Apple’s own apps and services, potentially limiting functionality with third-party applications like Gmail or WhatsApp. Analyst Grace Harmon noted that users who rely heavily on non-Apple services may find Siri’s capabilities limited.

Scale and trust are the two fundamental strengths positioning Apple favorably in the AI race. As eMarketer analyst Grace Harmon emphasized, ‘The two big things that are really working in Apple’s favor are scale and trust.’ With billions of active devices at its disposal, Apple has an almost unimaginable distribution network for AI features.

Apple is investing heavily in research and development (R&D) to support the revamped Siri. The company is using foundational models developed in combination with Gemini, as well as its own on-device processing capabilities. Analyst Gadjo Sevilla noted that Apple isn’t trying to reinvent the wheel but rather build upon existing AI technology.

The key differentiator for Siri AI lies in its ability to access and process personal information directly on users’ devices. This creates context awareness that competitors struggle to match, allowing Siri to provide a more personalized experience. Analyst Gadjo Sevilla explained that ‘A lot of what Siri AI is going to be able to do for its users securely will be on device.’ The assistant can pull answers from old message threads and extract information from emails without opening any apps.

Apple’s pitch centers around privacy, emphasizing the need for Siri to access messages, mail, and photos. The company claims it handles what it can directly on devices, with anything sent to the cloud used only to answer requests before being deleted. Analyst Grace Harmon noted that personalization is a crucial aspect of AI assistants, allowing users to tap into their existing data.

Despite its advantages, Apple faces several obstacles in the AI assistant race. The company has historically been criticized for delayed investment and limited third-party integration. As analyst Grace Harmon pointed out, ‘For a long time Apple has been kind of over promising, under delivering.’ This lack of urgency may hinder Apple’s ability to keep pace with competitors like Google or Microsoft.

Hardware limitations mean Siri AI won’t be available on all devices initially, potentially creating a fragmented user experience. Early demonstrations also suggest the assistant takes time to process requests, indicating it’s still in development ahead of its September launch. Third-party developers may face challenges accessing Siri AI integrations, which could further limit functionality.

The revamped Siri is set to put fully capable AI within reach of hundreds of millions of people who have never opened ChatGPT. However, users relying heavily on non-Apple services like Gmail or Google Photos may find Siri’s capabilities limited until those apps allow deeper integration.

Anthropic has started adapting its pricing model to suit Indian users, a crucial step in expanding its presence in the country. This move comes as global AI companies increasingly tailor their offerings to win over users in India’s vast market.

In recent days, some users in India have begun seeing local prices on Claude’s website and mobile apps. However, it appears that payments via Unified Payments Interface (UPI), a widely used instant payment network in India, are not yet enabled. Users still need to pay by card or through Apple’s and Google’s app store billing systems.

The introduction of rupee-denominated subscriptions is significant for Indian users, who have long sought this option due to the added friction caused by dollar pricing and currency conversion. This change is particularly notable given that India accounts for 5.8% of global Claude usage, making it the service’s second-largest market after the US.

Anthropic has listed various plans on its website in India, including Claude Pro at ₹2,000 (about $21) a month when billed annually, compared to $17 a month in the US. The prices for other plans, such as Claude Max and Team, also vary significantly from those offered in the US.

The Indian rupee pricing includes local taxes, which may affect the overall cost for users. Interestingly, prices on Claude’s mobile apps differ slightly from those listed on its website.

Anthropic has been expanding its focus on India, with significant developments over the past few months. In February, it opened an office in Bengaluru after announcing plans to do so in October. The company also appointed Irina Ghose, former Microsoft India managing director, as head of its business in the country in January.

Anthropic has partnered with Indian IT services giants Infosys and Tata Consultancy Services in recent months as part of its efforts to scale enterprise AI deployments. However, this expansion faced a setback when Anthropic abruptly suspended access to its Fable 5 and Mythos 5 models for non-US entities in June, prompting some developers and startup founders to consider alternatives.

The restriction on Fable 5 has since been lifted, but access to Mythos 5 remains limited. This incident highlights the challenges AI companies face when expanding into new markets, particularly in a price-sensitive market like India where converting widespread usage into paid subscriptions can be difficult.

Circeus is expanding its software offerings significantly. The company has just announced the acquisition of Encodian, a specialist in document automation for Microsoft Power Platform users.

The acquired business offers its flagship product Flowr, which lets organizations automate complex tasks related to documents directly within the Microsoft ecosystem. This solution appeals to more than 3,000 customers across over 80 different regions worldwide and has established itself as a notable player in this field.

One of Encodian’s most valuable features is that it can be easily purchased and deployed through the Microsoft Marketplace without requiring complicated integration processes. Businesses don’t have to go through unnecessary steps; they can find what they need quickly.

Circeus now finds itself better positioned in enterprise workflow automation, a trend driven by AI becoming increasingly capable of handling complex tasks. However, enterprises still require trusted systems that can execute specific actions such as managing files, applying templates, converting documents, and maintaining auditable workflows.

The company views Encodian’s platform as a key component for enabling its own AI agents to function effectively within the Microsoft environment. This strategic move will give businesses access to advanced automation capabilities directly integrated into their existing environments via Circeus’s centralized AI system.

Encodian has stated that it will continue operating under its current brand and name, with founder Jay Goodison remaining at the helm of the business. The company emphasizes that acquisitions are made for long-term growth rather than short-lived gains.

Circeus plans to support Encodian by providing forward-deployed AI engineering expertise, growth assistance, and shared back-office resources. This partnership will allow the acquired company to expand its offerings even further.

Growth is not new to Circeus; over the past four years alone, it has completed 18 strategic deals. The company serves more than 200,000 businesses globally and has a sizeable investment portfolio of $220 million raised so far.

The significance of Encodian’s platform was underscored by Gian Maria Gramondi, Founder and COO of Circeus: ‘Encodian sits at the point where automation can be integrated into existing workflows in Microsoft 365.’ This insight highlights why integrating automation capabilities within workflows is essential for success.

Jay Goodison expressed his enthusiasm about joining forces with Circeus: ‘Joining together will allow us to keep doing what we do best while gaining access to AI engineering and global reach that would have been challenging on our own. The only change customers can expect is an accelerated pace of product improvements.’

Circeus’s acquisition strategy has proven effective, given its impressive track record over the past four years. By partnering with companies like Encodian, Circeus aims to provide businesses with access to advanced automation capabilities directly integrated into their existing environments.

The acquisition is a strategic move that will give businesses access to more powerful tools for automating tasks within Microsoft 365. This integration of automation capabilities and workflows in one environment is crucial for success.

A major player in the AI landscape is expanding its capabilities with an acquisition that marks a significant shift towards helping businesses deploy and use AI tools effectively. OpenAI’s deployment arm has agreed to acquire Northslope, an applied AI firm specializing in enterprise AI implementation.

The move follows the launch of OpenAI’s deployment company in May, which aimed to assist enterprises in integrating AI into their core business operations. This acquisition is the second focused on enterprise AI use since its inception and underscores a growing trend among AI companies: providing more than just model development services.

Traditionally, consulting firms have handled tasks related to implementing AI solutions within organizations. However, with advancements in AI technology, companies are now betting that helping customers implement these tools will be as crucial as building the models themselves.

The acquisition expands OpenAI’s deployment arm by hundreds of ‘forward deployed engineers’ (FDEs) who work alongside clients to build and integrate AI systems into their operations. These FDEs possess a unique blend of technical expertise and business acumen, enabling them to bridge gaps between teams and employees struggling with AI model usage.

The strategy mirrors that employed by Palantir, which has long embedded engineers directly within customer organizations to develop software tailored to their specific needs. Northslope’s founders come from this background, bringing valuable experience in integrating technology into core operations.

OpenAI is not alone in recognizing the importance of providing comprehensive support for AI tools. Anthropic is building an AI services company aimed at helping mid-sized businesses utilize its Claude model effectively.

The success of OpenAI’s deployment arm will be closely watched as it navigates concerns surrounding AI spend, enterprise IP security, and overall adoption rates among enterprises. The next phase in the AI race may indeed hinge on who can successfully get their tools into widespread use within business operations.