The current phase of disillusionment is not a dead end but rather a recalibration. As enterprises and vendors align expectations with the technology’s capabilities, generative AI can move beyond the hype to deliver sustainable value.
Meta Joins the Race to Reinvent Search with AI Meta, the parent company of Facebook, Instagram, and WhatsApp, is stepping into the evolving AI-driven search landscape. As vendors increasingly embrace generative AI to transform search experiences, Meta aims to challenge Google’s dominance in this space. The company is reportedly developing an AI-powered search engine designed to provide conversational, AI-generated summaries of recent events and news. These summaries would be delivered via Meta’s AI chatbot, supported by a multiyear partnership with Reuters for real-time news insights, according to The Information. AI Search: A Growing Opportunity The push comes as generative AI reshapes search technology across the industry. Google, the long-standing leader, has integrated AI features such as AI Overviews into its search platform, offering users summarized search results, product comparisons, and more. This feature, now available in over 100 countries as of October 2024, signals a shift in traditional search strategies. Similarly, OpenAI, the creator of ChatGPT, has been exploring its own AI search model, SearchGPT, and forging partnerships with media organizations like the Associated Press and Hearst. However, OpenAI faces legal challenges, such as a lawsuit from The New York Times over alleged copyright infringement. Meta’s entry into AI-powered search aligns with a broader trend among tech giants. “It makes sense for Meta to explore this,” said Mark Beccue, an analyst with TechTarget’s Enterprise Strategy Group. He noted that Meta’s approach seems more targeted at consumer engagement than enterprise solutions, particularly appealing to younger audiences who are shifting away from traditional search behaviors. Shifting User Preferences Generational changes in search habits are creating opportunities for new players in the market. Younger users, particularly Gen Z and Gen Alpha, are increasingly turning to platforms like TikTok for lifestyle advice and Amazon for product recommendations, bypassing traditional search engines like Google. “Recent studies show younger generations are no longer using ‘Google’ as a verb,” said Lisa Martin, an analyst with the Futurum Group. “This opens the playing field for competitors like Meta and OpenAI.” Forrester Research corroborates this trend, noting a diversification in search behaviors. “ChatGPT’s popularity has accelerated this shift,” said Nikhil Lai, a Forrester analyst. He added that these changes could challenge Google’s search ad market, with its dominance potentially waning in the years ahead. Meta’s AI Search Potential Meta’s foray into AI search offers an opportunity to enhance user experiences and deepen engagement. Rather than pushing news content into users’ feeds—an approach that has drawn criticism—AI-driven search could empower users to decide what content they see and when they see it. “If implemented thoughtfully, it could transform the user experience and give users more control,” said Martin. This approach could also boost engagement by keeping users within Meta’s ecosystem. The Race for Revenue and Trust While AI-powered search is expected to increase engagement, monetization strategies remain uncertain. Google has yet to monetize its AI Overviews, and OpenAI’s plans for SearchGPT remain unclear. Other vendors, like Perplexity AI, are experimenting with models such as sponsored questions instead of traditional results. Trust remains a critical factor in the evolving search landscape. “Google is still seen as more trustworthy,” Lai noted, with users often returning to Google to verify AI-generated information. Despite the competition, the conversational AI search market lacks a definitive leader. “Google dominated traditional search, but the race for conversational search is far more open-ended,” Lai concluded. Meta’s entry into this competitive space underscores the ongoing evolution of search technology, setting the stage for a reshaped digital landscape driven by AI innovation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
the success of AI in ERP depends on user adoption. If AI agents continue to make implementation easier and enhance current workflows, it’s clear that the future of ERP could indeed be agentic.
Salesforce CEO Marc Benioff Criticizes Microsoft Copilot as “Repackaged ChatGPT” Salesforce CEO Marc Benioff took aim at Microsoft’s Copilot AI offerings during Salesforce’s latest quarterly earnings call, dismissing them as a rebranding of OpenAI’s generative AI technology. “In many ways, it’s just repackaged ChatGPT,” Benioff asserted. He contrasted this with Salesforce’s platform, emphasizing its unique ability to operate an entire business. “You won’t find that capability on Microsoft’s website,” he added. Benioff highlighted Agentforce, Salesforce’s autonomous AI agent product, as a transformative force for both Salesforce and its customers. The tool, available on Salesforce’s support portal, is projected to manage up to half of the company’s annual support case volume. The portal currently handles over 60 million sessions and 2 million support cases annually. Agentforce Adoption and Partner Involvement Salesforce COO Brian Millham outlined the significant role of partners in driving Agentforce adoption. During the quarter, global partners were involved in 75% of Agentforce deals, including nine of Salesforce’s top 10 wins. More than 80,000 system integrators have completed Agentforce training, and numerous independent software vendors (ISVs) and technology partners are developing and selling AI agents. Millham pointed to Accenture as a notable example, leveraging Agentforce to enhance sales operations for its 52,000 global sellers. “Our partners are becoming agent-first enterprises themselves,” Millham said. Since its general availability on October 24, Agentforce has already secured 200 deals, with thousands more in the pipeline. Benioff described the tool as part of a broader shift toward digital labor, claiming, “Salesforce is now the largest supplier of digital labor.” Expanding Use Cases and Market Impact Agentforce, powered by Salesforce’s extensive data repository of 740,000 documents and 200–300 petabytes of information, supports diverse use cases, including resolving customer issues, qualifying leads, closing deals, and optimizing marketing campaigns. Salesforce has committed to hiring 1,000–2,000 additional salespeople to expand Agentforce adoption further. Benioff positioned Salesforce as the leading enterprise AI provider, citing its 2 trillion weekly transactions through its Einstein AI product. He claimed Salesforce’s unified codebase provides a competitive edge, unlike rival systems that run disparate applications, potentially limiting AI effectiveness. “This is a bold leap into the future of work,” Benioff said, “where AI agents collaborate with humans to revolutionize customer interactions.” AI Growth Across Salesforce Products AI-driven growth extended beyond Agentforce to other Salesforce products: Millham noted that AI-related $1 million+ deals more than tripled year over year. Financial Highlights For Q3 FY2024, Salesforce reported: Looking ahead, Salesforce expects Q4 revenue between $9.9 billion and $10.1 billion, representing 7%–9% year-over-year growth. The company raised its full fiscal year revenue guidance to $37.8–$38 billion, an 8%–9% increase. Industry and Product Insights Salesforce’s growth was driven by its core clouds and subscription services, with health, life sciences, manufacturing, and automotive industries performing particularly well. However, retail and consumer goods saw slower growth. While subscription revenue for MuleSoft and Tableau decelerated, Salesforce’s broader portfolio continued to deliver robust performance. Benioff concluded by emphasizing the transformative potential of Salesforce’s AI ecosystem: “This is the next evolution of Salesforce—an intelligent, scalable technology that’s no longer tied to workforce growth.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Could the AI Energy Solution Make AI Unstoppable? The Rise of Brain-Based AI In 2002, Jason Padgett, a furniture salesman from Tacoma, Washington, experienced a life-altering transformation after a traumatic brain injury. Following a violent assault, Padgett began to perceive the world through intricate patterns of geometry and fractals, developing a profound, intuitive grasp of advanced mathematical concepts—despite no formal education in the subject. His extraordinary abilities, emerging from the brain’s adaptation to injury, revealed an essential truth: the human brain’s remarkable capacity for resilience and reorganization. This phenomenon underscores the brain’s reliance on inhibition, a critical mechanism that silences or separates neural processes to conserve energy, clarify signals, and enable complex cognition. Researcher Iain McGilchrist highlights that this ability to step back from immediate stimuli fosters reflection and thoughtful action. Yet this foundational trait—key to the brain’s efficiency and adaptability—is absent from today’s dominant AI models. Current AI systems, like Transformers powering tools such as ChatGPT, lack inhibition. These models rely on probabilistic predictions derived from massive datasets, resulting in inefficiencies and an inability to learn independently. However, the rise of brain-based AI seeks to emulate aspects of inhibition, creating systems that are not only more energy-efficient but also capable of learning from real-world, primary data without constant retraining. The AI Energy Problem Today’s AI landscape is dominated by Transformer models, known for their ability to process vast amounts of secondary data, such as scraped text, images, and videos. While these models have propelled significant advancements, their insatiable demand for computational power has exposed critical flaws. As energy costs rise and infrastructure investment balloons, the industry is beginning to reevaluate its reliance on Transformer models. This shift has sparked interest in brain-inspired AI, which promises sustainable solutions through decentralized, self-learning systems that mimic human cognitive efficiency. What Brain-Based AI Solves Brain-inspired models aim to address three fundamental challenges with current AI systems: The human brain’s ability to build cohesive perceptions from fragmented inputs—like stitching together a clear visual image from saccades and peripheral signals—serves as a blueprint for these models, demonstrating how advanced functionality can emerge from minimal energy expenditure. The Secret to Brain Efficiency: A Thousand Brains Jeff Hawkins, the creator of the Palm Pilot, has dedicated decades to understanding the brain’s neocortex and its potential for AI design. His Thousand Brains Theory of Intelligence posits that the neocortex operates through a universal algorithm, with approximately 150,000 cortical columns functioning as independent processors. These columns identify patterns, sequences, and spatial representations, collaborating to form a cohesive perception of the world. Hawkins’ brain-inspired approach challenges traditional AI paradigms by emphasizing predictive coding and distributed processing, reducing energy demands while enabling real-time learning. Unlike Transformers, which centralize control, brain-based AI uses localized decision-making, creating a more scalable and adaptive system. Is AI in a Bubble? Despite immense investment in AI, the market’s focus remains heavily skewed toward infrastructure rather than applications. NVIDIA’s data centers alone generate 5 billion in annualized revenue, while major AI applications collectively bring in just billion. This imbalance has led to concerns about an AI bubble, reminiscent of the early 2000s dot-com and telecom busts, where overinvestment in infrastructure outpaced actual demand. The sustainability of current AI investments hinges on the viability of new models like brain-based AI. If these systems gain widespread adoption within the next decade, today’s energy-intensive Transformer models may become obsolete, signaling a profound market correction. Controlling Brain-Based AI: A Philosophical Divide The rise of brain-based AI introduces not only technical challenges but also philosophical ones. Scholars like Joscha Bach argue for a reductionist approach, constructing intelligence through mathematical models that approximate complex phenomena. Others advocate for holistic designs, warning that purely rational systems may lack the broader perspective needed to navigate ethical and unpredictable scenarios. This philosophical debate mirrors the physical divide in the human brain: one hemisphere excels in reductionist analysis, while the other integrates holistic perspectives. As AI systems grow increasingly complex, the philosophical framework guiding their development will profoundly shape their behavior—and their impact on society. The future of AI lies in balancing efficiency, adaptability, and ethical design. Whether brain-based models succeed in replacing Transformers will depend not only on their technical advantages but also on our ability to guide their evolution responsibly. As AI inches closer to mimicking human intelligence, the stakes have never been higher. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more
The AI Productivity Paradox: Why Aren’t More Workers Using AI Tooks Like ChatGPT?The Real Barrier Isn’t Technical Skills — It’s Time to Think Despite the transformative potential of tools like ChatGPT, most knowledge workers aren’t utilizing them effectively. Those who do tend to use them for basic tasks like summarization. Less than 5% of ChatGPT’s user base subscribes to the paid Plus version, indicating that a small fraction of potential professional users are tapping into AI for more complex, high-value tasks. Having spent over a decade building AI products at companies such as Google Brain and Shopify Ads, the evolution of AI has been clearly evident. With the advent of ChatGPT, AI has transitioned from being an enhancement for tools like photo organizers to becoming a significant productivity booster for all knowledge workers. Most executives are aware that today’s buzz around AI is more than just hype. They’re eager to make their companies AI-forward, recognizing that it’s now more powerful and user-friendly than ever. Yet, despite this potential and enthusiasm, widespread adoption remains slow. The real issue lies in how organizations approach work itself. Systemic problems are hindering the integration of these tools into the daily workflow. Ultimately, the question executives need to ask isn’t, “How can we use AI to work faster? Or can this feature be built with AI?” but rather, “How can we use AI to create more value? What are the questions we should be asking but aren’t?” Real-world ImpactRecently, large language models (LLMs)—the technology behind tools like ChatGPT—were used to tackle a complex data structuring and analysis task. This task would typically require a cross-functional team of data analysts and content designers, taking a month or more to complete. Here’s what was accomplished in just one day using Google AI Studio: However, the process wasn’t just about pressing a button and letting AI do all the work. It required focused effort, detailed instructions, and multiple iterations. Hours were spent crafting precise prompts, providing feedback, and redirecting the AI when it went off course. In this case, the task was compressed from a month-long process to a single day. While it was mentally exhausting, the result wasn’t just a faster process—it was a fundamentally better and different outcome. The LLMs uncovered nuanced patterns and edge cases within the data that traditional analysis would have missed. The Counterintuitive TruthHere lies the key to understanding the AI productivity paradox: The success in using AI was possible because leadership allowed for a full day dedicated to rethinking data processes with AI as a thought partner. This provided the space for deep, strategic thinking, exploring connections and possibilities that would typically take weeks. However, this quality-focused work is often sacrificed under the pressure to meet deadlines. Ironically, most people don’t have time to figure out how they could save time. This lack of dedicated time for exploration is a luxury many product managers (PMs) can’t afford. Under constant pressure to deliver immediate results, many PMs don’t have even an hour for strategic thinking. For many, the only way to carve out time for this work is by pretending to be sick. This continuous pressure also hinders AI adoption. Developing thorough testing plans or proactively addressing AI-related issues is viewed as a luxury, not a necessity. This creates a counterproductive dynamic: Why use AI to spot issues in documentation if fixing them would delay launch? Why conduct further user research when the direction has already been set from above? Charting a New Course — Investing in PeopleProviding employees time to “figure out AI” isn’t enough; most need training to fully understand how to leverage ChatGPT beyond simple tasks like summarization. Yet the training required is often far less than what people expect. While the market is flooded with AI training programs, many aren’t suitable for most employees. These programs are often time-consuming, overly technical, and not tailored to specific job functions. The best results come from working closely with individuals for brief periods—10 to 15 minutes—to audit their current workflows and identify areas where LLMs could be used to streamline processes. Understanding the technical details behind token prediction isn’t necessary to create effective prompts. It’s also a myth that AI adoption is only for those with technical backgrounds under 40. In fact, attention to detail and a passion for quality work are far better indicators of success. By setting aside biases, companies may discover hidden AI enthusiasts within their ranks. For example, a lawyer in his sixties, after just five minutes of explanation, grasped the potential of LLMs. By tailoring examples to his domain, the technology helped him draft a law review article he had been putting off for months. It’s likely that many companies already have AI enthusiasts—individuals who’ve taken the initiative to explore LLMs in their work. These “LLM whisperers” could come from any department: engineering, marketing, data science, product management, or customer service. By identifying these internal innovators, organizations can leverage their expertise. Once these experts are found, they can conduct “AI audits” of current workflows, identify areas for improvement, and provide starter prompts for specific use cases. These internal experts often better understand the company’s systems and goals, making them more capable of spotting relevant opportunities. Ensuring Time for ExplorationBeyond providing training, it’s crucial that employees have the time to explore and experiment with AI tools. Companies can’t simply tell their employees to innovate with AI while demanding that another month’s worth of features be delivered by Friday at 5 p.m. Ensuring teams have a few hours a month for exploration is essential for fostering true AI adoption. Once the initial hurdle of adoption is overcome, employees will be able to identify the most promising areas for AI investment. From there, organizations will be better positioned to assess the need for more specialized training. ConclusionThe AI productivity paradox is not about the complexity of the technology but rather how organizations approach work and innovation. Harnessing AI’s potential is simpler than “AI influencers” often suggest, requiring only
Throughout history, disruptive technologies have been the catalyst for major social and economic revolutions. The invention of the plow and irrigation systems 12,000 years ago sparked the Agricultural Revolution, while Johannes Gutenberg’s 15th-century printing press fueled the Protestant Reformation and helped propel Europe out of the Middle Ages into the Renaissance. In the 18th century, James Watt’s steam engine ushered in the Industrial Revolution. More recently, the internet has revolutionized communication, commerce, and information access, shrinking the world into a global village. Similarly, smartphones have transformed how people interact with their surroundings. Now, we stand at the dawn of the AI revolution. Large Language Models (LLMs) represent a monumental leap forward, with significant economic implications at both macro and micro levels. These models are reshaping global markets, driving new forms of currency, and creating a novel economic landscape. The reason LLMs are transforming industries and redefining economies is simple: they automate both routine and complex tasks that traditionally require human intelligence. They enhance decision-making processes, boost productivity, and facilitate cost reductions across various sectors. This enables organizations to allocate human resources toward more creative and strategic endeavors, resulting in the development of new products and services. From healthcare to finance to customer service, LLMs are creating new markets and driving AI-driven services like content generation and conversational assistants into the mainstream. To truly grasp the engine driving this new global economy, it’s essential to understand the inner workings of this disruptive technology. These posts will provide both a macro-level overview of the economic forces at play and a deep dive into the technical mechanics of LLMs, equipping you with a comprehensive understanding of the revolution happening now. Why Now? The Connection Between Language and Human Intelligence AI did not begin with ChatGPT’s arrival in November 2022. Many people were developing machine learning classification models in 1999, and the roots of AI go back even further. Artificial Intelligence was formally born in 1950, when Alan Turing—considered the father of theoretical computer science and famed for cracking the Nazi Enigma code during World War II—created the first formal definition of intelligence. This definition, known as the Turing Test, demonstrated the potential for machines to exhibit human-like intelligence through natural language conversations. The test involves a human evaluator who engages in conversations with both a human and a machine. If the evaluator cannot reliably distinguish between the two, the machine is considered to have passed the test. Remarkably, after 72 years of gradual AI development, ChatGPT simulated this very interaction, passing the Turing Test and igniting the current AI explosion. But why is language so closely tied to human intelligence, rather than, for example, vision? While 70% of our brain’s neurons are devoted to vision, OpenAI’s pioneering image generation model, DALL-E, did not trigger the same level of excitement as ChatGPT. The answer lies in the profound role language has played in human evolution. The Evolution of Language The development of language was the turning point in humanity’s rise to dominance on Earth. As Yuval Noah Harari points out in his book Sapiens: A Brief History of Humankind, it was the ability to gossip and discuss abstract concepts that set humans apart from other species. Complex communication, such as gossip, requires a shared, sophisticated language. Human language evolved from primitive cave signs to structured alphabets, which, along with grammar rules, created languages capable of expressing thousands of words. In today’s digital age, language has further evolved with the inclusion of emojis, and now with the advent of GenAI, tokens have become the latest cornerstone in this progression. These shifts highlight the extraordinary journey of human language, from simple symbols to intricate digital representations. In the next post, we will explore the intricacies of LLMs, focusing specifically on tokens. But before that, let’s delve into the economic forces shaping the LLM-driven world. The Forces Shaping the LLM Economy AI Giants in Competition Karl Marx and Friedrich Engels argued that those who control the means of production hold power. The tech giants of today understand that AI is the future means of production, and the race to dominate the LLM market is well underway. This competition is fierce, with industry leaders like OpenAI, Google, Microsoft, and Facebook battling for supremacy. New challengers such as Mistral (France), AI21 (Israel), and Elon Musk’s xAI and Anthropic are also entering the fray. The LLM industry is expanding exponentially, with billions of dollars of investment pouring in. For example, Anthropic has raised $4.5 billion from 43 investors, including major players like Amazon, Google, and Microsoft. The Scarcity of GPUs Just as Bitcoin mining requires vast computational resources, training LLMs demands immense computing power, driving a search for new energy sources. Microsoft’s recent investment in nuclear energy underscores this urgency. At the heart of LLM technology are Graphics Processing Units (GPUs), essential for powering deep neural networks. These GPUs have become scarce and expensive, adding to the competitive tension. Tokens: The New Currency of the LLM Economy Tokens are the currency driving the emerging AI economy. Just as money facilitates transactions in traditional markets, tokens are the foundation of LLM economics. But what exactly are tokens? Tokens are the basic units of text that LLMs process. They can be single characters, parts of words, or entire words. For example, the word “Oscar” might be split into two tokens, “os” and “car.” The performance of LLMs—quality, speed, and cost—hinges on how efficiently they generate these tokens. LLM providers price their services based on token usage, with different rates for input (prompt) and output (completion) tokens. As companies rely more on LLMs, especially for complex tasks like agentic applications, token usage will significantly impact operational costs. With fierce competition and the rise of open-source models like Llama-3.1, the cost of tokens is rapidly decreasing. For instance, OpenAI reduced its GPT-4 pricing by about 80% over the past year and a half. This trend enables companies to expand their portfolio of AI-powered products, further fueling the LLM economy. Context Windows: Expanding Capabilities
Agentic AI vs. Copilots: Defining the Future of Generative AI Artificial Intelligence has rapidly evolved, progressing from simple automation to generative models, to copilots. But now, a new player—Agentic AI—has emerged, promising to redefine the AI landscape. Is Agentic AI the next logical step, or will it coexist alongside copilots, each serving distinct roles? Copilots and Agentic AI. Generative AI: Creativity with a Human Touch Since the launch of ChatGPT, generative AI has dominated tech priorities, offering businesses the ability to generate content—text, images, videos, and more—from pre-defined data. However, while revolutionary, generative AI still relies heavily on human input to guide its output, making it a powerful collaborator rather than an autonomous actor. Enter Agentic AI: Autonomy Redefined Agentic AI represents a leap forward, offering systems that possess autonomy and the ability to act independently to achieve pre-defined goals. Unlike generative AI copilots that respond to human prompts, Agentic AI makes decisions, plans actions, and learns from experience. Think of it as Siri or Alexa—enhanced with autonomy and learning capabilities. Gartner recently spotlighted Agentic AI as its top technology trend for 2025, predicting that by 2028, at least 15% of day-to-day work decisions will be made autonomously, up from virtually none today. Agentforce and the Third Wave of AI Salesforce’s “Agentforce,” unveiled at Dreamforce, is a prime example of Agentic AI’s potential. These autonomous agents are designed to augment employees by handling tasks across sales, service, marketing, and commerce. Salesforce CEO Mark Benioff described it as the “Third Wave of AI,” going beyond copilots to deliver intelligent agents deeply embedded into customer workflows. Salesforce aims to empower one billion AI agents by 2025, integrating Agentforce into every aspect of customer success. Benioff took a swipe at competitors’ bolt-on generative AI solutions, emphasizing that Agentforce is deeply embedded for maximum value. The Role of Copilots: Collaboration First While Agentic AI gains traction, copilots like Microsoft’s Copilot Studio and SAP’s Joule remain critical for businesses focused on intelligent augmentation. Copilots act as productivity boosters, working alongside humans to optimize processes, enhance creativity, and provide decision-making support. SAP’s Joule, for example, integrates seamlessly into existing systems to optimize operations while leaving strategic decision-making in human hands. This collaborative model aligns well with businesses prioritizing agility and human oversight. Agentic AI: Opportunities and Challenges Agentic AI’s autonomy offers significant potential for streamlining complex processes, reducing human intervention, and driving productivity. However, it also comes with risks. Eleanor Watson, AI ethics engineer at Singularity University, warns that Agentic AI systems require careful alignment of values and goals to avoid unintended consequences like dangerous shortcuts or boundary violations. In contrast, copilots retain human agency, making them particularly suited for creative and knowledge-based roles where human oversight remains essential. Copilots and Agentic AI The choice between Agentic AI and copilots hinges on an organization’s priorities and risk tolerance. For simpler, task-specific applications, copilots excel by providing assistance without removing human input. Agentic AI, on the other hand, shines in complex, multi-task scenarios where autonomy is key. Dom Couldwell, head of field engineering EMEA at DataStax, emphasizes the importance of understanding when to deploy each model. “Use a copilot for specific, focused tasks. Use Agentic AI for complex, goal-oriented processes involving multiple tasks. And leverage Retrieval Augmented Generation (RAG) in both to provide context to LLMs.” The Road Ahead: Coexistence or Dominance? As AI evolves, Agentic AI and copilots may coexist, serving complementary roles. Businesses seeking full automation and scalability may gravitate toward Agentic AI, while those prioritizing augmented intelligence and human collaboration will continue to rely on copilots. Ultimately, the future of AI will be defined not by one model overtaking the other, but by how well each aligns with the specific needs, goals, and challenges of the organizations adopting them. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Marc Benioff Dismisses AI Threats, Sets Sights on a Billion AI Agents in One Year Salesforce CEO Marc Benioff has no doubts about the transformative potential of AI for enterprise software, particularly Salesforce itself. At the core of his vision are AI agents—autonomous software bots designed to handle routine tasks, freeing up human workers to focus on more strategic priorities. “What if your workforce had no limits? That’s a question we couldn’t even ask over the past 25 years of Salesforce—or the 45 years I’ve been in software,” Benioff said during an appearance on TechCrunch’s Equity podcast. The Billion-Agent Goal Benioff revealed that Salesforce’s recently launched Agentforce platform is already being adopted by “hundreds of customers” and aims to deploy a billion AI agents within a year. These agents are designed to handle tasks across industries—from enhancing customer experiences at retail brands like Gucci to assisting patients with follow-ups in healthcare. To illustrate, Benioff shared his experience with Disney’s virtual Private Tour Guides. “The AI agent analyzed park flow, ride history, and preferences, then guided me to attractions I hadn’t visited before,” he explained. Competition with Microsoft and the AI Landscape While Benioff is bullish on AI, he hasn’t hesitated to criticize competitors—particularly Microsoft. When Microsoft unveiled its new autonomous agents for Dynamics 365 in October, Benioff dismissed them as uninspired. “Copilot is the new Clippy,” he quipped, referencing Microsoft’s infamous virtual assistant from the 1990s. Benioff also cited Gartner research highlighting data security issues and administrative flaws in Microsoft’s AI tools, adding, “Copilot has disappointed so many customers. It’s not transforming companies.” However, industry skeptics argue that the real challenge to Salesforce isn’t Microsoft but the wave of AI-powered startups disrupting traditional enterprise software. With tools like OpenAI’s ChatGPT and Klarna’s in-house AI assistant “Kiki,” companies are starting to explore GenAI solutions that can replace legacy platforms like Salesforce altogether. For example, Klarna recently announced it was moving away from Salesforce and Workday, favoring GenAI tools that enable seamless, conversational interfaces and faster data access. Why Salesforce Is Positioned to Win Despite the noise, Benioff remains confident that Salesforce’s extensive data infrastructure gives it a significant edge. “We manage 230 petabytes of customer data with robust security and sharing models. That’s what allows AI to thrive in our ecosystem,” he said. While companies may question how other platforms like OpenAI handle data, Salesforce offers an integrated approach, reducing the need for complex data migrations to other clouds, such as Microsoft Azure. Salesforce’s Own Use of AI Benioff also highlighted Salesforce’s internal adoption of Agentforce, using AI agents in its customer service operations, sales processes, and help centers. “If you’re authenticated on help.salesforce.com, you’re already interacting with our agent,” he noted. AI Startups: Threat or Opportunity? As for concerns about AI startups overtaking Salesforce, Benioff sees them as acquisition opportunities rather than existential threats. “We’ve made over 60 acquisitions, many of them startups,” he said. He pointed to Agentforce itself, which was built using technology from Airkit.ai, a startup founded by a former Salesforce employee. Salesforce Ventures initially invested in Airkit.ai before acquiring and integrating it into its platform. The Path Forward Benioff is resolute in his belief that AI won’t hurt Salesforce—instead, it will revolutionize how businesses operate. While skeptics warn of a seismic shift in enterprise software, Benioff’s strategy is clear: lean into AI, leverage data, and stay agile through innovation and acquisitions. “We’re just getting started,” he concluded, reiterating his vision for a future where AI agents expand the possibilities of work and customer experience like never before. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
AI Research Agents: Transforming Knowledge Discovery by 2025 (Plus the Top 3 Free Tools) The research world is on the verge of a groundbreaking shift, driven by the evolution of AI research agents. By 2025, these agents are expected to move beyond being mere tools to becoming transformative assets for knowledge discovery, revolutionizing industries such as marketing, science, and beyond. Human researchers are inherently limited—they cannot scan 10,000 websites in an hour or analyze data at lightning speed. AI agents, however, are purpose-built for these tasks, providing efficiency and insights far beyond human capabilities. Here, we explore the anticipated impact of AI research agents and highlight three free tools redefining this space (spoiler alert: it’s not ChatGPT or Perplexity!). AI Research Agents: The New Era of Knowledge Exploration By 2030, the AI research market is projected to skyrocket from .1 billion in 2024 to .1 billion. This explosive growth represents not just advancements in AI but a fundamental transformation in how knowledge is gathered, analyzed, and applied. Unlike traditional AI systems, which require constant input and supervision, AI research agents function more like dynamic research assistants. They adapt their approach based on outcomes, handle vast quantities of data, and generate actionable insights with remarkable precision. Key Differentiator: These agents leverage advanced Retrieval Augmented Generation (RAG) technology, ensuring accuracy by pulling verified data from trusted sources. Equipped with anti-hallucination algorithms, they maintain factual integrity while citing their sources—making them indispensable for high-stakes research. The Technology Behind AI Research Agents AI research agents stand out due to their ability to: For example, an AI agent can deliver a detailed research report in 30 minutes, a task that might take a human team days. Why AI Research Agents Matter Now The timing couldn’t be more critical. The volume of data generated daily is overwhelming, and human researchers often struggle to keep up. Meanwhile, Google’s focus on Experience, Expertise, Authoritativeness, and Trustworthiness (EEAT) has heightened the demand for accurate, well-researched content. Some research teams have already reported time savings of up to 70% by integrating AI agents into their workflows. Beyond speed, these agents uncover perspectives and connections often overlooked by human researchers, adding significant value to the final output. Top 3 Free AI Research Tools 1. Stanford STORM Overview: STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking) is an open-source system designed to generate comprehensive, Wikipedia-style articles. Learn more: Visit the STORM GitHub repository. 2. CustomGPT.ai Researcher Overview: CustomGPT.ai creates highly accurate, SEO-optimized long-form articles using deep Google research or proprietary databases. Learn more: Access the free Streamlit app for CustomGPT.ai. 3. GPT Researcher Overview: This open-source agent conducts thorough research tasks, pulling data from both web and local sources to produce customized reports. Learn more: Visit the GPT Researcher GitHub repository. The Human-AI Partnership Despite their capabilities, AI research agents are not replacements for human researchers. Instead, they act as powerful assistants, enabling researchers to focus on creative problem-solving and strategic thinking. Think of them as tireless collaborators, processing vast amounts of data while humans interpret and apply the findings to solve complex challenges. Preparing for the AI Research Revolution To harness the potential of AI research agents, researchers must adapt. Universities and organizations are already incorporating AI training into their programs to prepare the next generation of professionals. For smaller labs and institutions, these tools present a unique opportunity to level the playing field, democratizing access to high-quality research capabilities. Looking Ahead By 2025, AI research agents will likely reshape the research landscape, enabling cross-disciplinary breakthroughs and empowering researchers worldwide. From small teams to global enterprises, the benefits are immense—faster insights, deeper analysis, and unprecedented innovation. As with any transformative technology, challenges remain. But the potential to address some of humanity’s biggest problems makes this an AI revolution worth embracing. Now is the time to prepare and make the most of these groundbreaking tools. 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Three Key Generative AI Data Privacy and Security Concerns The rise of generative AI is reshaping the digital landscape, introducing powerful tools like ChatGPT and Microsoft Copilot into the hands of professionals, students, and casual users alike. From creating AI-generated art to summarizing complex texts, generative AI (GenAI) is transforming workflows and sparking innovation. However, for information security and privacy professionals, this rapid proliferation also brings significant challenges in data governance and protection. Below are three critical data privacy and security concerns tied to generative AI: 1. Who Owns the Data? Data ownership is a contentious issue in the age of generative AI. In the European Union, the General Data Protection Regulation (GDPR) asserts that individuals own their personal data. In contrast, data ownership laws in the United States are less clear-cut, with recent state-level regulations echoing GDPR’s principles but failing to resolve ambiguity. Generative AI often ingests vast amounts of data, much of which may not belong to the person uploading it. This creates legal risks for both users and AI model providers, especially when third-party data is involved. Cases surrounding intellectual property, such as controversies involving Slack, Reddit, and LinkedIn, highlight public resistance to having personal data used for AI training. As lawsuits in this arena emerge, prior intellectual property rulings could shape the legal landscape for generative AI. 2. What Data Can Be Derived from LLM Output? Generative AI models are designed to be helpful, but they can inadvertently expose sensitive or proprietary information submitted during training. This risk has made many wary of uploading critical data into AI models. Techniques like tokenization, anonymization, and pseudonymization can reduce these risks by obscuring sensitive data before it is fed into AI systems. However, these practices may compromise the model’s performance by limiting the quality and specificity of the training data. Advocates for GenAI stress that high-quality, accurate data is essential to achieving the best results, which adds to the complexity of balancing privacy with performance. 3. Can the Output Be Trusted? The phenomenon of “hallucinations” — when generative AI produces incorrect or fabricated information — poses another significant concern. Whether these errors stem from poor training, flawed data, or malicious intent, they raise questions about the reliability of GenAI outputs. The impact of hallucinations varies depending on the context. While some errors may cause minor inconveniences, others could have serious or even dangerous consequences, particularly in sensitive domains like healthcare or legal advisory. As generative AI continues to evolve, ensuring the accuracy and integrity of its outputs will remain a top priority. The Generative AI Data Governance Imperative Generative AI’s transformative power lies in its ability to leverage vast amounts of information. For information security, data privacy, and governance professionals, this means grappling with key questions, such as: With high stakes and no way to reverse intellectual property violations, the need for robust data governance frameworks is urgent. As society navigates this transformative era, balancing innovation with responsibility will determine whether generative AI becomes a tool for progress or a source of new challenges. While generative AI heralds a bold future, history reminds us that groundbreaking advancements often come with growing pains. It is the responsibility of stakeholders to anticipate and address these challenges to ensure a safer and more equitable AI-powered world. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more
The concept of artificial intelligence, or synthetic minds capable of thinking and reasoning like humans, has been around for centuries. Ancient cultures often expressed ideas and pursued goals similar to AI, and in the early 20th century, science fiction brought these notions to modern audiences. Works like The Wizard of Oz and films such as Metropolis resonated globally, laying the groundwork for contemporary AI discussions.
ChatGPT has also appeared in influence operations, with groups using it to generate political content for social media. OpenAI observed an Iranian-led operation, Storm-2035, using ChatGPT to publish politically charged content about U.S. elections and global conflicts. Yet, OpenAI noted that these AI-driven influence efforts often lack audience engagement.
Automate LinkedIn Outreach with We-Connect’s New Salesforce Integration Sales and marketing teams can now streamline their LinkedIn outreach and lead management efforts with We-Connect’s powerful new integration for Salesforce, the world’s leading CRM platform. We-Connect, the premier LinkedIn automation tool, has officially launched its native integration with Salesforce, enabling seamless synchronization of contact data, campaign metrics, and outreach activity. This integration provides sales and marketing teams with a unified platform to manage all LinkedIn outreach efforts directly within Salesforce’s familiar interface. Transforming LinkedIn Outreach for Sales and Marketing Teams Traditionally, LinkedIn outreach happens outside CRM systems, leaving teams without a clear way to track campaign effectiveness. Sales reps often resort to manual searches on LinkedIn rather than leveraging data already housed in their CRM. The We-Connect and Salesforce integration revolutionizes this process by: Key Features of the Integration A Game-Changer for Outreach Efforts “Our new Salesforce integration brings LinkedIn outreach into a single, unified platform,” said Gary Egan, Product Manager at We-Connect. “With this integration, sales and marketing teams can stay aligned, act on real-time insights, and scale their outreach efforts like never before.” By consolidating LinkedIn activities within Salesforce, teams can better measure campaign performance, maintain a consistent buyer journey, and boost efficiency—all while leveraging Salesforce’s powerful CRM capabilities. For more details, visit the We-Connect Salesforce Integration page. About We-Connect Founded in 2018, We-Connect is the leading LinkedIn automation tool for sales, marketing, recruiting, and business professionals. Its advanced features help users automate LinkedIn interactions, connect with the right people, and generate high-quality leads effortlessly. We-Connect empowers professionals to build meaningful relationships, drive growth, and achieve their business goals with efficiency and precision. Learn more about how We-Connect transforms LinkedIn outreach at We-Connect.io. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
AI is revolutionizing the way we work, offering a wide range of tools beyond ChatGPT that can enhance efficiency, creativity, and productivity. Whether you’re working with data, code, marketing, videos, images, AI bots, or research, here are the top AI tools that can transform your workflow. Cool and New AI. Don’t get spooked. There will be a cornucopia more in November. 🌟 Code 1️⃣ GlideTurn spreadsheets into powerful mobile apps without writing a single line of code. Glide makes it easy for non-developers to create professional apps with minimal effort. 2️⃣ BubbleA visual programming platform that allows users to build web applications without any coding knowledge. Ideal for entrepreneurs and startups looking to launch digital products quickly. 3️⃣ AskCodiThis AI coding assistant speeds up coding tasks, offers helpful suggestions, and simplifies debugging for developers, making it a must-have tool for coding professionals. 🌟 Data 1️⃣ BasedLabsA robust data analytics platform designed for scientists and engineers. BasedLabs offers complex data processing and model building with exceptional precision. 2️⃣ Coral AIPerfect for data-driven professionals, Coral AI provides efficient edge AI tools for processing large datasets and delivering insights with on-device intelligence, speeding up tasks. 3️⃣ JuliusAn AI-powered tool for market researchers and data analysts, Julius streamlines data processes and offers powerful insights into market trends. 🌟 Marketing 1️⃣ Sprout SocialThis all-in-one social media management platform leverages AI to help marketers optimize their social presence, engage with audiences, and track detailed analytics. 2️⃣ AdCreative AIEnhance your marketing campaigns with AI-generated ads that convert. AdCreative AI allows marketers to design high-impact, creative ads effortlessly. 3️⃣ Jasper AIA top tool for content creators, Jasper AI assists in crafting high-conversion marketing copy, blogs, and ad texts at scale, making it indispensable for digital marketing. 🌟 Video 1️⃣ SynthesiaCreate professional videos without the need for cameras or actors. Synthesia’s AI avatars enable you to produce multilingual videos, making it ideal for corporate and educational content. 2️⃣ HeygenThis AI tool simplifies video production by allowing users to create AI-generated videos, perfect for marketing campaigns or training materials. 3️⃣ Opus ClipOpus Clip transforms long-form video content into short, engaging clips optimized for social media, helping creators repurpose content easily. 🌟 Image 1️⃣ Getimg.AIAutomate image creation with Getimg.AI, which enhances your visual content by generating high-quality images in minutes, speeding up the design process. 2️⃣ PicsartA versatile image editing and design platform with AI tools that make creating stunning visuals effortless, making it ideal for social media, advertising, and creative projects. 3️⃣ Leonardo AIA powerful AI-driven tool for creators, Leonardo AI helps generate high-quality images, illustrations, and graphics, making it an essential tool for designers and artists. 🌟 AI Bot 1️⃣ LiveChatAn AI-powered live chat solution that integrates seamlessly into websites to provide real-time customer support, enhancing business communication. 2️⃣ LandbotThis tool helps create conversational experiences with AI-powered chatbots for customer support, sales, and lead generation, automating client interactions. 3️⃣ CustomGPTA customizable GPT-powered AI chatbot tailored for specific industries and businesses, perfect for providing personalized customer support. 🌟 Research 1️⃣ ChatPDFTurn PDFs into interactive documents with ChatPDF, allowing users to easily navigate and extract information using an AI-based assistant. 2️⃣ VidIQVidIQ provides AI-powered tools to optimize YouTube content for better engagement and visibility, making it invaluable for content creators. 3️⃣ SemrushAn advanced SEO platform powered by AI, Semrush gives marketers and researchers deep insights into online visibility, helping boost content performance. AI extends far beyond ChatGPT. This diverse range of tools is designed to make your work more efficient and productive, whether you’re coding, marketing, creating content, or conducting research. Embrace these AI tools to unlock new levels of creativity and efficiency. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
