A Guide to Finding Certainty in Tech's Unknown Future

In the tech industry, nobody truly knows what the future holds—we are all speculating. However, some things are certain.

I have been part of the tech industry since the late nineties, having completed my BS and MS at Stanford during the first internet boom. Back then, few could have predicted how our lives would change over the next decades—shopping online, connecting with strangers, using our phones to pay for goods or to communicate through video, and carrying one of the most powerful computers in our pockets.

We often forget how enormous and uncertain the initial leap to the internet was. It lowered the barriers to coding and application development, leading to a surge in software developers and data generation which compounded innovation over decades. While innovation moved at an exponential rate for those creating it, driven by venture capital, developer talent, and user demand, it took over 20 years to impact our lives globally and at scale.

From 1994 to 1999, the first websites mirrored real-world businesses: online classifieds, storefronts, and media. Few original web businesses emerged then, with some notable exceptions like Netscape, Yahoo!, Amazon, eBay, Netflix, Google, and Salesforce. But, their true value was realized in the following decade, thanks to the development of robust web infrastructure, ubiquitous compute power with AWS's launch in 2006, and accessible data and enterprise tools. The internet became a commodity but, in the process, enabled huge value creation through product and customer-led companies. The period between 2000 and 2010 saw the rise of LinkedIn, Palantir, ServiceNow, Skype, Facebook, YouTube, UiPath, Reddit, Workday, Adyen, Twitter, Spotify, Shopify, Dropbox, Airbnb, Twilio, and Okta, transforming how we live and work.

The iPhone's introduction in 2007 further reduced development barriers, increasing data creation and accessibility, anytime, anywhere. A few years later, mobile applications evolved from mimicking existing websites, just like the first websites had mimicked brick and mortar businesses, to creating new paradigms. This shift from 2010 to 2020 enabled the exponential growth for companies like Twitter, Spotify, Twilio, Uber, Slack, WhatsApp, Square, Stripe, Pinterest, Instagram, Snap, Zoom, TransferWise, Plaid, and Robinhood, many of them mobile first.

The beginning of a new tech cycle

Recent developments in AI represent a technology leap akin to the internet, possibly even more powerful. Over the next decades, AI will drastically change our lives and work, much of which is still unimaginable, just as it was in 1994. While the timeframe for adoption may shorten from 20, to 15 or 10 years, AI adoption and value creation will take time as people and organizations, especially large ones, resist change and need tools, processes, and time to adapt. Big tech companies are aware of this and as Kindred's Mark Evans recently told me, “we have never seen this level of incumbent TOMO (Terrified Of Missing Out).” This has resulted in billions of investment from incumbents, making them somewhat less vulnerable to disruption at least in the short term, thus making historical pattern recognition and outcome prediction more challenging. So, what can we count on today with certainty?

1. Commoditization Across Layers: Charles Gorintin, Alan 's cofounder and CTO, once told me, “every layer tries to commoditize the one above or below:”

• Cloud service providers (GCP, Azure, AWS) are trying to commoditise LLMs by partnering with all of them
• They are also building their own chips to commoditise the infrastructure layer currently dominated by Nvidia
• Facebook open-sources LLMs to commoditise them and use the cheapest solutions in their products
• LLMs are trying to commoditise CSPs by partnering with many
• They are also attempting to commoditise the application layer by verticalizing into the application layer

2. Product is King: Research alone doesn't make a company—product and distribution do. The combination of product vision, execution capability, technology innovation, business acumen, and go-to-market leadership lead to sustainable businesses Companies must build products that meet a clear need for a significant number of customers, show critical usability improvements to existing solutions, and elegantly slot into existing workflows to minimize adoption pain.

3. “Constrained Creativity, a Driver of Innovation in Science and AI in particular”: I am quoting Arthur Mensch, CEO of Mistral, who told me they observed first-hand the amount of waste that large corporations have with their compute. Throwing money at the problem is not a winning solution as a business model predicated on raising billions is not a sustainable strategy. Mistral has been able to develop one of the most powerful LLMs in market at a mere fraction of the price. ?Efficient, cost-effective models will drive innovation, leading to another technological leap as AI becomes more efficient and smaller, revolutionizing our devices.

4. The Data Moat: Every company must ensure as a first step that their data is clean, reliable, accessible, can easily be manipulated and analyzed. Data is the fuel of AI and a moat against competitors. Without it they will lose against the competition in the long term. It’s a guarantee.

5. Technology-User Experience Gap: We are trying to harness powerful yet immature technology, making usability complex. In the mid-term, we will talk about AI systems and solutions rather than LLMs or agents. Ultimately, mature AI building blocks will form modern software solutions focused on solving problems, not just applying new technologies. Those who grasp this quickly will be the likely winners.

6. Changing User Interfaces: The user interface of our devices and products will likely change. We will figure out AI at the edge by making models more efficient, cheaper, and smaller. The computers in our pockets will behave very differently from the way they do today. How? Your guess is as good as mine here. Perhaps we will invent a completely new device that can understand you, see for you, listen for you, talk for you, and earn your trust. What is certain, however, is that those who get it right will follow in the footsteps of Facebook, Instagram, and Twitter, by reinventing interaction and information access.

7. Focus on Capabilities not Use Cases: Companies focusing on outcomes, identifying critical problems to solve, and building capabilities which include AI as one building block of many will dominate. Those finding one off use cases to apply AI will fall behind. An outcomes focus will lead to building capabilities which can be repurposed across functions in the organisation from marketing & communications to finance and legal, leading to faster innovation.

8. Rapidly Changing AI Technology but Slow Enterprise Adoption: AI is still immature but evolving quickly. The pace of change is not about to slow down any time soon because it is driven by an ongoing LLM war fuelled by hungry investors and formidable talent which have captivated consumers and enterprises with both excitement and fear. Conversely, these rapid changes will cause slower enterprise adoption. Enterprises need robust tools that solve a problem for their organisation, not immature technologies which lead to long proofs of concept?and delay scaled deployments.

9. Co-creation Between Human and Machine: It is the first time that the enterprise is faced with a piece of software that doesn’t reliably provide the correct answer. It is probabilistic not deterministic. Therefore, initially, until we solve this challenge or change our processes to adapt to it, AI solutions will see the greatest adoption in co-creation models where humans are in the loop, augmenting creativity, efficiency, and productivity.

10. The Incumbent Winning Strategy: Incumbents who combine leveraging existing distribution channels to incrementally improve the user experience, while at the same time taking risks to create new destinations for brand new concepts and products, will be ahead of others. This is a concept that Spotify’s CTO & CPO Gustav S?derstr?m illustrated beautifully at the Sana AI conference while describing their approach in delighting users by enhancing existing products with AI and taking risks betting on new ones with the AI DJ.

11. Nimble Teams and Adaptive Cultures: Because of the constant change, companies thriving during this period need flexible teams and cultures who embrace change, are prepared to test, learn and iterate quickly. This applies to both solution creators and adopters and is a crucial investment criterion for those looking to deploy capital into the sector.

12. Collapsing the Talent Stack: This is another concept which Charles Gorentin elegantly put forth in one of our discussions. At Alan, he is seeing the role of the developer changing, and a rise in the importance of the technical product manager who understands product/customer needs and can build using LLMs. For other knowledge workers the shift will be felt first in the most junior roles. Ultimately, all of us will need to adjust to a new technology, a new way of thinking, a new set of tools. Investing in our education establishments from the early years through to university is a necessity. Investing in talent with the right skillsets and helping existing teams adapt to change is critical.

The Path Forward

As the technology matures and new tools emerge, we'll move from adapting AI to our current workflows to adapting our work around this powerful software because of its promise to boost productivity and effectiveness, increase profits and enhance our lives. This behavioural change will drive new business models, and in a decade, our work and lives will be profoundly different. This change presents a massive long-term opportunity for people, businesses and investors. To leverage and influence it we need to find allies to enrich our perspectives, work with adaptable talent, iterate and learn quickly in order to rapidly refine our vision of the future. Otherwise, we will miss the opportunity of a lifetime.

A huge thank you to Seshu Ajjarapu, Nima Badiey, Arjun Bansal , Karim Beguir , Jim Gao, Mehdi Ghissassi, Jonathan Godwin, Charles Gorintin, Joel Hellermark, Christian Karam, Amir Khosrowshahi, Jaroslaw (Jarek) Kutylowski, George Liang, Alfred Lin, Arthur Mensch, Nasrin Mostafazadeh, Curtis Northcutt, Ali Partovi, Hadi Partovi, Naveen Rao, Aaron Rosenberg, Milos Rusic, Chris Re, Peter Sarlin , Thomas Scialom, PhD , Yoav Shoham , William Stasior, Tris Warkentin, and Timur Cuceloglu Yarnall for letting me pick their brains and indulging me in debate in order to keep up with the pace of change on the frontlines.

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