All posts by jederrick

I am a serial creator of technologies and businesses.

Launch of Authentrics.ai

As most people that follow this blog know, I’ve been involved in the world of startups for about two decades. Focus has been on technologies that can transform markets solving hard(er) problems. Several of these involved semiconductor-based hardware systems while others were pure software to make at scale compute more efficient and more performant. 

Over the past decade or more many of the software companies that I’ve been assisting have been AI/ML related. AetherBio (using AI/ML to optimize genetics for biological factories), Bitfusion.io (providing a platform for AI/ML workloads across CPU, FPU, and FPGAs/Hardware accelerators), OnSpecta (3X optimized performance for AI/ML Inference), a SBIR Phase-II for the US Navy, Salience Labs (photonics-based compute), and currently Austin-GIS (vision processing for security and advertising delivery). The technologies applied within AI/ML neural networks and processing systems have not only made made great strides in data set size and processing, but also some significant advances in formulation of problems. The first neural network I personally constructed was very, very limited and was based on a switched capacitor structure back in my university days and only mimicked a couple of synapses. The advances have been enormous and it is not hyperbole to say exponential with hundreds of thousands times the compute available today.

However, even as these advances have occurred and we are seeing broader applicability and adoption of AI/ML, we still face several monumental challenges that have not been addressed because ultimately AI/ML systems rely on content, data, and appropriate training and governance of operation. For a while and certainly during 2023 the challenge of trustworthiness of operation, authenticity, and providence of training content have emerged as key factors for AI/ML adoption and use. Even with all other hype and advances in AI, these factors have not been adequately addressed and only recently gained the awareness and concern that they warrant.

It was for this reason, that during 2023 I set out to study and attempt to address the issue of measuring the impact and value of content within an AI/ML neural network. The result of that study / work was an invention and associated IP filing titled “CONTENT ATTRIBUTION SYSTEM FOR ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING SYSTEMS“. This algorithm provides the ability to look with the structure and weights of a neural network over time and determine how the neural network is affected by new training data sets, operation, and tuning. 

Authentrics.ai was formed to commercialize this technology and bring this capability to market. Importantly, it is not a new method of creating a neural network but rather it allows periodic measurement and monitoring of neural networks to determine the value of training sets, impact of each training set on the computation of results, and determine the contribution of each data set to the results that the neural network produces. The company will provide this as a SaaS and as an on-premise software solution and is applicable to any system that utilizes neural networks. If not obvious, this has application to companies who want need to trust their AI, optimize training processes, and value the content that they use.

More information about Authentrics.ai is available at our website and Linkedin company page. You can also reach out directly to John.Derrick@Authentics.ai.

Thanks for reading and if you have questions about the offering, the company, or how you could get involved, please reach out. 


Genesis of Productive Innovation

Having been in the trenches of technology startups since the end of the last millennium (ouch), I’ve seen a lot (more than a thousand)  startup pitches / plans and have led six startups, consulted on a dozen or so, advised and mentored a few dozen, and invested in very few.  
Along the way, I’d like to think that I’ve gained some insight / wisdom about how to build businesses and experience some success along the way.  Hopefully that is true as I spend a few days a week mentoring early stage entrepreneurs on exactly that. 
I generally operate within deep tech, across a lot of different areas including AI/ML, special purpose or very high performance compute across digital, analog, and sometimes photonics, energy tech, and a few others, but as broad as these sound, they are actually all within the area where deep knowledge and expertise matter far more than a company’s scale or the amount of available capital.  In short, opportunities where concentrated expertise can outperform the market.

Inventions and innovations are closely related, but not exactly the same.  One mistake we technologists often get caught in is convincing ourselves that lot’s of invention equates to innovation, it doesn’t in practice.  

Webster would say that Innovation means.

  1. the introduction of something new
  2. a new idea, method, or device

The practical definition of innovation for an entrepreneur looking to start or scale a business is the introduction of something new that solves an experienced problem in a new and unique manner that provides an advantage to the market and / or to you the provider.  This is the type of innovation that will provide an advantage to a small group with deep knowledge and expertise an opportunity in the marketplace.  

So talking about the Genesis of Practical Innovation, there are really three key variables that must present: 

  1. deep understanding of a market problem 
  2. an innovation, enabled by invention or construct, that uniquely or advantageously solves the problem 
  3. a method of providing that innovation to the marketplace

Though there are a lot of people who seek to be entrepreneurs, I strongly advise them to develop deeper understanding of the market and the challenges within it.  Some markets have very clear challenges like data processing like faster, lower power, and more secure.  Others are far more difficult to find challenges that are high enough on the customer pain list for the market to engage with them.  Once they more fully understand the pain point, then and only then are they likely to create a solution that is viable.

The innovation, often coupled or supported with invention, needs to address the challenge without too much additional pain and effort by either the entrepreneur or the customer.  For example, in compute I have seen many startups that provided processor architectures with far better characteristics than Intel, ARM, or DSPs, but few have been even marginally successful because they required new software, toolchains, or had other major constraints on use.  The few that were successful, including two of my own startups allowed the customers to use existing code without recompilation to experience 10-20 times performance gains.  One of the companies that I advised that was acquired provided 3X performance gains as a compilation step.  The companies that required software to be changed either had little to no success.

 Lastly, a great product with no viable path to market is not a real product or at least is not a good foundation for a  company.  Sometimes this overlaps with technology integration, but often it is a lack market entry points or no viable method of funding to reach the market.  Startups must be able to provide the innovation to the market within the time and funding constraints required by the market.  

So in short, focus on innovation that can have major impacts to the market that can be demonstrated and proven with minor amounts of time and resource.  Focusing on these factors will save you years of working on projects that will ultimately fail and increase the value of your time.  While working as an EIR (Entrepreneur in Residence) at a $3B venture firm in Austin, I spent a lot of time meeting and strategizing with startups that had okay, sometimes great, technology, but they were solving a problem that the market did not view as top three challenges or they required far too much time and capital to prove it out.  Some we were able to refocus and drive to success and others realized that they had not really created a startup worthy innovation and moved to better things.

In closing, having created a number of startups, 75% or so with actual exits, I will say two things.  If you want to be an entrepreneur, understand what is really required for success.  In short, don’t just have a cool idea or technology, but understand what it requires to be successful in funding, building, and exiting a company based on that technology.  The second, though admirable to challenge yourself to be entrepreneurial, the decision to make that leap should be driven by a demonstrated need in the market and an opportunity to innovate with key unique expertise or insight into key challenges of a market.  I’ve seen too many entrepreneurs fail because they either had a technology or a market need, but they did not have both.

If this post raises questions or comments, please reach out ( jederrick@icloud.com ). There are a lot of tools available to start work on fleshing out a startup and some are highly recommended like Lean Canvas, Kaufmann Foundation business model templates, etc..  but I’ve also developed some simple tools to answer fundamental questions like “does this make sense as a startup” around capitalization models, customer / market models, and process flows for key activities in deep tech entrepreneurship, to aid in understanding what is vital to success.  I have used this approach for a few dozen opportunities over the years and glad to discuss and share freely.