Group Chat Recap
09 • 27 • 24

'A Bleeping Nightmare'

Top-down transformation nightmares and 2 other takeaways about integrating AI into enterprises from members and guests

Editor’s Note: It was the last Friday in September and so we assembled all our members, experts and guests for our end of the month virtual event. These events are shaped by the group dynamic; we start with a plan, but the group dictates the discourse.

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We came into this session with a plan: provoke Peter Pawlick, head of experience design at Proto. Pawlick published a 5 part series on our platform about AI simulations and his perspective deserved attention. If you haven’t read it yet, here it is in a nutshell: design thinking is dying and many brands and agencies don’t know it yet.

Design thinking is the engine that drives digital transformation. It is an endless iterative and agile process that is designed to ‘move fast and break shit.’ It is more than a method, it is a culture. And that culture is about to be replaced.

Synthetic data and other generative AI systems make it possible to preview ideas before they are designed or built. This means that an enterprise that wants to chase a north star vision can stress test 10,000 pathways to get there before deploying any capital on design or development.

In other words, move fast and break synthetic shit so the first public launch is a guaranteed success (or so the theory goes).

As I was saying, we came into the session with, dare I say, a good plan. But like all good plans, it fizzled on first contact; the group took over the discourse. I love when that happens.

The group was full of agency leaders, enterprise representatives, and founders in AI startups. In other words, all sides of the digital transformation spectrum were covered.

Here are 2 other takeaways from the group - anonymized in the way we always do:

Matt Chmiel

Matt Chmiel

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1. The AI Firewall

We heard perspective from members who have been struggling to sell AI builds and services into corporate clients.

The only groups willing to consider these services are the marketing and customer service departments. The use-case is pretty clear: the content and services can live freely outside of the company firewall; mistakes in this instance are a minor issue rather than a compliance nightmare.

The rest of the organization is dragging its feet, for good reason. As we’ve heard in other events, AI is great at rapid prototyping but not running a product or ensuring compliance. As one insider put it, AI might work for "marketing procurement, but AI within the rest of the organization is going to be pretty slow.”

Top-down AI transformation is a fucking nightmare.

2. Realistic AI

Now let’s drill deeper: what is the conversation like for the few corporations that are willing to integrate AI deeper into the org-chart? We heard a few perspectives that might influence the way you collaborate with partners.

“AI is not a magic button” and “LLMs are not always the answer to the problem.” The real problem is that expectations for how this technology can help is not fully understood. This creates a communication issue that blows up if not addressed the right way.

One of our members put it bluntly: “Top-down AI transformation is a fucking nightmare.” In other words, employees reject it and compliance pushes back. One blocker comes in after another.

Real change is coming from the bottom-up, where employees (or agencies) are hacking together solutions, often without waiting for permission. One group is speeding up brand assets and media buying. Another group uncovered a way to track and deal with disinformation in social media using AI tools. This is opening up new service-offerings that were never before considered.

This perspective is important because it represents an agency offering for recalcitrant enterprises: do not look for the silver bullet; instead, build tools that solve specific problems. Keep adding new tools, solving new problems, eventually developing a suite of services.

We run a Group Chat every week. If you want to participate, inquire about membership here. If you want to keep up with the perspectives that we hear, you can subscribe to our weekly newsletter.

Group Chat Recap
09•19•24

'Holy Bleeping Bleep'

What it feels like to build a functioning CRM in 3 hours, and 4 other takeaways about AI and team management.

Editor’s Note: This Group Chat was a prolific blend of personalities and perspectives spanning a variety of unrelated industries. We provoked this group with practical questions about AI and management. Their respective answers fed into a good discourse. Here’s a public-facing recap)

0:00 / 0:00

People are always asking what happens in our events. We can’t tell you. That’s part of our deal; we have closed-door sessions with real discourse. Our members get to share raw thoughts, compare notes, and develop new connections. And you get this sanitized recap.

This group had 4 members:

  • 2 Founders (1 AI startup and 1 large public company & venture development firm)
  • 1 Chief Digital Officer in prestigious cultural commerce
  • 1 Chief Operating Officer of an agency

They generated 5 takeaways.

Matt Chmiel

Matt Chmiel

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1. Middle Management is Ready for an AI Take Over

In private they don’t mince words: “We want to replace the manager, just like Uber replaced the dispatcher. AI can do a better job at managing people than humans. Managers are the most dysfunctional part of an organization, and people leave jobs because of bad managers, not bad companies.” The role of the manager as we know it is obsolete.

Another participant, tired of slow corporate change, took drastic measures within their company, reshuffling the hierarchy to move faster: “We took our chief creative officer, made them Chief AI Officer, and launched initiatives to trim the fat.” The focus here was cutting out inefficiency and embracing AI as a force for operational speed and innovation.

Managers are the most dysfunctional part of an organization, and people leave jobs because of bad managers, not bad companies.

2. AI Prototyping: Faster Than Bureaucracy Can Keep Up

With greater efficiency comes an unexpected issue. As one exec put it: “We spun up an AI-driven CRM prototype in just three hours, and the reaction was ‘holy fucking shit.’” But the challenge surfaced when this quick success bumped into a familiar corporate problem: maintenance. As this leader explained, “Now, I don’t know what to do. If everyone starts using it, it becomes a product, and then I need to support it.”

The prototype worked so well but it was just a proof of concept. Implementing this thing is where it gets sticky again.

This dilemma—building too fast to scale—captures the double-edged sword of today’s AI landscape. It’s faster to build something tangible than to even discuss building it, but as they pointed out, “The challenge comes when you have to support and scale what was meant to be a prototype.”

3. Mediocrity Won’t Survive

Another issue: people. They put it bluntly: mediocrity will not survive in this new AI-driven world. “Being mediocre is just going to be very difficult.” The pace of AI innovation is outstripping human adaptability. The faster the models retrain, the faster the gaps between skill levels grow. In this environment, anyone slow to adapt will be left behind.

The Chief AI Officer that was mentioned earlier is in charge of hiring new resources. All of them have to demonstrate real opinions, experience, and output from various AI platforms if they want to get hired.

4. Donkeycorn Ventures

Forget unicorns. While some chase billion-dollar valuations, a new concept emerged during the discussion—the “donkey-corn.” This represents small, high-performing companies with $2 million in revenue, built by tiny teams grinding hard in niche markets. The future belongs to companies focused on efficiency and scalability without bloating. Someone referred to the Sam Altman question: “Who’s going to be the first billion-dollar brand owned by one person?” That question does not imply a single business. In this executive's mind, it is a network of donkeycorns.

5. AI-First Companies

One consistent theme throughout the chat was the consensus that any new company starting today is an AI-first company. “You have two types of people: those who embrace change and those who fight it. Only one of them wins.” Whether it’s AI managing teams or personalizing brand experiences, the clear takeaway was this: the future is not waiting for anyone. The business leaders in this chat agreed—AI isn’t just a tool; it’s a revolution in how work will be done.

We run a Group Chat every week. If you want to participate, inquire about membership here. If you want to keep up with the perspectives that we hear, you can subscribe to our weekly newsletter.

SAFE TO FAIL

The Future is a Simulation

Part 5

How

to Get

Started

Peter Pawlick

Principal, Experience, Head of Experience Design, Proto

Peter Pawlick

Principal, Experience, Head of Experience Design, Proto

Editor’s note: At this point, you have spanned the history, and future of simulation and been given the tools to use for this process. The final installment of this series is a metaphorical baton pass to get you started on your simulation.

The future of simulation is ready to go mainstream, expanding far beyond capital-intensive industries, and into the digital realm. If you are leading a digital transformation agency or running an in-house transformation, the next step is to figure out how to leverage this powerful technology in the right way.

The previous article laid out a so-called 4V Framework you can use to shape your simulation. Now you need a determine the value of this process for your own needs. Consider the MASTERY framework as a diagnostic tool to identify how simulation might be relevant to your own company or team:

SAFE TO FAIL

The Future is a Simulation

PART 1

An Ancient Strategy

PART 2

The AI Spring

PART 3

Virtual Prototyping

PART 4

Focus Areas and Tools

PART 5

How to Get Started

Mine

Uncover actionable insights from complex data and market analysis, identifying emerging trends and opportunities. What if you could predict market shifts or new consumer behaviors before they become apparent to your competitors? How might this foresight influence your strategic investments and guide your innovation roadmap?

Architect

Build and test virtual models of potential organizational changes or new business ventures. What if you could visualize the impact of a proposed change across your entire organization before implementation? How would this ability to test different scenarios inform your decisions and mitigate risks associated with transformative changes?

Strategize

Leverage strategic simulations to evaluate and refine various business strategies to understand their potential impacts thoroughly. What if you could simulate the outcomes of different strategic choices and their effects on your business? How could this capability enhance the precision of your strategic planning and accelerate effective decision-making at the executive level?

Train

Enhance your team's capabilities through targeted simulation-based training in a controlled, risk-free environment. What if your leadership and most critical teams could practice navigating complex business scenarios without any real-world risks? How might this training prepare them better for strategic pivots and innovation challenges?

Engage

Align organizational efforts and test the impact of new policies or operational changes before they are implemented. What if you could preview the results of a new organizational policy or a shift in workflow? How would this visibility help in aligning departments and optimizing overall decision-making processes?

Refine

Continually optimize your operations by simulating various process configurations to identify the most efficient models. What if you could streamline operations and improve productivity continuously? How would this commitment to ongoing refinement support sustained growth and operational excellence?

Yield

Maximize the effectiveness of your strategic decisions and operational processes by testing and enhancing outcomes before full-scale implementation. What if you could fine-tune your strategies and operations to ensure they deliver maximum value and impact and focus resources on the strongest areas of return? How would optimizing these yields transform your organization's performance and competitive positioning?

As simulation becomes more accessible, it addresses the critical barrier of risk, enabling businesses to model and test scenarios virtually. This reduces the need for costly physical prototypes and mitigates risks associated with new innovation initiatives. Consequently, organizations can move more confidently and quickly from concept to execution, exploring innovative solutions, iterating rapidly, and bringing advancements to market faster.

The democratization of simulation technology, propelled by AI, promises to reverse the "innovator's dilemma," enabling large corporations to take on a level of risk formerly reserved for startups. These organizations can leverage extensive data for precise simulations, accelerating innovation processes. However, this raises questions about the broader impact. While simulation capabilities might give established companies a competitive edge, relying on vast data suggests those with access to such resources could disproportionately benefit, leading to control consolidation and dominance by larger entities.

Nevertheless, simulation holds potential for broader inclusion by allowing organizations to predict outcomes and evaluate ideas more objectively. This could lead to a more inclusive approach to innovation, where decisions are based on outcomes rather than assumptions.

Accelerating innovation without allowing time for adaptation could cause social and economic disruptions. Balancing rapid technological advancement with strategies to help societies adjust is crucial. As simulation becomes more widespread, fostering thoughtful integration over speed is vital to ensuring these advancements benefit a broader population and bring people along (both employees and customers).

The democratization of simulation offers unprecedented opportunities for precise, cost-effective decision-making. Yet, it also creates a divide between those who can harness its power and those who cannot. Organizations that adapt to this new era are poised for breakthroughs, while others may struggle and face increased risks. Preparing for this shift is crucial; businesses must invest quickly in tools, training, and strategies to leverage simulation effectively and navigate the evolving landscape.

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Focus Areas and Tools

START OVER

PART 1

An Ancient Strategy

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SAFE TO FAIL

The Future is a Simulation

Part 4

Focus

Areas

and Tools

Peter Pawlick

Principal, Experience, Head of Experience Design, Proto

Peter Pawlick

Principal, Experience, Head of Experience Design, Proto

Editor’s note: You've spent the past three installments understanding where simulation technology comes from and why it matters. The fourth update to the series breaks down the available tools and steps that are available today that bring simulation to your innovation project.

In corporate innovation, a range of conditions and variables — including organizational structure, company culture, market dynamics, customer needs, regulatory environments, and technological trends — play crucial roles. Each variable can significantly impact the success and direction of innovation efforts.

At Proto, our Experience Design group uses simulation to increase our clients’ velocity and success rate in corporate venturing. Our approach is organized around four focus areas:

SAFE TO FAIL

The Future is a Simulation

PART 1

An Ancient Strategy

PART 2

The AI Spring

PART 3

Virtual Prototyping

PART 4

Focus Areas and Tools

PART 5

How to Get Started

1. Visualization

We help clients find the best way to represent hypotheses, context, and scenarios to facilitate understanding and decision-making.

2. Virtualization

We help clients model systems and conditions to test and explore future possibilities in a controlled, safe way.

3. Validation

We help clients lower their organizational barriers to experimentation by making it faster, easier, and less risky to learn by doing.

4. Variation

We help clients test assumptions across a range of conditions to explore how different variables influence outcomes.

In today's complex business environment, senior executives and C-level officers should promote the use of simulation technologies.

These tools help navigate strategic challenges effectively and ensure that all organizational levels understand and pursue common goals.

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1. Strategic Clarity and Alignment

Visualization simplifies complex data, ensuring everyone in the organization understands the strategic goals and their roles in achieving them. This is particularly important for addressing complicated business issues or entering new markets.

Claude Sonnet’s ability to produce simulated 3D physics using WebGL allows designers to create and test detailed digital prototypes early in development. This reduces the need for costly physical prototypes and extensive user testing, facilitating rapid iteration and real-time feedback. Similarly, automation powered by GPT-4 generates Figma designs based on product requirement documents (PRDs). In both cases, AI-enabled visualization bridges the imagination gap and accelerates the transition from concept to tangible reality, making it faster and easier to test and iterate on ideas quickly and cost-effectively. 

Future advancements will integrate real-time adjustments based on predictive analytics and user feedback, further shortening development cycles and allowing for comprehensive virtual prototyping environments. This transforms the design process by bridging the gap between imagination and empirical validation.

2. Risk Mitigation and Cost Efficiency

Virtualization allows for the testing of strategies in a simulated environment, reducing the financial risks of new initiatives. This is crucial in industries where errors are costly, as it allows potential problems to be identified and addressed before they occur.

The Decision Lab leverages behavioral science and AI to de-risk innovation by predicting consumer behavior and refining products to better meet market demands. This helps businesses develop new revenue models aligned with market needs, reducing risks associated with new launches. In 2023, IBM introduced Watsonx, a suite of AI tools designed to modernize business processes and improve data-driven decision-making, further reducing risks in innovation planning by analyzing data and simulating business scenarios. This virtual approach to risk management allows strategies to be tested in a controlled environment, identifying potential problems early and significantly reducing financial risks. 

As AI evolves, tools will incorporate advanced machine learning techniques like reinforcement learning and federated learning, leveraging real-time data streams for deeper insights and accurate predictions while maintaining data privacy. This enhances risk mitigation by allowing businesses to address issues proactively and make more informed decisions.

3. Data-Driven Decisions

Validation through simulation provides data that supports decision-making processes. This method ensures that new ideas are not only theoretically sound but also empirically viable, allowing decisions to be based on solid evidence rather than assumptions and anecdotes.

Pecan AI uses predictive analytics to analyze customer data, helping businesses forecast demand and tailor their offerings to specific consumer needs. Similarly, Neurons Inc. utilizes neuromarketing tools to simulate and predict consumer responses to various stimuli, enabling companies to refine products and marketing strategies. These tools simulate responses of virtual consumers to various stimuli, providing deep insights into consumer behavior without the need for traditional market research.

Findings from recent studies show that large language models (LLMs) like GPT-4 can predict the outcomes of social science experiments with high accuracy, suggesting their potential for product testing and market analysis. Future advancements will further enhance these capabilities, allowing for hyper-personalization and continuous product adjustments based on real-time data. This approach transforms the innovation process by validating concepts with data from synthetic audiences, reducing product failure risks, and ensuring alignment with market demands.

4. Agility and Market Responsiveness

Variation in simulation tests the impact of changes in one part of the system on the entire business. This capability is essential for quickly adapting to market changes and maintaining competitiveness, as it allows for swift and efficient responses to new challenges and opportunities.

AI platforms like Arena AI enable continuous monitoring and real-time optimization of processes, allowing companies to swiftly adapt to market changes. This agility is achieved through real-time adaptive learning mechanisms that predict market trends and enable rapid scenario testing for dynamic strategy adjustments. Future AI systems will further enhance this capability by integrating more sophisticated real-time learning and prediction algorithms, allowing businesses to dynamically adjust their strategies and maintain a competitive edge. This enhances market responsiveness, ensuring that companies can quickly respond to new challenges and opportunities, maintaining competitiveness in a rapidly changing market landscape.

By anticipating these shifts and advocating for these simulation principles, leaders ensure their teams are equipped to make informed decisions and adapt to changes, positioning the organization for ongoing success and innovation.

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PART 3

Virtual Prototyping

NEXT

PART 5

How to Get Started

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SAFE TO FAIL

The Future is a Simulation

Part 3

Virtual

Prototyping

Peter Pawlick

Principal, Experience, Head of Experience Design, Proto

Peter Pawlick

Principal, Experience, Head of Experience Design, Proto

Editor’s note: The third installment of this series covers the approaching end of the era of iterative design. The rise of virtual prototyping means the fall of design thinking, otherwise knows as 'move fast and break things.'

For most companies, innovation is an organizational, social, and cultural challenge even over a design or technical challenge. The root of all of these challenges is risk — specifically, the complexity and ambiguity that make it difficult to assess and manage risk.

In October 2023, about four months before NVIDIA posted the largest single-day gain in the history of capitalism, Jensen Huang (Co-founder & CEO) gave an interview on the implications of “virtually prototyping” the Riva 128 3D graphics chip in 1997: not only a watershed moment in NVIDIA’s history but a paradigm shift in how chips would be produced henceforth. He asked, “What would you do if you knew your first attempt would be perfect?” He goes on to describe how he effectively bet the company on the success of the Riva 128 before it was fabricated, a high-stakes gamble that he was able to make thanks to the virtual prototyping process.

What would you do if you knew your first attempt would be perfect?

JENSEN HUANG

SAFE TO FAIL

The Future is a Simulation

PART 1

An Ancient Strategy

PART 2

The AI Spring

PART 3

Virtual Prototyping

PART 4

Focus Areas and Tools

PART 5

How to Get Started

It’s intriguing not least because it flies in the face of the stale hegemonies of Design Thinking, Lean, and Agile, all of which favor iteration over perfect first attempts. All three sprouted from the corporatism that predated them but eventually became ideologies. Co-creation, empathy, feedback loops, iteration, Post-its: these were all only ever intended to be means to an end, tactics in service of the real goals of getting it right, of shipping, of winning. Somewhere along the way, the means became the end.

Here are just a few examples of how simulation challenges or obviates Design Thinking, Lean, and Agile Methodologies.

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1. Efficiency

Simulation allows for rapid testing of scenarios and variables in a virtual environment. This capability dramatically accelerates the innovation process, potentially bypassing the iterative cycles of prototyping and feedback central to Design Thinking and Agile methodologies. Where Design Thinking might spend weeks on empathy work and prototyping, and Agile cycles through sprints to evolve product features, simulation can model these steps much faster, providing immediate insights based on complex data interactions.

2. Precision

Unlike Lean's focus on eliminating waste through incremental improvements and Agile's emphasis on adaptability through continuous iteration, simulation offers a way to predict outcomes before any physical or substantial financial investments are made. This ability to foresee and troubleshoot potential issues in a virtual setting minimizes risk and reduces the likelihood of costly pivots later.

3. Scope

Simulation enables testing in environments and scenarios that might be impractical or impossible to arrange in real life due to cost, safety, or geographical limitations. This expands the scope of innovation, allowing for the exploration of ideas that might be dismissed as too risky or expensive under traditional methods.

4. Scale

Research with synthetic audiences, composed of agents modeled on real users, significantly speeds up data collection and refinement. Each agent behaves based on patterns derived from real user data, allowing for accurate simulations of complex interactions. This enables continuous testing, offering deeper insights and enhanced scalability beyond traditional methods.

4. Integration

As products and services increasingly integrate with complex systems, the limitations of traditional methodologies to handle multi-layered complexity become apparent. Simulation provides a holistic view that can integrate these complex systems into the innovation process more seamlessly than the relatively compartmentalized approaches of Design Thinking, Agile, and Lean.

These all add up to significant savings in time and money, while also expanding the reach, size and complexity of the testing we can perform. 

Just as GPU architecture design is a complex process situated within a comprehensive system of variables — including operating conditions, system configurations, software and drivers, and usage scenarios — corporate innovation similarly unfolds within the complex ecosystem of a corporation. 

In GPU design, variables like operating conditions affect performance and durability, while system configurations and software optimizations directly influence efficiency and capability. These elements must be balanced and optimized to achieve the desired outcomes, necessitating a deep understanding of both the components involved and their interactions within the broader system.

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An Ancient Strategy

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Culture

Wins

in

Paris

Our Newsletter Gets a Response

Carlos Mare

Senior Creative Director, M139 Design Studio

Carlos Mare

Senior Creative Director, M139 Design Studio

Editor’s Note: This response to last week’s newsletter is coming directly from our inbox. We loved it so much we turned it into a post. Carlos emailed me the day after we published the newsletter wondering if any brands won the Olympics. If you want to get your response in our feed, first you have to subscribe and then you can email me your reactions.

I read the very thoughtful piece questioning whether brands won at the Olympics which was provoking on several levels for me since I was in Paris participating in pre-Olympic events. My overall observation when I arrived in Paris was how minimal the advertising and discussions (mindful of the electoral debacle that unfolded) were regarding the games, it was as if it were an irritation for the Parisians to have this global imposition on them. That said there were specific things that were unlike the spirit of Americans who have a huge enthusiasm and budget for the games, the Parisians seemed, to me, more interested in their politics, culture, and their way of life. 

CULTURE WON THE OLYMPICS, SPECIFICALLY HIP HOP.

If there was a winner in all of this, it would be the branding of American culture and its impact on the world stage, from Basketball, Track, and other US-dominated events, but namely and uniquely Breaking, a dance form that like Graffiti and Rap transformed the world and marketing itself. As an insider and longstanding authority of the culture I have participated and seen its development since day one, so my perspective is rather unique. 

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Hip Hop icon Snoop Dog was a unique paradigm shift from standard programming for the network and the IOC, both of which are very conservative culture classes. Snoop gave them the lift needed to reach a broader and more diverse audience, which by all metrics has been drifting away from the games as a new generation gets hip to the disparities and consequences of the games on their communities. The young ain’t 'playing' so to speak, they want inclusion and equity, so by virtue of Snoop being present it was a moment of 'you better recognize' and a continuum from our recent 50th Hip Hop Anniversary. Our impact on brands and culture is resounding and continues to be so with and without big brands, but this moment presents something wholly different in how brands brand Hip Hop. I would ask you to watch the moving video by Montefiore Hospital and its lack of branding and focus on storytelling and culture. 

Also notable was NIKE and its collaboration with famed Graffiti artist Futura who launched their novel sneaker JAM for the Breaking community and was featured in competition. The commercial was phenomenal and culturally relevant to the vast underrepresented community. It also aired on prime time! Another amazing moment of branding around this was the NIKE event at the Pompidou Center where I was previously for–of all things–an exhibition for artist Brancusi and where NIKE installed an enormous LED system on its facade to celebrate Futura and the NIKE shoe with an onsite event featuring a fantastic Breaking performance by a crew of dancers.

Can culture be a brand? Of course it can, and the question that has remained for me since day one is who will benefit most?

Notably, the Breaking portion of the events left people's heads scratching as to whether it was a sport, and who the hell was that Australian dancer? Criticism aside, her performance also elevated its exposure globally. What may be overlooked was how Delta Airlines’ adverts featured Breaker and medal-winning dancer Victor Montalvo, or his New York Times Magazine feature. He was not alone either– American dancer Sunny Choi also had her commercial spots, too. 

Throughout Paris, there were a bunch of cultural happenings around Hip Hop Culture and Street Culture at very high levels which included my installations at the Olympic Museum, mural at CARRE BAUDOUIN, and Bboy sculpture on the river Seine at the foot of the events which was a branding effort for Breaking and the culture. Hip Hop was also present on ads all over the Metro promoting a staged event at the famed Théâtre du Châtelet. Notably, I went to a Street Art Exhibition at the 'precious' Petit Palis' where I met up with my friend Sheperd Fairey who was exhibiting, and we both admitted to the unlikeliness of all that was happening if it were not for Hip Hop Culture.

So in closing, can culture be a brand? Of course it can, and the question that has remained for me since day one is who will benefit most? 

The answer is society, and in this case, the caveats here are who will cannibalize it most and who will protect it most?

I'm on guard.

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SAFE TO FAIL

The Future is a Simulation

Part 2

The

AI Spring

Peter Pawlick

Principal, Experience, Head of Experience Design, Proto

Peter Pawlick

Principal, Experience, Head of Experience Design, Proto

Editor’s note: The second installment of the Safe to Fail series is a jump-cut from antiquity to the 20th century. The AI spring is mainstreaming simulation techniques that were previously confined to modern-day capital-intensive industries. What can an automotive factory teach a digital platform about simulation?

In the first two decades of the 21st century, before the AI Spring, simulation in the context of innovation remained largely confined to high-risk or capital-intensive industries such as aerospace, automotive, and nuclear energy, where the stakes of failure could be catastrophic both in safety and financial impact. These simulations were resource-intensive, requiring substantial computational power and expertise, which limited their accessibility and application to only those industries that could afford such investments. For instance:

SAFE TO FAIL

The Future is a Simulation

PART 1

An Ancient Strategy

PART 2

The AI Spring

PART 3

Virtual Prototyping

PART 4

Focus Areas and Tools

PART 5

How to Get Started

1. In the pharmaceutical industry...

... computational simulation has broad applications including predicting drug interactions with biological targets, optimizing clinical trial designs, enhancing personalized medicine approaches, and supplementing regulatory approval processes. 

2. In the automotive sector...

... simulating the design of new manufacturing lines for electric vehicles can significantly reduce production errors and streamline operations, safeguarding investments that often exceed hundreds of millions of dollars. 

3. In the oil industry...

... simulation is used for planning and executing offshore drilling projects by predicting geological conditions and optimizing drilling strategies.

4. In climate science...

... simulations are used to model complex interactions in atmospheric and oceanic systems to predict climate trends and extreme weather. These models synthesize data from diverse sources, including satellite imagery and ground-based weather stations, to simulate processes such as radiative transfer and the hydrological cycle. They provide insights into how climate systems react to various factors, supporting both scientific research and strategy, for example, in the context of international climate negotiations and national policy development.

For endeavors that did not meet the highest thresholds of financial or safety risk, other empirical and analytical risk mitigation strategies were more commonly employed due to cost-benefit considerations. Even for projects undertaken by large corporations in the range of $1M-$10M — which is to say, the vast majority — the financial and resource investments required for sophisticated simulations were too difficult to justify. Additionally, the time and training required to develop and implement simulations can delay project timelines, which may be counterproductive in dynamic and fast-paced environments. Consequently, organizations tended to reserve simulation tools for larger, higher-stakes projects where the potential risks, complexity, and benefits significantly outweighed the costs of simulation technology. 

The extent of adoption of simulation in 2024 is equivalent to the adoption of computers in 1970: broadly used in specific industries — but not by most people and not in most industries. But as simulation becomes democratized and barriers to adopting simulation are flattened, there is growing potential to apply simulation to a host of new contexts. 

AI algorithms can analyze vast amounts of data more quickly and with greater accuracy than traditional methods. This capability allows for more detailed and comprehensive models, enabling simulations that are not only faster but also offer more precise predictions and insights.

The AI Spring will democratize simulation in several ways:

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1. Reducing Cost

AI technologies are driving down the costs of simulations. Advances in machine learning algorithms and more efficient computing architectures are enabling more complex simulations to be run at a fraction of the previous costs. This democratization of technology allows smaller companies and those in less capital-intensive industries to adopt simulation tools.

2. Increasing Accessibility and Ease of Use

AI is also making simulation tools more user-friendly and accessible. AI-powered interfaces and automated systems are reducing the level of expertise required to design and run simulations, opening up the technology to non-specialists and integrating it into regular business operations.

3. Expanding Applications

With these advancements, simulations are now being used in new and diverse contexts. For example, in healthcare, AI-driven simulations are used for predicting patient outcomes, personalizing treatment plans, and training medical staff. In urban planning, they assist in everything from traffic flow analysis to disaster response and environmental impact studies. In retail, simulations help in optimizing supply chain logistics, customer experience, and even in predicting market trends.

By reducing the barriers to entry, the AI Spring is significantly broadening the range of applications for simulation, making it a commonplace tool not just for high-stakes engineering projects but also for everyday business and organizational activities. This expansion is expected to continue as AI technology evolves, further integrating simulation into the fabric of modern industry and society.

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SAFE TO FAIL

The Future is a Simulation

Part 1

An

Ancient

Strategy

Peter Pawlick

Principal, Experience, Head of Experience Design, Proto

Editor’s note: This is our first epic provocation, we mean that literally and on multiple levels. On one hand, the sheer length, depth, and breadth of this argument required serialization. On the other hand, the thinking that is represented here will quite literally take us to the very earliest days of human history. It is time to reflect on the role that AI-generated simulation is going to play in digital transformation.

An overlooked implication of the AI Spring is the democratization of simulation, which is set to change how businesses make decisions on a scale that’s still hard to appreciate. It will challenge and potentially displace methods and schools of thought like Design Thinking, Agile, and Lean, which have come to dominate innovation processes over the past two decades. 

A simulation is a representation of a system that could exist in the real world.

SAFE TO FAIL

The Future is a Simulation

PART 1

An Ancient Strategy

PART 2

The AI Spring

PART 3

Virtual Prototyping

PART 4

Focus Areas and Tools

PART 5

How to Get Started

Semantically speaking, simulation is a process, not an object. We build a model or simulator; we run a simulation. Physical simulation has been used since antiquity to represent complex systems, predict outcomes, and enhance decision-making. For millennia, simulation has been used when the stakes were too high or the possible outcomes too diverse to rely solely on direct observation or trial and error. Indeed, it’s fair to say that many of humanity’s most significant discoveries and innovations have been enabled by simulation.

To understand the transformative potential of simulation on business practices and innovation strategies, it is helpful to consider how it evolved and what is driving its expanding relevance.

Many of humanity’s most significant discoveries and innovations have been enabled by simulation. 

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Ancient simulators included symbolic, visual, physical, and mechanical systems to represent astronomical systems and predict celestial events. Early examples include the sophisticated calendric systems developed in Mesoamerica around the 1st millennium BCE, which codified astronomical observations to model and inform planting and harvesting cycles, and ceremonial timings. The Antikythera mechanism (c. 100 BCE) was an early example of an orrery — a working, mechanical model of the solar system. Comprised of 69 bronze gears housed in a 13-inch wooden box with a crank handle, it could accurately predict the position of the sun, moon, and the planets known at the time. It is generally acknowledged to have been the world’s first analog computer. 

The construction of the Roman aqueducts (c. 312 BCE) applied simulation to hydraulic engineering, combining scale modeling and prototyping with empirical and iterative methods. From 150 CE onwards, the development of medieval globes and maps, including Al-Zarqali's astrolabe and Al-Idrisi’s world map (1154), expanded simulation’s role in geography by enhancing the precision of cartographic details, enabling long-distance exploration and territorial administration.

The Renaissance marked a period of significant refinement in simulation tools, with the creation of mechanical models such as Tycho Brahe's armillary sphere (c. 1580) and Galileo's inclined plane experiments (c. 1603). These tools enabled more accurate simulations of physical phenomena, crucial for validating the laws of celestial and terrestrial mechanics. Leonardo da Vinci’s use of detailed models for architectural and mechanical designs bridged the gap between theoretical science and practical application, enhancing the design process with a higher fidelity of conceptualization and testing. 

During the 17th to 19th centuries, the introduction of war games, notably the Prussian Kriegsspiel, illustrated how simulation could be strategically applied to military training and planning, offering a “safe-to-fail” environment to test battle strategies and tactics. Additionally, the planning of major infrastructure projects like the Erie Canal and exploratory missions like the Lewis and Clark Expedition showcased simulation’s relevance in logistical and operational planning on large scales. Another significant advancement during this period was Kelvin's tide-predicting machine, developed in the late 19th century. This mechanical computer had profound implications for naval planning and the safety of maritime operations, further demonstrating the practical applications of simulation in both military and civilian contexts.

Indeed, it’s fair to say that many of humanity’s most significant discoveries and innovations have been enabled by simulation.

As the field of simulation evolved into the mid-20th century, the transition from mechanical and analog systems to digital computing opened new dimensions in simulation capabilities. A notable example from this era was the Link Trainer, an early flight simulator used for pilot training during and after World War II, which represents the progression from purely mechanical simulators to more integrated systems involving electrical components. The Electronic Numerical Integrator and Compute (ENIAC), developed in 1945, was the first electronic general-purpose computer. It was designed to calculate artillery firing tables for the United States Army's Ballistic Research Laboratory. Its ability to be reprogrammed to solve a full range of computing problems made it a pivotal development in the evolution of digital computers. This marked the start of a new era in which computers could handle increasingly complex simulations, fundamentally transforming numerous fields by providing a powerful tool for analysis and forecasting.

One of the earliest examples of computer-assisted simulation in a business context was the General Purpose Simulation System (GPSS).

In the post-war period, the emergence of digital computers enabled the simulation of complex, multivariate systems, enhancing decision-making across scientific, engineering, and economic disciplines. One of the earliest examples of computer-assisted simulation in a business context was the General Purpose Simulation System (GPSS), developed by Geoffrey Gordon at IBM in 1961. Designed as a discrete event simulator for teleprocessing networks, GPSS quickly became foundational in various business applications like manufacturing process optimization and logistics management.

As GPSS and similar systems proved their utility, simulation expanded into broader business and industry areas. It allowed businesses to visualize and manipulate complex systems, extending beyond initial telecom and logistical applications to include processes like manufacturing and telecommunications networks. Through simulating different scenarios, businesses could optimize processes, manage resources, and improve efficiency. 

As it had been for millennia, simulation was deployed in service of challenges that were both complex and high-stakes. However, the primary focus during this period was on enhancing operational efficiency rather than driving innovation, which remained a narrow application.

SAFE TO FAIL

The Future is a Simulation

Design thinking is going to be replaced with AI-generated simulation technology. What does that look like? We wrote a multi-part series about it.

NEXT

PART 2

The AI Spring

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RSS Feeds Are the Solution to Really Bad Search and Social Trends

RSS Feed

The future of media comes from the past

Overheard at ON_Discourse

Overheard at
ON_Discourse

EXPLORE ISSUE #006

Editor’s note: The following perspective opened our minds. We had no idea that RSS feeds were ever going to resume any relevance in the next internet. One of the underappreciated aspects of this feature is that each individual RSS feed can eventually be trained to deliver only hyper-personal content from a singular, trusted newsroom.

This post was written by a human and narrated by an AI-generated voice. (powered by Wondercraft.ai).

0:00 / 0:00

I ran a major tech publication for over a decade. I recently left that role and that platform to start a new media venture. My new venture is very different. The old site was huge; my startup is not. My old site drew 10M monthly uniques from search and social channels; my startup has terrible SEO and a light social presence. The old site ran ads; the startup runs a paywall. Like I said: different.

I designed the startup to thrive in the next internet, which will feel smaller than the social web. Audience will matter more than traffic; subscriptions will matter more than ads; and reporting will matter more than managing. The most successful media brands will be closer to the work, closer to the audience, and largely disengaged from platforms. The next internet will be built for direct relationships between brands and audiences. This is why my startup resurrected the RSS feed.

Personalized RSS

The newsletter trend that sparked during the pandemic signaled an important trend: people want to get their content directly from the source. We found a new way to capitalize on this trend by leveraging and stretching a technology that is 25 years old. While the RSS feed still thrives as a podcast distribution channel (listen wherever you get your podcasts), its ability to distribute text-based content has not evolved much, until now.

In order to modernize this tech, we had to create a way to monetize the RSS feed. It was surprisingly complicated to find a way to gate the RSS feed so that it could not be leaked and shared among non-subscribers. We had to find two separate companies that could create personalized RSS feeds at scale, and then sync them with paid customer IDs in our CMS. As far as we know, this is the first text-based, subscriber-gated, personalized RSS feed from a publication. The individual feed stops once a subscription is canceled and we are notified if a personalized feed is accessed by hundreds of different devices and IP addresses.

It’s old tech, but it resonated with users. It helped us generate a lot of new subscribers who reported being very happy with this service. They can now find our material much more easily without having to come to our website and without having to deal with the roll of the dice as to whether they will see it on social platforms because all of the socials are just so crowded and desperate right now.

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While the RSS feed still thrives as a podcast distribution channel, its ability to distribute text-based content has not evolved much, until now.

Diminishing Social

Regarding social platforms: I think the platforms are already starting to become completely unusable. Really middling, low-effort, shitty AI content is flooding the platforms, making discoverability a huge issue. This trend is not going to stop; it will keep getting worse.

When you log on to Facebook right now you are inundated with weird AI generated images that Facebook seemingly has no idea how to moderate or how to prevent from going viral. Google is constantly tweaking its algorithm, but it’s often pushing stuff that people don’t want to see. And I have yet to see an AI news summary that doesn’t lose some context.

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A New Ecosystem

The media ecosystem is going to split into 3 tiers: the massive media companies like the New York Times are going to offer mass coverage of everything; the middling mass of AI-generated content that is created by bots for bots for programmatic ads; and the small, independent media companies that have direct relationships with audiences.

In this ecosystem there is going to be a market for things that are distinctly human-created

You

Are Wrong,

Saneel

EXPLORE ISSUE #006

Catch up on the debate

Do not expect the next tech innovation to come out of a garage

The next internet is for the platforms

AI Will Reverse the Innovator's Dilemma Market leaders are the secret winners to AI disruption

Saneel Radia

04•01•24

You Are Wrong, Saneel AI will democratize innovation which means we will see it come from smaller companies and creators

Overheard at ON_Discourse

05•06•24

REACTION

Overheard at ON_Discourse

Editor’s note: We published Saneel’s post about innovation in early April and recently received a message that directly challenges his thesis. At ON_Discourse, we live for this kind of dialogue. It doesn’t hurt when the post has a substantive Jeff Goldblum reference.

This post was written by a human and narrated by an AI-generated voice. (powered by Wondercraft.ai).

0:00 / 0:00

Saneel, your argument is too broad. On one hand, I agree that the platforms have more resources, data, and capital to create new kinds of experiences in the next internet, but that does not mean all innovation will be coming from them. As Jeff Goldblum said, life finds a way. In this regard, the human need to create, to push, to thwart, to resist, to stand out, to communicate, and to connect will drive new experiences without the extra push that comes from the platforms.

I have seen it in my own field of luxury goods: AI tools are opening new capacities and capabilities to smaller and smaller entities. As a result of this, more and more independent creators are competing with conventional design teams. AI is scaling their inherent taste into studio-quality products.

I don’t know what it will look like; I can’t predict, and I don’t think you can either. All I know is that human creativity is getting amplified by this technology. The innovation might not come from a garage, but I do not expect it to come from the board room either.

Let’s see…

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