Breaking the Tech Policy “Doom Loop”
For those of us working in product and entrepreneurship, there are many books, podcasts, and newsletters to help optimize our output, but very few challenge us to reflect on how new technologies and the products built on top of them are received outside the industry. This is especially true of politics, policy, and legal circles where a lack of trust and understanding with tech contributes to a negative feedback cycle (or “doom loop”) that slows progress. This article will detail a few key steps we can take to reduce the tension between the energy and optimism of technology and the skepticism of society at large to emerging technologies.
Let’s delve into the recent explosion of discourse around GPT3 playing out in realtime. On a recent episode of the Ezra Klein Show, I pitched a question about the growing engagement of experimentation of large language models. I was excited to have this topic discussed at length on a non-tech oriented show.
One key insight was to look to our recent history with AI-driven social media feeds. We are living through an era where AI has learned what engages us and incentivizes users to cater to various social algorithms in order to drive engagement and reach. Now, large language models absorb our writing, knowledge, and preferences directly and feed us back answers to all kinds of prompts. Can we take time to reflect on a decade of feedback loops between people and their algorithmic feeds? With this context, product people can better prepare for a world we seek to fill with all kinds of generative AI tools. Since product managers are well-versed in considering tradeoffs and balancing needs of different stakeholders, I propose thinking more deeply and specifically about framing AI product releases for a policy-minded audience.
For several years, I have been working to understand and bridge the divide between the tech world and the U.S. policy ecosystem. While I was steeped in political culture as a leader at the University of Chicago Institute of Politics, I was also encouraged to expand my conception of politics far beyond the traditional route, engaging with policymakers and journalists on issues like the rise of automation, as well as the privatization of R&D and innovation spending.
I was also one of the first students in the public policy department to specialize in technology policy and conducted research into the emerging practices around algorithmic accountability and opportunity to experiment with “policy sandboxing.” I also prioritized coursework on the history, philosophy, and social sciences of science and technology, focusing research on the U.S. federal government involvement in advancing next generation computing and the new attempts to incubate a commercial space industry.
While this felt like a niche interest at the time, it is probably the experience I am most excited to draw on throughout my career — there are so many opportunities to bring technology policy and history into perspective, especially working in product, where predicting how people will react and work with new technologies, and how government will respond, can be critical for success.
Before we can break down the silos between policy and tech, we first have to explore the underlying dynamics driving a wedge. Through my experience in both realms, here are some general observations broken down along 5 key dimensions and ideas to forge a better path forward through a convergence in thinking.
Where do these opportunities lead us? First, we need to break free from the limiting mindset pitting tech progress against new rules and regulations. That’s far from the only interesting question in the tech policy ecosystem. Here are a few more interesting questions that I encourage product people to think about to break out of the tech policy doom loop:
- What can we learn from the U.S. history of investing in innovation and research and the critical hand-off and implementation by the tech sector?
- What proactive forms of regulation can be passed to establish safe and effective regulatory sandboxes for new technologies to thrive?
- What forces of culture drive outcomes in tech? How do we define and classify different and distinct sub-cultures within the industry? How do we overcome the role of “culture” and confirmation bias in how people interpret new technologies or products?
In 2023, I am excited to continue pursuing my curiosity at the intersection of tech, policy, psychology, and society. If you have ideas for more books or media on these topics or ever want to discuss how you think about these dynamics in your own world, please feel free to reach out!
