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AI is an extraordinary tool that amplifies our cognitive capacity. It can analyze, summarize, and generate content faster than any human. However, AI is only ever as good as the questions we ask it. It will never replace our capacity for thinking, and can, in fact, reinforce bias because it is learning what we teach it. For this reason, the top skills of the future include thinking skills. According to the World Economic Forums Future of Jobs report, employers anticipate that beyond technical literacy, the most in-demand capabilities will be creative thinking, critical thinking, resilience, and the capacity for learning. Thinking is a premium, and yet it is also the very thing that is most at risk. We all know that when it comes to data, rubbish in = rubbish out. The same goes for our mind. What we feed it and how we use it determines the quality of our contribution and the value we add. As a high-performance coach and leadership expert, I spend my time consulting with leaders and their teams, challenging them to do better thinking and extract the value of their collective capacity. Modern-day workers are facing a triple threat from the joint epidemics of algorithms, attention theft, and burnout. Heres why: 1. Algorithms reinforce biases More and more, our capacity to think, create, and problem-solve is being challenged by algorithms delivered through social media. Our viewpoints are being regurgitated back to us via algorithms that sense what we like, what we tolerate, and what we think we need. Social media serves to reinforce existing beliefs, not challenge them. We are slowly losing the capacity for critical thinking, and this is the very capacity we need to develop if we are to remain adaptive in a world where cognitive load is being managed more and more by computers. 2. Attention theft robs us of time Attention theft is catastrophic to independent thinking and crippling our ability to focus. How many notifications are pinging right now to pull your attention away from reading this article? How many times a day are you pulled away from the task at hand? Research from Tania Barney, neuroscience and sensory processing expert, suggests that distractions are costing us time as well as money. Her research found: An average of 2.1 hours are lost daily as a result of distractions. The average time spent on a task before we get distracted is 11 minutes. The average time it takes after a distraction to return to a task is 25 minutes. After meetings, emails, unplanned interactions, and rest breaks, how many hours do we have left in a day for thinking and productive work? We get pulled into the urgent things that feel pressing but do not meaningfully matter (like chats with colleagues, reply-all emails, and notification alerts). All this leads to the next major threat to thinkingburnout. 3. Burnout robs us of energy Burnout is the compound interest on lost productivity due to attention theft. Just because were getting distracted by urgent unimportant stuff doesnt mean that the real important stuff goes away. It piles up, weighing us down psychologically and eating into recreation hours where we should be recharging our batteries through rest, exercise, or time with loved ones. Burnout is a global issue, costing humans their well-being and businesses millions in lost productivity. Burnout is the result of prolonged work stress. Symptoms include overwhelm, constant exhaustion, and a feeling of being ineffective at work no matter how hard you try. Increased rates of burnout add up to bad news for business. Burnout has been identified as one of the leading causes driving people to leave their jobs. But it also leads to disengagement, which can cost employers 34% of a disengaged employee’s annual salary, according to the Gallup State of the Global Workplace 2021 report. Prioritize team thinking time to leverage collective potential With the joint epidemics of algorithms, attention theft, and burnout, our most precious resources have changed from time and money to energy and attention. To counteract this, we all need to be more curious. Promote and legitimize thinking time by asking more questions in daily interactions. Encourage your team members to build on one anothers ideas. Create regular cadences when the team meets to reflect, reprioritize, and reset, such as quarterly team-planning workshops. The future of work is humanand your capacity to create spaces and places where people can think, learn, adapt, and grow is what will allow teams and organizations to transform and endure.
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E-Commerce
With their drab gray suits and their Buddy Holly glasses, the so-called traitorous eight don’t look like revolutionaries. Given no context, you can imagine them occupying some kind of middle-management role at a small regional bank. And yet these are the people you can thank for the digital world. The eightwhich included Intel cofounder Gordon Moorehad departed Shockley Semiconductor Laboratory to found Fairchild Semiconductor, which soon became the world’s biggest producer of electrical components for computers. Many of its founders would, in turn, leave again to launch their own ventures. Many of these companies coalesced in the same areathe place we now call Silicon Valleycreating an ecosystem for innovation and technological development that endures to this day. Look again at that photo. Even with the suits and the glasses, these are arguably some of the most interesting and influential people that the technology industry has ever known. Even if you don’t know their names, and even though they have never appeared on the Joe Rogan podcast, they still have a legacy that endures to this day. The Power of Hardware On a personal level, I have always found hardware more interesting than software. There’s a joke that CPUs are just “rocks we tricked into thinking,” which has some truth to it. You can’t help but be amazed at the process that turns iron, copper, gold, and silicon dioxide into something that can run unfathomably complex mathematical calculations, play chess, and stream Netflix. And that’s before you take into account that even the most basic consumer CPU has billions of transistors, each measuring a fraction of the width of a hair. Perhaps the main reason I’m drawn to hardware is that it’s often easy to measure whether something is better than the thing that preceded it. With a tape measure, you can see whether one computer is smaller than another. You can calculate how many mathematical operations a CPU can perform in a second, or count the number of pixels on a display. You can measure its weight, or the heat it gives off, or whether one battery has a larger capacity than another. Hardware is clean-cut. Straightforward. Unambiguous. And these improvements aren’t theoretical, but are felt directly by the end user. When a physical object is meaningfully better, you can tell. If you have upgraded from an Intel to an Apple Silicon Mac, you know this. You probably remember what it was like when you ditched your bulky CRT monitor for an LCD flat panel. You know the difference between a computer with a mechanical hard drive and one with flash storage. Hardware is typically built with utility in mind. The old adage “hardware is hard” is true, but it neglects the fact that it’s also pretty expensive. You only really build something if you believe it’s better than the existing thing, and that somebody will find it useful enough to pay for it. Silicon Valley Needs to Rediscover Its Roots The modern tech industryespecially that which now occupies the same hallowed ground once trod by the treacherous eighthas become a shell of its former self. Techs innovations feel only marginally iterative at best. It is this that makes me nostalgic for the era when Silicon Valley was about siliconor, more specifically, physical, tangible objects that changed the world. And I believe it is an era that we can, and must, return to. The Silicon Valley of the late 1960s, 1970s, and 1980s was a glorious time of American innovation and engineering, where verifiable geniuses discovered the breakthroughs that allowed our current world to exist. The integrated circuit. The microprocessor. The computer mouse. It was an era when technological vision and clear-thinking business strategy combined to bring new inventions to a market, and then popularize them to a global scale. And in doing so, Silicon Valley changed everything. To be clear, I am not just talking about vision. I’m talking about hardware. The applications we will need to run in the future will require faster, better computers, and we need somebody to invent them. Faster, better computers will allow us to reclaim ownership of the tech we use, enabling us to finally break free of the cloud. It will help undo some of the disastrous cultural changes that have occurred over the past decade or so, when people got used to the idea that they must always be subject to the mercies of another, larger tech company. Hardware is hard. Change is even harder. But in this case, I think it’s worth it. The Bright Light on the Tip of the Spear So, there is some cause for optimism, and its not in giant GPUs. Buried in the news coming out of CES was the announcement of Nvidias DGX Spark, a $3,000 desktop computer powered by Nvidias GB10 Grace Blackwell Superchip, that went relatively unnoticed but I believe is a significant moment in personal computing. The DGX Spark delivers up to 1 petaflop of performance in a compact form factor, giving researchers and developers unprecedented access to cutting-edge computational power directly at their desks. Its like having a computer thats a thousand times faster than a regular desktop in the body of a Mac Mini, and Im a little surprised it isnt being taken more seriously. In more human terms, Nvidia created an ultrapowerful Mac Mini that developers, data scientists, and AI researchers are able to use to run reasonably large data workloads and AI models on their desk as opposed to a fleet of massive GPU servers in the cloud. While Silicon Valleys biggest companies have grown on the back of software, the truth is that it needs hardware to grow any further, and while those GPUs might be the headline-grabbers during Nvidias earnings, creating meaningful new kinds of computing is what will lead to actual innovation in software. As a result, by creating a Blackwell chip inside a Mac Mini-size supercomputer, Nvidia allows companies to crunch through large data sets or run self-hosted generative AI models quickly and efficiently, all without relying on the cloud to do so. This vastly lowers the barrier to entry for high-performance computing, which currently requires buying or renting expensive specialized hardware or spinning up expensive infrastructure. I’m going to dive briefly into why this matters. For years, both at Voltron Data and previously at BlazingSQL, I’ve advocated for clustering smaller, more efficient, and less-expensive GPUs together using high-performance networking. However, network limitations have always prevented full utilization of the cluster’s compute performance since data simply couldn’t move fast enough to keep GPUs fully fed. While it hasnt shipped yet, Nvidia has specifically called out the inclusion of ConnectX to allow users to connect two Nvidia DGX Spark computers together, as well as other features (NCCL, RDMA, GPUDirect storage) that are specifically built for faster networking. This will enable efficient parallel processing and high-bandwidth communication, making high-performance AI and analytics workloads accessible to a broader range of researchers and enterprises. A distributed model using a cluster of Nvidia DGX Spark units could offer a more cost-effective and flexible alternative to currentl available GPU clusters, lowering the barrier to entry for basically any high-performance computing use cases. My focus on Nvidia DGX Spark is to illustrate a greater point about what will keep Silicon Valley at the forefront of technological progress. True innovation doesnt come from just making things bigger or more powerful, but in the distinct relationship and interactions between software and hardware, and even between different pieces of hardware. Nvidia DGX Spark isnt just Nvidia making a chip smaller, but finding ways to add faster on-device memory, software to make getting the data to both the memory and the GPU faster, and (I imagine) some unique ways to keep it cool. The truly world-changing innovations and technological breakthroughs that will advance humanity will come from a deep commitment to silicon engineering, and Silicon Valley needs to remember that this is the only way that software will continue to grow.
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E-Commerce
Todays B2B CEOs are tasked with a delicate balancing act: driving growth, improving efficiency, and creating seamless customer experiences, all while navigating unprecedented market complexity. Meanwhile, the revenue professionals responsible for executing these goals face their own challenges. Buying journeys have become increasingly labyrinthine, with big buying teams and long sales cycles. Seventy-seven percent of B2B buyers say their last purchasing decision was very complex or difficult, with more than 800 interactions on average with potential vendors. Misalignment across revenue teams compounds the issue, making it nearly impossible to deliver efficient, relevant, and cohesive buyer experiences. This complexity creates a cycle of inefficiency, where teams work harder to achieve diminishing returns. Fortunately, were at a pivotal moment in technology. AI and data advances are empowering organizations to simplify complex revenue cycles. Among these innovations, AI agents offer a promising solution. AI agents arent simple software add-ons. Theyre intelligent partners that enable teams to act faster, collaborate more effectively, and scale more strategically. Lets explore how CEOs can equip their teams with AI agents to achieve sustainable growth. AI agents are partners, not tools AI agents represent a significant evolution in business technology. Unlike traditional software, which passively waits for human input, AI agents actively analyze data, surface opportunities, make recommendations, and drive results in real time. For CEOs, this distinction is critical. AI agents dont just automate repetitive tasks; they perform work that aligns with strategic goals. From identifying early buying signals to optimizing customer engagement, AI agents seamlessly integrate into workflows to ensure every touchpoint is efficient, personalized, and impactful. In a world in which breaking down silos and acting on intelligence faster than competitors defines success, AI agents are the bridge between vision and execution. Why good data powers great outcomes AI agents are only as effective as the data that fuels them. AI agents are built on large language models (LLMs) trained on public data. That data can sometimes produce sketchy resultslike when Googles AI search raised (and then dashed) Disney fans hopes by describing the impending release of Encanto 2 because it pulled its data from a fan fiction site. The fallout of misinformation in business can do much more damage than simply disappointing movie-goers. Poor-quality data can lead to disjointed recommendations and faulty business decisions. Not only that, but if you only use public data to feed your AI agents, you’ll have the same output as everyone else relying solely on LLMs. The solution for this lies within a businesss own walls. Enterprises have massive amounts of data that LLMs have not seen. Feeding this data to AI agents allows them to produce differentiated, contextualized output. For instance, integrating intent data into a sales-focused AI agents diet yields personalized outreach based on individual prospects needs. It’s also important that the data AI agents use is clean, accurate, and comprehensiveand that it spans the entire revenue organization. Shared data ensures that AI agents can piece together the full picture of the buyer journeyfrom early intent signals to post-sale engagement. What CEOs get wrong about AI agents AI agents are difficult to implement. AI agents don’t necessarily require complex overhauls. Scalable, modular solutions make it easier than ever to adopt AI incrementally, starting with specific use cases and expanding as success builds.Example:Many of our customers quickly deploy our conversational email agent for one-use case (such as re-engaging closed/lost opportunities) and build from there. This enables teams to see the immediate value of AI agent-led contextual email conversations, while at the same time laying the foundation for broader adoption. AI agents are only about efficiency. While AI agents excel at streamlining processes, their real value lies in their ability to drive strategic outcomes across industries.Example:Johnson & Johnson uses AI agents in drug discovery to optimize chemical synthesis processes. AI enhances efficiency, but more importantly, it drives strategic advancements in pharmaceutical innovation by accelerating development timelines and improving cost-effectiveness. The ROI of AI agents: Real-world impact Harri, a global leader in workforce management technology for the hospitality industry, faced a challenge familiar to many CEOsthe need to scale engagement without increasing resources. To support their strong marketing team in generating demand, Harri implemented an AI agent through 6sense as part of its outreach strategy. The AI agent autonomously identified high-intent prospects and delivered timely, personalized messages at scale, enabling Harri to engage buyers more efficiently and effectively. The results: They generated more than $12 million in pipeline and $3 million in closed/won deals in just one quarter. Campaigns achieved a 34% view-through-rate (VTR) rate, far exceeding the initial goal of 20%. They scaled marketing efforts without compromising on personalized engagement. By scaling outreach, improving engagement, and targeting high-value opportunities, Harri took pressure off its team, while achieving significant growth and enhancing the buyer experience. Pave the way for smarter growth AI agents are still so new that CEOs who arent using them yet can get ahead of the competition by learning to incorporate them now. These agents simplify complexity, align revenue teams, and deliver results. By integrating AI agents, CEOs can create seamless, personalized buying journeys that meet todays expectations while driving growth. With significant AI advancements ahead, having a clear strategy is essential. By proactively adopting AI agents, organizations can address challenges and position themselves for sustained success in a rapidly evolving market. Jason Zintak CEO of 6sense.
Category:
E-Commerce
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