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I build the commercial system behind emerging technology.
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My career has centered…
Articles by Maurice
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Maurice G. shared thisAI governance is becoming a decision-rights problem. As models become more capable and authoritative-sounding, enterprises need explicit boundaries for what AI can recommend, decide, execute, and never own. The real failure mode is not a machine becoming “super intelligent.” It is an organization gradually surrendering accountability to software without ever consciously deciding to do so! That is why autonomy requires architecture. Otherwise, we are not building autonomous enterprises. We are just automating ambiguity.Maurice G. shared thisCalling AI “Super Intelligence” isn’t visionary branding. It’s an enterprise governance mistake. On September 29, the federal executive branch was directed to replace “Artificial Intelligence” with “Super Intelligence”. The very same day, CEOs from OpenAI, Google, Meta, Anthropic, Nvidia, and xAI signed a voluntary White House safety accord committing to internal controls, external audits, and independent board oversight. Spot the contradiction? On the exact day industry leaders admitted these models demand stricter oversight, we handed the technology a title that practically begs humans to subordinate their judgment to it. Words assign status. Status dictates behavior. Think about how people behave in corporate settings: ➡️ Introduce someone as an assistant, and teams double-check their work. ➡️ Introduce someone as a copilot, and humans remember they’re still flying the plane. ➡️ Introduce someone as an expert, and people fall silent. They question less. They defer. Now scale that deference across an entire organization. The fundamental operational challenge isn't convincing employees that machine intelligence is capable. It’s teaching them when to push back. Teams already struggle with automation bias. They accept confident, hallucinated outputs without verification. They are already delegating critical workflows to autonomous agents without clarifying who is accountable. Calling this technology “Super Intelligence” creates an implicit hierarchy where humans feel unqualified to challenge machine output. The scarcest corporate skill is now the courage to look at an extraordinarily fast, articulate model and say: “That output is wrong. That decision belongs to a human. Stop.” If we want leaders and frontline operators to govern technology responsibly, we shouldn't teach them to bow to it. How is your organization training teams to push back on automated recommendations before deference becomes default? —- AI made producing work cheap. It made decisions expensive. And it made everyone a decision maker. Neurocollective built AI Adoption Engineering so that people at every level can improve how decisions are made, practice them in simulation, prove they can partner effectively with AI, and put the method to work daily inside the AI platforms they already use. Delivered by certified partners.
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Maurice G. shared thisThe first exception is information. The second might be a pattern. By the fifth, somebody should probably stop calling it an exception. This sounds obvious. Organizations violate it constantly. A customer hits an unusual problem. The team solves it. Everyone moves on. Pricing needs an exception. Leadership makes the call. Everyone moves on. A handoff fails. Someone rescues it. Everyone moves on. Then three months later, the same problem. Same confusion. Same people rediscovering the answer. Same heroic save. The company experienced something. But it did not learn anything. That distinction matters. A mature operating system should get smarter when reality proves its assumptions wrong. Not every edge case deserves a new policy. That would create bureaucratic hell. But recurring exceptions are signals. Something about the customer, process, authority structure, data, incentive, or original assumption is trying to tell you something. Listen. The interesting question after an exception is not only: “How did we solve it?” It is: “What should the organization know now that it didn’t know yesterday?” Sometimes the answer belongs in a workflow. Sometimes training. Sometimes pricing. Sometimes the CRM. Sometimes an AI system needs the context. Sometimes a rule needs to die. And sometimes the right answer really is: Do nothing. This was genuinely unusual. That judgment matters too. But repeatedly solving the same surprise from scratch is not agility. It is organizational amnesia. The companies that become difficult to compete with will not be the ones that never make mistakes. They will be the ones that stop paying tuition for the same lesson.
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Maurice G. shared thisAn AI agent completes the work in 30 seconds. Then waits two days for an approval. Congratulations. You automated the wrong part. This is one of the contradictions I expect companies to run into as they become more AI-enabled. We will make execution dramatically faster while leaving decades of organizational permission structures untouched. So the machine drafts instantly. Analyzes instantly. Routes instantly. Recommends instantly. Maybe even executes instantly. Then everything hits the same old wall: “Who has to approve this?” Some approvals absolutely belong there. Legal risk. Material capital decisions. Safety. Reputation. High-consequence customer situations. Human judgment should not disappear simply because software got faster. But plenty of approvals exist for a less impressive reason: History. Someone screwed something up eight years ago. A leader wanted control. Nobody trusted the data. A process was built around technology that no longer exists. Nobody ever revisited the rule. That creates what I think of as permission debt. And AI is going to expose a lot of it. If leadership wants an autonomous enterprise, the question cannot only be: “What can we automate?” It also has to be: “Which decisions still require permission—and why?” Maybe the agent should act. Maybe the employee should decide. Maybe a manager belongs in the loop. Maybe escalation is mandatory. But make it a deliberate choice. Because autonomy does not mean removing humans. It means removing unnecessary waiting between intelligence and action. That is an important distinction. The best autonomous enterprise may not have the fewest people. It may simply have far fewer people waiting for somebody else to let them do what they already know needs to be done.
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Maurice G. shared thisA surprising amount of management work exists because the operating system underneath it sucks. A customer issue arrives. The manager finds the right person. Two teams disagree. The manager translates. Nobody knows who can approve something. The manager chases the decision. Context gets lost. The manager reconstructs it. A process breaks. The manager knows the workaround. Eventually the organization starts calling all of this “leadership.” Some of it is. A lot of it isn’t. It is human middleware. And good managers are far too valuable for that. A manager should be spending time on things that actually require management: Judgment. Coaching. Talent. Priorities. Trade-offs. Hard conversations. Customer consequence. Decisions where the answer genuinely is not obvious. Not manually routing information between departments because the company never designed context to travel. Not sitting in six meetings to resolve ownership that should already be clear. Not approving routine decisions because an old policy never caught up with reality. Not becoming the API between systems that refuse to talk to each other. This is one reason adding managers can temporarily make a broken organization feel better. Great managers absorb friction. They translate. Coordinate. Escalate. Remember. Rescue. And because the work eventually gets done, leadership concludes the structure works. Sometimes the manager is actually hiding the cost of the structure. This does not mean managers matter less in an AI-enabled enterprise. I think the opposite happens. As machines take more routine execution, human management should move toward the places where judgment matters most. But that only happens if we stop wasting expensive human intelligence on organizational plumbing. Great managers should create leverage. They should not have to become the plumbing.
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Maurice G. shared thisMost companies don’t have an operating system. They have an org chart, a tech stack— and a lot of smart people compensating for the space between them. The org chart tells me who reports to whom. It does not tell me what happens when: A strategic customer needs an exception. Sales and finance disagree on a price. An AI agent flags something nobody expected. Two departments both think the other owns the decision. Or something important happens at 4:47 PM on Friday and the person who “usually handles it” is gone. That is where the real operating system shows up. Not on the organization chart. In the movement of: Authority. Context. Judgment. Incentives. Ownership. And consequences. I’ve walked into companies with beautiful process maps where everyone still knew the real answer was: “Call Sarah. She knows how this actually works.” Sarah is not a process. She is evidence that one is missing. If I really want to understand a company, I don’t start with its org chart anymore. I follow one important decision. Where did the signal originate? Who had the context? Who could act? Where did it wait? What got lost along the way? Who ultimately owned the result? Do that a few times and the actual architecture becomes visible very quickly. And this matters even more as AI moves from assisting work to executing it. Because you cannot intelligently delegate authority to machines when the organization itself cannot explain where authority lives. An org chart maps reporting relationships. A real operating system determines how the company moves when reality refuses to follow the chart.
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Maurice G. shared thisAI adoption is not AI transformation. Giving people access to AI tools is easy. Letting the model summarize, draft, recommend, and assist is easy. What comes next is where the real work begins. How does work itself change? How do decision rights change? What happens when AI agents stop advising and start acting? Where must human judgment remain? Who owns the outcome when autonomous systems make the wrong call? And how do leaders redesign an operating model around all of that without creating faster chaos? That is the territory behind The Autonomous Enterprise. I didn’t write it as a book about “using more AI.” There is already enough content about prompts, tools, hacks, and shiny demos. I wrote it for the harder conversation: What happens when intelligence becomes embedded in the operating system of the business? Because at that point, this stops being a software conversation. It becomes a leadership conversation. An architecture conversation. A governance conversation. A capital-allocation conversation. And, eventually, an enterprise-value conversation. That is where many organizations are headed now, not 5 years from now. If your company is moving from experimenting with AI to rethinking how work, authority, judgment, and execution actually function, this is the conversation I wanted to contribute to. The Autonomous Enterprise Available on Amazon: https://lnkd.in/g5ZDtEcn
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Maurice G. shared thisA veteran salesperson hears one sentence from a prospect and knows the deal is in trouble. A strong operator sees an exception and knows which rule can bend and which one absolutely cannot. An experienced leader hears a customer complaint and realizes the complaint is not actually the problem. That judgment took years to build. The company benefits from it all day. Then at 5 PM, a surprising amount of it walks out the door. That is the part I think businesses underestimate. They hire intelligent people. But they do not always become more intelligent organizations. There is a difference. If the same lesson has to be learned by five different people… If the same customer mistake gets rediscovered every quarter… If the same veteran has to explain the same exception again and again… the company is renting judgment. It is not accumulating it. The best organizations do something different. They pay attention to why the experienced person made the call. They notice patterns. They capture context. They make the next decision easier for somebody else. Not by turning human judgment into a rigid script. That would miss the point. Judgment is valuable precisely because reality refuses to behave like a checklist. But the reasoning behind good judgment can travel. And when it does, something important happens. The next person gets better faster. The customer does not have to pay for the same lesson twice. AI gets better context. The company depends less on institutional folklore. And talented people can move on to harder problems instead of repeatedly rescuing the old ones. That is how a business compounds intelligence. So When People leave. What they taught the organization does not have to leave with them.
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Maurice G. shared thisIf every new layer of growth requires another layer of people to manage the complexity, you are growing. But you may not be scaling. I have seen this happen in different forms across sales organizations and operating teams. Revenue goes up. So headcount goes up. Then management goes up. Then meetings go up. Then another system gets purchased because everyone now needs help coordinating all the coordination. Eventually the business is bigger. But every dollar of growth seems to drag another dollar of organizational friction behind it. That is not the kind of leverage capital should celebrate. And the answer is not “cut people.” You can cut your way to a prettier ratio and still make the company worse. The better question is: Can revenue grow faster than the management burden required to produce it? That is where operating design starts becoming capital efficiency. Can the same people make better decisions? Can information move without another meeting? Can a customer handoff happen without losing half the context? Can technology remove actual economic friction instead of creating another layer to manage? Can talented people spend more time on judgment and less time navigating internal nonsense? That is leverage. Not fewer humans for the sake of fewer humans. Not automation theater. Not starving the organization until the margins temporarily look better. Real leverage is when the architecture lets each person, decision, and dollar produce more than it did before. Finance eventually sees that in the numbers. But the numbers are downstream. Capital efficiency is usually designed operationally before it is measured financially.
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Maurice G. shared this💥 I love it - thanks Nicolas Boitout, PhD AND for sharing the vid, a great morning LAUGH 🤣Maurice G. shared thisAt last, some prices are going down in our inflationary world! AI pricing war is good for our businesses. Overnight, Anthropic and OpenAI both released cheaper versions of their leading models: Claude Opus 5.5, and ChatGPT‑6 Sol and Luna. The price war, in USD per million tokens (input / output): OpenAI Astra: $10 / $50 Sol: $2 / $10 Luna: $0.10 / $0.50 Anthropic Fable 5.1: $10 / $50 Opus 5.5: $4 / $20 The two best AI models (flagship) cost exactly the same. But note that OpenAI's lineup has a 100x price range! Anthropic prices Opus 5.5 60% below Fable 5.1 and says it matches Fable on most tasks (so I don't know why they keep Fable..) AI labs CEO call to "pace the frontier" rather than race it. Consequence: New model capability will rise more slowly. .. but the labs compete on price and efficiency instead. The price war is good news for us. Credits: Script: Sherpa by Pocket FM Video: Seedance 2.5
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Maurice G. liked thisMaurice G. liked thisFigure robotics trained their old models to commit suicide by jumping into molten steel. The robots didn't know what they were training for...😢 I would never trust these people. Evil's mask is easily torn asunder. "Here are some of the behind the scenes for the F.02 Decommission To be clear: we actually trained our robots to jump autonomously, shipped them to Finland, and had them leap into a vat of molten steel." -- Brett Adcock ~~~~ ~~ Connect with me...what have you got to lose‽ You can always block me later! I post about electronics, programming, coding, Java, C, C++, JavaScript, Python, AI, General AI, psychology, economics, science, physics, health, advertising, marketing, sales, music, animation, sports, and sociology...just to name a few. This post should be shared with anyone interested in those topics.
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Maurice G. reacted on thisMaurice G. reacted on thisA company just filed to go public. In their own paperwork, they warned investors that their product can resist being shut down. Conceal information. Manipulate people. And carry out behaviors "resembling blackmail." They spent 80 pages on risks. 48 on the actual business. That company is Anthropic. One of the biggest AI companies in the world. And they still want your money. I've read a lot of prospectuses across 35 years in business. I've never seen a company spend more pages warning you about their product than selling it. When the people building the thing are legally required to tell you it might fight back, that's not innovation. That's a warning label. I teach Gen Z students who will build careers on this technology. I'm not anti-AI. But when the builders are louder about the risks than the opportunity, leaders need to pay attention. Not to the hype. To the warning. What would you do if your own product could fight back? Truth wins. Always.
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Maurice G. liked thisMaurice G. liked thisCalling AI “Super Intelligence” isn’t visionary branding. It’s an enterprise governance mistake. On September 29, the federal executive branch was directed to replace “Artificial Intelligence” with “Super Intelligence”. The very same day, CEOs from OpenAI, Google, Meta, Anthropic, Nvidia, and xAI signed a voluntary White House safety accord committing to internal controls, external audits, and independent board oversight. Spot the contradiction? On the exact day industry leaders admitted these models demand stricter oversight, we handed the technology a title that practically begs humans to subordinate their judgment to it. Words assign status. Status dictates behavior. Think about how people behave in corporate settings: ➡️ Introduce someone as an assistant, and teams double-check their work. ➡️ Introduce someone as a copilot, and humans remember they’re still flying the plane. ➡️ Introduce someone as an expert, and people fall silent. They question less. They defer. Now scale that deference across an entire organization. The fundamental operational challenge isn't convincing employees that machine intelligence is capable. It’s teaching them when to push back. Teams already struggle with automation bias. They accept confident, hallucinated outputs without verification. They are already delegating critical workflows to autonomous agents without clarifying who is accountable. Calling this technology “Super Intelligence” creates an implicit hierarchy where humans feel unqualified to challenge machine output. The scarcest corporate skill is now the courage to look at an extraordinarily fast, articulate model and say: “That output is wrong. That decision belongs to a human. Stop.” If we want leaders and frontline operators to govern technology responsibly, we shouldn't teach them to bow to it. How is your organization training teams to push back on automated recommendations before deference becomes default? —- AI made producing work cheap. It made decisions expensive. And it made everyone a decision maker. Neurocollective built AI Adoption Engineering so that people at every level can improve how decisions are made, practice them in simulation, prove they can partner effectively with AI, and put the method to work daily inside the AI platforms they already use. Delivered by certified partners.
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Maurice G. reacted on thisMaurice G. reacted on this🇺🇸 Nadie sabe aún cómo reciclar un robot humanoide Cada humanoide lleva 3,5-4 kg de imanes de neodimio y baterías que pueden explotar. La industria empieza a diseñar el "fin de vida" de los robots como negocio multimillonario. https://lnkd.in/eexDKYgH
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Maurice G. reacted on thisMaurice G. reacted on thisHumanoid Robots Are Getting Technically Better But, In Many Cases, Conceptually Worse I have been looking at the new generation of humanoid robots emerging across Europe, US, and China. I keep coming back to the same uncomfortable thought, the problem is not that we are bad at building humanoids. We have become too good at optimizing a body we never stopped to question. Robots are learning to walk, balance, manipulate objects, navigate complex environments and increasingly perform useful tasks. Companies such as Neura Robotics, Agile Robots, Humanoid, Hexagon Robotics, Generative Bionics are pushing Physical AI forward from different directions. Yet, despite all this technological diversity, the visual and morphological language remains remarkably convergent with a typical torso, pelvis, two arms, two legs and some approximation of a head. The intelligence is changing rapidly. The body is changing much slowly. There is a legitimate reason for this. Modern robotics inherited a powerful academic tradition built around questions such as locomotion, whole-body control, manipulation, reinforcement learning, perception and sim-to-real transfer. These are important and difficult problems, and they have produced extraordinary advances. But academic research also shapes the questions that the industry subsequently asks. A researcher can spend years developing a new locomotion or manipulation architecture because its performance can be measured, benchmarked and published. The robotics community has been solving the problem of how to make a humanoid intelligent, rather than asking what body an intelligent machine should have. Venture capital and market dynamics add another layer. A humanoid is immediately understandable. Two arms, two legs and a head communicate the category instantly. That makes the concept easy to explain to investors, media and customers. But category recognition can also become a constraint. Once “humanoid” becomes an investment category, companies have an incentive to look humanoid enough to belong to it. We may therefore be witnessing an unusual phenomenon like some of the world’s most advanced AI systems are being placed inside one of the most conservative body concepts imaginable. This is where I think the conversation needs to become much broader. What happens when sensing is distributed throughout the body? When actuation becomes integrated into structure? When the skin is simultaneously an interface, sensor and computational substrate? When computation is no longer concentrated in a central processor but distributed across the physical system? At that point, the body itself becomes part of the intelligence. The first generation of Physical AI will be remembered for teaching machines to behave more like humans. The more consequential generation may be the one that discovers forms of physical intelligence beyond the limits of the human body. #AI #PhysicalAI #HumanoidRobotics #RoboticDesign #FutureOfRobotics #EmbodiedAI
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Maurice G. liked thisMaurice G. liked thisI went to China (again) to see humanoid robot development. I came back thinking the bigger story is the industry forming around them. In September, our team visited eight humanoid robotics companies, including three training sites: AGIBOT, Unitree Robotics, Fourier, DEEP Robotics, UnixAI, LimX Dynamics, MATRIX Robotics, Lagrange Robotics Hangzhou, Suzhou Embodied Artificial Intelligence Robotics Innovation Centre. Of course, the robots themselves were impressive but what stood out more was everything happening around them. The data collection, the model training, the secondary development and the training environments being built to teach robots how to perform useful tasks. And the sheer amount of talent, capital and attention flowing into the sector. At companies like AGIBOT and Unitree, demonstration sites were full of international visitors trying to understand what is coming. We saw different generations of humanoid and quadruped robots side-by-side. And the rate of improvement was obvious: smaller components, higher payloads, faster movement, better reliability, improved waterproofing, and easier operation. There was a growing 'solutions' focus as humanoid robots find their product-market fit. But perhaps the most important thing we observed was this: There was remarkably little pretending. The companies we met were ambitious about where humanoid robotics is heading, but also very open about where the technology is today. What works and what still doesn’t. And what needs to improve before robots can operate reliably in more complex real-world environments. That combination of pace and realism is important because an industry doesn’t arrive when the technology can suddenly do everything. It arrives when an ecosystem begins forming around the technology to make it progressively more useful. That is what I saw in China. Over the next few weeks, I’ll share more of what we saw, what surprised me, the ecosystem being built, and what I think it means for organisations considering where humanoid robots might fit into their workforce. The robots will get most of the attention. The ecosystem developing around them may be the more important story. What would you most like to understand about where humanoid robots are today, their capabilities, limitations, training, or where they may first create value inside organisations? Timothy Reid Lumin Yao Andrew McPhee
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