Highlighting Data Intelligence with Databricks & Bonbon’s Reward Innovation | E2020

Databricks’ Naveen Rao joins Alex Wilhelm 0s

• The conversation revolves around the motivations and goals of startups, with a focus on those that are mission-driven and aim to make a positive impact on humanity, such as Databricks, which is one of the most valuable private market technology companies in the world today 10s.
• Not all startups are formed with the same mission-driven approach, and some may prioritize making money over considering the broader implications of their technology on humanity 25s.
• The initial motivations of Databricks' founders were centered around their care for the field of technology and its potential impact on humanity, with a focus on AI and its effects on society 14s.
• Databricks has made significant investments and acquisitions, including a $500 million series I funding round that valued the company at around $43 billion, as well as the purchase of Mosaic ML 1m37s.
• Naveen Rao, the CEO and co-founder of Mosaic ML, is a guest on the show, and his company was acquired by Databricks over a year ago, with much having happened in the world of AI, particularly on the open source front, since then 2m5s.
• The host, Alex, welcomes Naveen Rao back to the show to discuss the developments in the world of AI and Databricks' role in it 2m7s.

Acquisition of Mosaic ML and strategy 2m10s

• The acquisition of Mosaic ML by Databricks was a $1.3-1.4 billion deal that took approximately 62 days to close from the initial conversation to the actual signing of the paperwork 2m52s.
• The first conversation about the acquisition took place at the end of March, and the deal was signed in mid-June, with the concept of the acquisition being discussed in early May 2m47s.
• Mosaic ML is an important component of Databricks, as it brings the ability to train AI models for corporations with data, which complements Databricks' existing capabilities built on the open-source Delta Lake Project 3m36s.
• The acquisition allows Databricks to further popularize the lakehouse format for BI across structured and unstructured data 3m23s.
• The decision to sell Mosaic ML to Databricks was based on the idea that the combined entity would be more valuable than the sum of its parts, rather than focusing solely on the valuation of Mosaic ML 3m58s.
• The founder of Mosaic ML considered the risks and potential outcomes of staying independent versus joining Databricks, ultimately deciding that the latter was a lower-risk option with a similar potential outcome in terms of valuation 5m22s.
• The founder had a conversation with Ali Ghodsi, the CEO of Databricks, about the valuation and stock price of Databricks, which was seen as less risky than Mosaic ML's high-risk, high-beta situation 4m11s.
• The founder walked through the logic of the decision, considering the potential revenue and valuation of Mosaic ML if it stayed independent, and comparing it to the potential outcome of joining Databricks 4m30s.
• The founder tweeted about "founder mode" and the idea that if you have to tell people you're in founder mode, you're probably not in founder mode 5m40s.
• Founding a company requires being in "founder mode" to get deals done quickly, such as the 62-day deal completion from start to finish 5m46s.
• Investors were initially hesitant about the deal, but ultimately supported the decision, with some even suggesting to continue growing the company instead of selling 6m2s.
• The decision-making framework used internally was to determine which path would allow the company to influence the world more 6m39s.
• This perspective is specific to Mosaic ML, as the company is driven by a mission to impact humanity through AI, and not all startups share this motivation 6m57s.
• Mosaic ML is composed of academics who care about the field of AI and its impact on humanity, which drives their decisions 7m2s.
• Not all startups are mission-driven, and some may be more focused on making money, which is a valid goal 7m17s.
• The comment about SAS companies not surviving in 2024 if they were not strong in 2021 is valid, and the importance of a strong feature or mission cannot be overstated 7m45s.
• Databricks has an open-source heritage, and Mosaic ML has contributed to popular open-source models, including MPT 7B and MPT 30b, which have been widely downloaded 8m2s.
• There is a shared mission agreement between Databricks and Mosaic ML regarding open-source development, which is reflected in their contributions to the field 8m27s.
• The recent veto of the AI regulatory bill in California, SP 1047, has implications for open-source AI development, as it may have made it more legally risky 8m36s.

Regulatory challenges and open-source AI 8m54s

• Regulatory challenges are a significant obstacle to Open Source AI development, with potential lawsuits over copyright information being a major concern 8m55s.
• An executive order issued at the end of last year imposes hard limits on compute, which may impact Open Source AI, although the exact consequences are unclear 9m11s.
• The decision to use open source or closed source is a business decision, but imposing restrictions can cause problems, as seen with the limited rollout of multi-L models in Europe due to regulations 9m23s.
• The restrictions on Open Source AI development ultimately harm consumers, who have fewer choices and limited ability to modify or customize AI models for their purposes 9m36s.
• It is too early to impose strict regulations on Open Source AI, and it would be better to wait until the impacts and liabilities are better understood, potentially in 5 years 9m49s.

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• The Meta Llama family of models, including the recent 3.2 update, is not being brought to Europe due to regulatory confusion, with the decision likely based on perceived risk rather than an attempt to influence AI regulation 11m31s.
• DataBricks, like Meta, conforms to regional regulations and may restrict or modify its product offerings and features in different geographic regions, including the availability of open-source models 12m49s.
• The company is cautious about providing workarounds to regional regulations and instead focuses on complying with existing rules 13m12s.
• The development of open-source AI models is being closely watched, with excitement about the potential for these models to close the performance gap with paid AI APIs 13m36s.

Meta's Llama models and reinforcement learning 13m57s

• Meta's Llama 3.1 models were better than expected, particularly in terms of the care taken on the reinforcement learning from Human Feedback (RLHF) side, which was a significant investment for the company 13m59s.
• RLHF is crucial for AI models as it helps provide bounds and guidance to ensure the models produce positive and safe responses, especially when dealing with sensitive topics 15m9s.
• The process of implementing RLHF is expensive and requires a lot of human effort, which is why Meta's investment in the Llama model matters 15m54s.
• Databricks' open-source model, dbrx, was trained at a cost of around $10 million, which may seem like a relatively small amount, but it highlights the need to make training models more efficient and cheaper 16m17s.
• Mosaic ML aims to make training models more affordable and efficient, and the company is working closely with Meta to achieve this goal 16m30s.
• There are currently no plans for a dbrx 2, as Databricks is focusing on partnering with Meta to drive innovation and make models more efficient 17m10s.
• Research is ongoing to make neural networks learn faster and cheaper, and the benefits of these advancements will be passed on to users in the form of faster response times, higher quality, and lower costs 17m22s.
• The concept of "Mosaic's law" suggests that the price of achieving similar model quality will decrease by a factor of 10 every year, which is being observed in the industry today 18m7s.

Depreciation of AI models and future plans for Databricks 18m19s

• The depreciation of AI models is a growing concern, with their value decreasing rapidly over time due to progression, as seen in Open AI's declining price points for models over time 18m25s.
• Databricks' partnership with Meta allows the company to have leading AI models within its platform without having to train them, which can be seen as beneficial for both parties 18m40s.
• The partnership between Databricks and Meta is non-competitive, as Meta is focused on enabling a developer community and using models internally, while Databricks is focused on enterprise solutions 19m2s.
• The complementary nature of the partnership allows Databricks to enable developers and Meta to build a developer community, making it a successful collaboration 19m18s.
• The strong match between Databricks and Meta has raised speculation about a potential acquisition, with the possibility of Meta buying Databricks being considered plausible, although not the strangest occurrence in the last 24 months 19m34s.

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Open vs. closed source AI models and advances 20m55s

• Open AI's model has been a significant development, but it feels like a high water mark, and it's unclear if open-source models can catch up to closed-source ones, which are currently ahead 21m6s.
• Closed-source models, such as those developed by Open AI, are still superior, but companies like Meta are investing in open-source models to close the gap 22m6s.
• The economics of the AI industry are challenging, with models costing billions of dollars to develop and only generating revenue for around six months, making it difficult for companies to recoup their investments 21m37s.
• The halflife of a model is quite short, which affects the industry's capital investments and makes it hard for companies to justify spending large amounts of money on model development 21m29s.
• The L1 model, for example, doesn't represent a huge step forward in terms of the model itself, but rather in the orchestration of the model, including how it self-corrects and comes up with better answers 22m15s.
• These models work by generating probability distributions of outputs, which means there's a probability that the output is incorrect, but the mean of that probability is correct, requiring multiple generations to get to the right answer 22m33s.
• Open AI's approach to this problem is to focus on retries and iteration, rather than trying to make the output probability correct, which provides insight into the direction of the industry 22m55s.
• The idea of orchestrating models to have retries and get to a more correct solution through iteration is a key concept in the development of AI models 23m9s.

Model orchestration and no code/low code AI 23m17s

• The concept of using existing technology to improve output through reinforcement learning is being explored, where a model produces multiple results and judges their quality to determine the best step forward 23m24s.
• This process is similar to how a human agent thinks through multiple steps to solve a problem, breaking it down and assessing each step to ensure correctness 23m53s.
• The idea of orchestration to improve model output quality is still in its early stages, and there is potential for significant gains by breaking down problems into smaller, modular components 24m14s.
• This approach is not new, but rather a natural progression of engineered systems, where complex problems are broken down into smaller, independently verifiable components 24m35s.
• The concept of compound AI systems involves breaking down large language models into smaller, modular components, such as language front ends, reasoning engines, and back ends, which can be independently verified and orchestrated to create a more reliable output 24m41s.
• This approach is similar to advancements in programming languages, where code is broken down into functions and objects that can be independently verified and strung together 25m13s.
• The goal is to move away from giant monolithic blobs of code and towards more modular, reliable, and maintainable systems 25m30s.
• By breaking down complex problems into smaller components, it becomes easier to find and debug bugs, and to create more reliable and efficient systems 26m20s.
• The idea of independently verifiable components is key to creating more reliable and efficient systems, and is a natural progression of engineered systems 26m23s.
• No-code and low-code programming abstracts one layer above typing code, allowing users to connect things and move boxes around, making it accessible to those who are not hardcore developers 26m32s.
• The use of no-code and low-code programming makes users feel like they have superpowers, especially for those who haven't written code in a long time 26m43s.
• In the future, it is unlikely that people who are not hardcore developers will be able to set up different models and functions without the help of the engineering department, even in three years 27m0s.
• Coding assistants have opened up the possibility of who can work with these models and functions, but there is still a certain level of complexity that can be expressed in this way 27m8s.
• Human language is naturally imprecise, and coding is a more precise way to describe a problem, making it a more effective way to program 27m21s.
• While it is possible to program in English, it is often more effective to program in a programming language because it is more precise and allows for exact effects to be known 27m32s.
• High levels of precision are needed to make models behave in a certain way under certain conditions, and this requires describing conditions very precisely 27m42s.
• It is unclear how long it will take for parameters, rules, and tests to be set up entirely in natural language versus code, but it is likely that this will not happen soon 28m6s.

Precision in AI and enterprise applications 28m9s

• Many use cases, approximately 80%, can be captured using patterns, allowing most of the work to be done using English, but the remaining cases require precise language, similar to a legal document, which demands exactness and clarity, even if written in English 28m13s.
• The concept of precision in language is comparable to programming languages, where strict syntactical requirements enforce correctness and verifiability, and there have been attempts to turn legal language into code with these characteristics 28m55s.
• The idea of automating the legal industry using AI could potentially lead to a shift in employment, with lawyers becoming developers due to their familiarity with precise language and communication styles 29m11s.
• The term "agentic AI" has become a catch-all phrase and is not well-defined, but it originated from the concept of building systems that can work through a set of steps, break down problems, and complete multiple tasks to achieve a goal 30m0s.
• The concept of agentic AI is related to workflow automation and is often seen in robotic process automation (RPA), which involves automating repetitive tasks through a set of predefined steps 30m11s.
• The Mosaic AI agent framework and agent evaluation services, as well as Sierra by Brett Taylor, are examples of initiatives working on agents inside the enterprise, which has led to curiosity about how Databricks defines agentic AI 29m32s.

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Enterprise automation of repetitive tasks 32m0s

• Enterprise automation of repetitive tasks is a common pattern, but it's challenging to automate, which is where Robotic Process Automation (RPA) and agents come in, with the goal of observing what a person does and learning from it to automate tasks within data platforms like Databricks 32m1s.
• The vision is to automate pieces of workloads that dominate inside data platforms, such as data ingestion, transformation, dashboarding, and decision-making, by breaking down problems into smaller pieces and taking steps to automate them 32m39s.
• The concept of agentic AI is similar to what models like 01 are already doing, but the key difference is the need for bespoke and built-from-scratch solutions that are specific to a company's process, data, and learnings 33m8s.
• Generalized models like 01 can be broken with simple questions, highlighting the need for specificity and working from a company's own data, which is the ground truth of what should happen 33m36s.
• The concept of data intelligence involves using a company's data to create intelligence that can automate tasks and make useful decisions for the business, which is different from general intelligence that can do cool things but may not be applicable to a specific company's data 34m18s.
• The difference between general intelligence and data intelligence is that the latter is a model applied to a company's existing corporate data set, which is why Databricks, Data Lakehouse, and Mosaic ML make sense together 34m34s.
• Building a proof of concept for a general application is relatively straightforward, but delivering a high-quality app is challenging for many customers, and the issue is not just about data quality and cleaning, but also about understanding causation and how to get to an end goal 34m54s.
• Data quality and cleaning are crucial for building a reliable model, as bad data leads to bad outcomes, and humans are still better at understanding causation and how to achieve a goal, which is an area that AI has not yet cracked 35m24s.
• The second half of 2023 and the first half of 2024 saw a surge in interest in AI strategy among Fortune 500 companies, with every company needing to have a strategy in place 36m11s.

AI business strategies and data governance 36m18s

• Many large companies are asking their management to develop a strategy for implementing AI, but simply getting an Open AI account is not enough, and a real strategy requires understanding what success means and how it will have a business impact 36m18s.
• To develop a successful AI strategy, companies need to think through what success criteria means and identify a problem to solve, then assemble components to create a working demo, and evaluate its performance 37m7s.
• The demo may have performance issues and break in certain places, so it's essential to characterize these issues, build an evaluation, and give it a score to determine its effectiveness 37m26s.
• Once the demo is scored, companies can start to improve it using various tools, but getting to this point is often a blocker for many applications due to plumbing issues like security and governance 37m59s.
• Data governance is a significant part of what Databricks does, and they have a framework called Unity Catalog, which provides a universal source of truth for permissions, lineage, and logs 38m57s.
• Unity Catalog helps regulated industries like banks and healthcare companies manage their data safely and securely, ensuring that they can't just throw data into a model without proper permissions and governance 39m10s.
• Databricks is working to break down the steps required for enterprises to implement AI safely and systematically, making it easier for them to bring their data into an AI context 38m15s.
• Regulated industries like banks work on trust, and their business is based on trust, which makes them conservative in adopting new technologies, and this has been a big blocker to getting these solutions out to their customers 39m39s.
• These industries are now thinking about a blast radius, where they can try building a solution, expose it to internal users, and then work their way up to something that goes external as they start to trust these solutions 40m0s.
• A huge blocker to adopting these solutions is the lack of trust in the engineering and security of these solutions, and this is what Databricks has addressed with the integration of Mosaic and the development of the Unity Catalog 40m20s.
• The Unity Catalog allows for the inheritance of access controls from data to models, and it also enables vector databases to be aware of user permissions when retrieving data 40m32s.
• The Unity Catalog framework has been open-sourced and is available on a GitHub repository, which provides a mature way of thinking about data governance and has been applied to different components like fine-tuning, embedding, and vector databases 41m12s.
• The next frontier is getting to correct and useful models, and the pathway to adopting these solutions has been smoothed by companies like Databricks, which has helped a lot of companies go from having a Genie strategy to using their own data in production for internal or external features 42m21s.
• The industry is moving quickly, with significant advancements in just two years, such as the development of chat GBT, which was not even available two years ago, specifically in November 2022 42m49s.
• Enterprise speed is relatively fast, with two years being a short time to get a proof of concept going, especially when compared to private equity-owned companies 43m10s.
• Conversations with enterprises are happening daily, discussing how to implement data intelligence and best practices, with governance tools being a key aspect 43m26s.
• Being a trusted partner is crucial for big companies, making it challenging for startups to be adopted by large enterprises like banks, such as Chase 43m48s.
• Databricks has built trust with its customers, making it easier to implement new features on top of its existing platform, leading to transformative results and fast business growth 44m19s.
• Databricks' growth has been significant, with a lot of uptake, although precise numbers cannot be disclosed 44m27s.
• Before the rise of generative AI, Databricks was already growing quickly in the machine learning space 44m37s.
• The company's branding now emphasizes its position as a data and AI company, reflecting the industry's shift 44m49s.
• New customers are coming to Databricks due to a combination of interest in its Lakehouse product, data storage, and BI, as well as its AI capabilities 45m10s.
• Many companies still struggle with data platform migrations and legacy systems, making Databricks' Lakehouse product an attractive solution 45m16s.
• Some companies are still using outdated systems, such as old IBM mainframes, and are looking for ways to modernize 45m31s.
• Data intelligence is a key area of focus, with companies looking to leverage their data for AI, and this is an area where Databricks is currently winning, combining data and AI capabilities 46m9s.
• Databricks started with the concept of combining data and AI, as seen in the Netflix challenge in 2011 or 2012, where Netflix published a dataset and asked developers to build a system that could match user preferences to movie recommendations 46m26s.
• Many people use Databricks for AI and then connect it to other data platforms, highlighting the need for more compute to avoid being GPU-poor 46m56s.
• Databricks runs on top of the cloud, as a software company, and does not build data centers, which means they do not have to worry about building a hyperscale network of data centers around the world 47m39s.
• Despite not building data centers, Databricks still conducts fundamental research and has a sizable footprint of GPUs, but they are not building open-source models 47m47s.
• The company is seeing an increase in inference workloads, particularly with the rise of richer use cases, and is focused on making inference faster, cheaper, and of high quality, which requires significant GPU compute 48m0s.
• Databricks is expanding into multiple geographies and breaking apart its deployment patterns, rather than having one giant monolithic cluster, but the demand for GPUs is expected to continue growing 48m27s.
• There is a risk of platform risk due to dependence on major scaled cloud platforms, and the potential for these platforms to develop their own AI models and BI tools, which could impact Databricks' business 48m42s.
• The idea of offering a Databricks Cloud alternative to the big tech companies is a possibility, but it would require significant investment and resources 49m4s.
• The current focus is on establishing a particular point in the stack of trust, owning abstractions for data, and creating models that are really great, with the ability to deploy them, which can be a challenging task when moving up the stack 49m30s.
• Typically, moving up the stack becomes very hard, as seen in hardware companies moving to the cloud, or cloud companies trying to build their own hardware, with every cloud company currently building their own inference chips 49m50s.
• Databricks is not doing hardware, nor are they doing cloud, but they are working with cloud companies, driving a lot of initial revenue to them, and storing a ton of data, with a good control point due to the tabular acquisition 50m29s.
• The clouds are incentivized to work with Databricks, but this may not always be true, and there is a concern that the largest tech companies owning the major cloud platforms could lead to less innovation over time 51m9s.
• Building a hyperscale cloud requires a lot of money, and only a few companies can do it, making it difficult for new companies to enter the market, which is a concern for the future of innovation 51m32s.
• The hope is that in the future, Databricks will consider building their own cloud, but currently, it's not in their plans, and they are focused on working with existing cloud companies 51m44s.
• Historically, incumbency events in tech have led to the rise of new companies, such as Apple and Microsoft, which both started in the late 1970s with the PC, and have since become two of the largest companies in the world 52m2s.

Tech incumbents' evolution and AI competition 52m8s

• Tech incumbents have maintained their advantage over time through multiple tech transitions, and despite being behind in innovation, they often buy into or acquire startups that are doing something innovative, allowing them to stay ahead 52m8s.
• The evolution of ecosystems requires certain capital to achieve incumbency, and tech giants have this capital, which keeps their momentum going 52m37s.
• Working with multiple tech giants allows companies to be present wherever their customers are, making it beneficial for businesses to collaborate with these incumbents 52m46s.
• The debate between small and large models is ongoing, but the key is to build the right model for a specific application, considering factors like latency and quality 53m8s.
• Trends show that no one has built a model bigger than GP4, which happened 16 months ago, and even Open AI is building smaller models, such as GPT 40 and 40 Mini 53m20s.
• Smaller models can achieve higher levels of quality, and chaining these models together in compound AI systems is the way forward for economic and modular development 53m58s.
• Large models can exist as a way to help create smaller derivative models by taking the outputs of the larger model and modifying smaller models for better performance 54m14s.
• This approach is seen in Microsoft's work with the FI model, which used 1.3 billion parameters to achieve high-quality results, and in the use of synthetic data generation from bigger models to train smaller models 54m30s.
• Building a large model can be a necessary step to create a high-quality smaller model, and this approach may be the way forward for AI development 54m51s.

AI model sizes and synthetic data quality 54m55s

• Synthetic data is considered to be of slightly lesser quality than non-synthetic data, and this perception may be a bias that needs to be reassessed 55m6s.
• The quality of synthetic data can degrade with each subsequent generation, similar to the concept of making copies of copies, leading to a loss of accuracy and a phenomenon known as model collapse 55m44s.
• Synthetic data can still be a useful technique for augmenting models and allowing them to explore real-world distributions, but it is essential to maintain quality and keep the data grounded 56m5s.
• The field of AI has major issues, including the use of brute force methods, such as training models on massive amounts of data, which is not how humans or animals learn 56m22s.
• Humans and animals use significantly less data to develop high-quality causal models, and there is still much scientific research to be done to build efficient and low-power AI models 56m34s.
• The brain's efficiency, using only 20 watts of energy, is an encouraging example of what can be achieved with low power consumption, and researchers have been exploring the comparison between brain watts and data center watts 56m47s.
• There was an effort to replicate the human brain's neural network, which is highly efficient, but this approach seems to have been set aside during the Transformer Revolution, and it is unclear if research will shift back towards biology in the future 57m11s.

AI inspiration from biology and future innovation 57m29s

• The human brain project and similar initiatives were considered poor facsimiles of what would lead to intelligence, as they tried to replicate something without understanding why it exists, making the systems delicate and prone to falling apart with small parameter errors 57m29s.
• Extracting principles from biology might be a more sensible approach, and looking at biology could still be a useful path forward, as there's a different dimension to more data that's currently missing in these systems 57m51s.
• Current AI systems lack the ability to understand how to make better decisions and self-critique, unlike the human brain, which has multiple systems that interact in interesting and precise ways 58m8s.
• The book "Thinking Fast and Slow" by Daniel Kahneman has roots in neurobiology and explores the different systems in the brain, including those that make learning without parameters possible and those that simulate different realities to make good decisions 58m21s.
• AI systems today don't have a good grounding in reality and are mostly pattern-matching against training data, but taking inspiration from biology without direct mimicry could lead to improvements 58m48s.
• There's still much work to be done, but the idea that there's still so much improvement coming in the ability to build intelligent software systems is a significant takeaway 59m10s.
• The next 10 years are expected to bring incredibly quick development in AI, leading to something truly marvelous, similar to the rapid progress made in the development of the internet 59m24s.
• The progress made in cloud computing, live updates, and applications running within a browser was not contemplated when the first web browser came out, and similar innovations can be expected in AI 59m40s.
• Companies like Google have figured out how to build resilient infrastructure, and this engineering has to happen to achieve high reliability and uptime 1h0m9s.
• ChatGPT was released just two years ago, and there's still 10 years of innovation left to make these AI systems really amazing 1h0m21s.

AI startup landscape and healthcare applications 1h1m1s

• As an angel investor, investments are typically made in companies or founders that can be useful to, and often focus on new technologies or solving vertical problems, with a particular interest in applying AI to healthcare due to its fundamental importance to human life 1h1m38s.
• Healthcare is a key area of interest because despite having the necessary pieces to make it accessible to many people, structural reasons have prevented this from happening, and there is a desire to invest in companies that can help address this issue 1h1m51s.
• Many AI founders reach out to discuss their companies, and there is a willingness to talk to any founders who are building something cool 1h2m1s.
• Databricks is open to making acquisitions, having a corporate development team that scans for companies that fill in holes or solve problems that Databricks does not have a good solution for, but is thoughtful about its approach and only acquires companies that strategically align with its goals 1h2m22s.
• Acquisitions are assessed based on factors such as whether they solve a problem that Databricks does not have a good solution for, whether they have a customer base that Databricks wants access to, and whether they can be integrated into Databricks' existing products 1h2m40s.
• An example of a successful acquisition is Wac AI, which created a user interface for embedding data and visualizing it, and has since been morphed into Databricks' products 1h2m55s.

Databricks' growth trajectory and potential IPO 1h3m6s

• Databricks is interested in making more acquisitions to grow and have a bigger impact on its customers, with the company currently growing fast and expanding its revenue by over 60% year on year 1h3m7s.
• The company's rapid growth makes it a potential major player for other companies looking for an exit, and it is likely to be a key player in the industry in the future 1h3m20s.
• Databricks' growth trajectory has led to speculation about a potential initial public offering (IPO), with the company's response being that it will happen eventually, but not yet 1h3m34s.
• There is speculation that Databricks may go public as early as H1 2025, although this has not been officially confirmed by the company 1h3m39s.
• Naveen Rao, a representative of Databricks, can be found online on Twitter, where he is active and engages in discussions about AI, using the handle @navengrao 1h4m1s.
• Rao is also on LinkedIn, where people can find him by searching for his name 1h4m8s.

Elliot Easterling of bonbon joins TWIST for a Jam with JCal. 1h4m25s

• A "Jam with JCal" session is a discussion where a startup founder shares their work, customers, product, goals, and vision for changing the world 1h4m26s.
• The founder is then asked about their biggest challenges and struggles, and a dialogue ensues to solve problems, which is a key aspect of startups 1h4m41s.
• The host has experience investing in 400 companies, taking over 10,000 pitches from founders, and conducting 2,000 episodes of "This Week in Startups" 1h4m48s.
• The guest for the session is Elliot Easterling 1h5m13s.

Bonbon.tech and the rewards platform overview 1h5m16s

• Bonbon.tech is a company that offers a rewards platform for publishers, allowing them to reward anything and drive more engagement and higher registration rates 1h5m58s.
• The company aims to solve the pain points of ad-focused publishers, who have been suffering from big tech changes such as cookie deprecation, reduced search results, and social media algorithms referring less traffic 1h6m12s.
• Bonbon.tech's platform provides consumers with relevant rewards, access to unique content, simple and transparent data and privacy controls, and a better user experience 1h7m0s.
• Publishers benefit from the platform by getting logins, which re-enable cookies and lost IDs, and allow them to build direct relationships with their users 1h7m13s.
• The platform also offers a gamified engagement points program that drives repeat visitors, page views, and video watches, resulting in five times more monetization per user 1h7m26s.
• Bonbon.tech's optimization engine drives outcomes such as 300% higher registration rates, 100% more engagement, and 250% higher ad rates 1h8m6s.
• The company has found that 54% of people who log in will complete their data profiles, providing a richer understanding of the publisher's site users 1h8m15s.

Demonstrating Bonbon's technology and user stats 1h8m29s

• Bonbon's technology allows publishers to trigger a rewards window, either inline or as a pop-up, which runs multiple offers simultaneously to determine what users care about most through machine learning, resulting in a 3X higher registration rate 1h8m46s.
• The rewards window offers users the chance to win prizes, such as a television set, in exchange for logging in or registering, and users are automatically entered into the contest upon registration 1h9m13s.
• After registration, the process is gamified, encouraging users to provide more information, such as their name, zip code, and gender, with high response rates: 94% for name, 91% for zip code, 89% for gender, and 54% for phone number verification 1h9m30s.
• Users also earn points for reading articles, which drives 100% more engagement 1h9m48s.
• Bonbon's platform consists of three parts: the Open Identity Manager, which collects and manages first-party data, the Rewards Engine, which runs hundreds of offers, and frontend tools that deliver the product to consumers 1h9m57s.
• The platform also includes an API that allows publishers to issue rewards on their own 1h10m13s.
• Bonbon has deployed its technology on 27 websites, with 60,000 registered Bonbon members, and has built a first-party data file of 60,000 users as of last week 1h10m27s.
• The company's publisher network generates 60 million monthly page views 1h10m45s.

Publisher financials and Bonbon's business models 1h11m15s

• Many publications, such as Gadget, Auto blog, The Verge, and others created by Vox, are currently facing challenges, including constrained budgets, flat growth, or contraction, making it essential to carefully select ideal customer profiles 1h11m15s.
• To address the budget constraints of publishers, two solutions are offered: a SaaS platform for Enterprise publishers, where they can pay a SaaS fee, and a free version with ads, which requires publishers to meet a minimum size requirement to qualify 1h11m51s.
• The free version with ads injects ads into all modules, essentially paying for the full program, including rewards, making it a risk-free option for publishers 1h12m2s.
• The Enterprise version allows publishers to opt-out of owning user profiles if they pay enough money, enabling them to keep their user data exclusive 1h12m16s.

Privacy and user engagement in Bonbon's platform 1h12m27s

• Bonbon's platform is a cross-publisher rewards program that issues rewards across multiple publishers, allowing the cost of rewards to be abstracted and split among them, making it more manageable for individual publishers 1h12m27s.
• The platform provides a privacy guarantee, allowing users to opt out of any publisher they want, which is a key part of Bonbon's value proposition 1h13m7s.
• Users have the option to participate or not participate in the rewards program, and they can choose to share their data with publishers in exchange for personalized content and potential rewards 1h13m23s.
• The platform allows for personalization, enabling users to receive content and offers that are relevant to their interests, such as sales for men's products or tickets to specific sports games 1h13m56s.
• The data collected through the platform can be used to personalize content for users and provide them with opportunities to participate in sweepstakes and other gamification elements 1h14m25s.
• Gamification is a planned feature for the platform, although no specific examples have been implemented yet 1h14m39s.

Gamification strategies and user engagement 1h14m44s

• After people register, a weekly email campaign is sent to them with personalized articles that earn extra points, enabling gamification through a newsletter program 1h14m45s.
• Point bonuses are offered for completing specific tasks, such as visiting a publisher's website and playing one of their games, which can earn users 100 points 1h15m3s.
• The goal is to create hyper-engaged users through communication, directing traffic back to the publisher's site, and rewarding them for their behavior and activity 1h15m23s.
• A tried and true gamification strategy is inviting a friend or referring a member, where users can earn points by entering a friend's email and having them register 1h15m34s.
• This referral strategy is similar to those used by companies like Robin Hood, Uber, and Dropbox, where users are rewarded for gifting or sharing services with others 1h16m3s.
• The business model has two ways to win: through tools and networks, making it an interesting and potentially successful approach 1h16m14s.

Fundraising and network effect business challenges 1h16m22s

• In today's fundraising environment, venture capitalists (VCs) prioritize revenue growth, which can be challenging for network effect businesses that focus on building distribution and user acquisition 1h16m22s.
• Bonbon's business model involves building users at zero cost, unlike most rewards businesses that pay for users, and its go-to-market strategy should focus on building distribution and getting more users 1h16m40s.
• However, VCs and the market want to see revenue growth, which can put pressure on publishers to pay for users, potentially slowing down the business 1h16m55s.
• Network-based businesses have a unique monetization approach, and proving the value of a large user base through experiments and data can help demonstrate growth potential to investors 1h17m11s.
• Running small experiments, such as sweepstakes or contests, can help drive user engagement and page views for publishers, and demonstrate the potential for growth and revenue 1h17m43s.
• By proving the effectiveness of these experiments and demonstrating user growth and engagement, businesses can correlate their efforts to revenue growth and demonstrate their potential to investors 1h19m30s.
• Ultimately, it is up to the business to run these experiments, prove their value, and demonstrate growth potential to investors, rather than relying on publishers to pay for users 1h19m2s.

Proving user engagement and strategies for growth 1h19m42s

• To achieve viral growth in a business, especially for sales and SaaS products, a growth rate of 10% a month is not sufficient, and instead, a 5 to 10% week-over-week growth rate is needed, which can be achieved through numerous tests and experiments with low dollar amounts 1h19m42s.
• A key factor in achieving growth is to tell a unit economic story to investors, showcasing that users are acquired for free and experiments demonstrate revenue traction and engagement on a per-user basis 1h20m7s.
• To prove user engagement and strategies for growth, it's essential to show that the business can scale by investing small dollar amounts to demonstrate the potential for growth and then adding zeros to the velocity 1h21m15s.
• A business can achieve this growth by running experiments with a small number of users and then scaling up, with the goal of getting publishers addicted to the product and eventually charging them 1h20m55s.
• To get started, a business may need to invest small amounts of money to show the potential for growth on a micro basis and then imagine adding zeros to the velocity 1h21m17s.
• The team working on this project consists of a product person, a couple of engineers, and offshore part-time engineers, with a total team size that is relatively small 1h21m35s.
• The business is in its early stages, having raised $1.4 million in funding, and is looking to achieve a network effect by investing in giving away its product and leveraging social media and platforms like TikTok 1h22m0s.

Exploring growth opportunities and market expansion 1h22m18s

• The publishing industry is experiencing a decline in attention, and businesses in this sector are contracting, making it challenging to sell tools to them, much like trying to sell deck chairs on the Titanic 1h22m21s.
• In contrast, growing areas include TikTok, shorts, video podcasts, and other emerging trends, which could be potential opportunities for expansion 1h22m35s.
• It's essential to consider the potential impact of engaging with a large number of users, such as 60,000 TikTokers, and how this could shape the future of the business 1h22m45s.
• The success of a business can be likened to surfing, where the size and quality of the waves (or market trends) can greatly impact performance, and it's crucial to identify and ride the big waves 1h22m57s.
• The current market or "beach" being surfed may have a tide that's going out faster than the business can grow, making it essential to reassess and adapt to changing trends 1h23m26s.
• The importance of catching emerging trends is highlighted by personal experiences of catching the blog, magazine, podcasting, and angel/seed investing waves, which have led to successful outcomes 1h23m35s.

Content formats and e-commerce strategies 1h23m46s

• Investing in an incubator can be beneficial, but sometimes it's necessary to catch bigger waves outside of it to achieve success 1h23m48s.
• Alternative content formats such as live streaming and shorts on platforms like TikTok and YouTube can be effective for businesses 1h23m57s.
• The speaker encourages businesses to think about live streaming as a potential format for their content 1h24m8s.
• Having an interesting business idea is not enough; it's essential to have product-market fit or market pull to succeed 1h24m12s.
• Raising a lot of money without having product-market fit or market pull can put a business in a challenging position 1h24m19s.
• Finding another "beach" or market with bigger waves can be necessary for success, and e-commerce is another area to consider 1h24m31s.
• Direct-to-consumer businesses have struggled with Facebook advertising, but some have found success on platforms like TikTok, social media, and podcasting 1h24m36s.
• The environment and platform used can significantly impact a business's success, and it's essential to experiment and find the right fit 1h24m50s.
• Bonbon's Reward Innovation is testing new areas and experimenting with different approaches, but the details are not disclosed 1h25m2s.
• The domain name "bonbon.b.te" is mentioned, and listeners are encouraged to visit the website to learn more 1h25m19s.
• The episode ends with a promotion for the domain name registrar "get.g.te" and an invitation to tune in to the next episode of "Jam with Jake" 1h25m29s.