Quick news hit from this week before we get to the main post:
Open AI announced a relatively big update this week, enabling web search directly, in what could be a move to compete directly with Google and Perplexity. The feature includes partnerships with news and data providers for up-to-date information and new visual designs for weather, stocks, sports, news, and maps. It’s a great upgrade, and one that has me wondering if ChatGPT eventually becomes a super app:
Now, on to this week’s programming: Some major lessons, predictions, and takes I got from Kai-Fu Lee’s NY Times bestselling book, AI Super-Powers: China, Silicon Valley, and the New Work Order.
This is the age of data, and if you don’t have your own data, you’re losing
Kai-Fu Lee speaks of the major transitions we’re experience in AI, and it struck me that we’re already experiencing this transition:
This brings us to the second major transition, from the age of expertise to the age of data. Today, successful AI algorithms need three things: big data, computing power, and the work of strong - but not necessarily elite - AI algorithm engineers. Bringing the power of deep learning to bear on new problems requires all three, but in this age of implementation, data is the core. That’s because once computing power and engineering talent reach a certain threshold, the quantity of data becomes decisive in determining the overall power and accuracy of an algorithm.
Why it matters:
Whether you work in house, at an association, or at a public/corporate affairs agency, having access to the same large language models as everyone else won’t be a differentiator.
We’re at an interesting moment where most of us are in the same 100 meter dash, and we’re all neck-and-neck trying to figure out how to make the best use of off-the-shelve tools, but we’re about to get completely overlapped by those who have built their own models, or have learned how to leverage the data they sit on.
Organized labour will have another big moment
Based on the current trends in technology advancement and adoption, I predict that within fifteen years, artificial intelligence will technically be able to replace around 40 to 50 percent of jobs in the United States. Actual job losses may end up lagging those technical capabilities by an additional decade, but I forecast that the disruption to job markets will be very real, very large, and coming soon.
Why it matters:
This should be a big opportunity for organized labour to grow in relevance, adapt to today’s realities and position itself for tomorrow. I’ve already shared my own views and analysis on where I think we’re most susceptible to job replacement in public affairs. We tend to operate in strategic and creative fields, which protects us for a bit, but only a bit. Most jobs are exposed to automation and replacement, but the speed with which that occurs will depend on how effective organized labour is in staying ahead of technology.
If Big Labour organizes well, and if political actors and regulators feel the heat, new regulations may push out Kai-Fu Lee’s 15-year horizon to 20 or 25.
We’re in an energy and technology arms race, and the fallout will be ugly
AI-driven automation in factories will undercut the one economic advantage developing countries historically possessed: cheap labour. Robot-operated factories will likely relocate to be closer to their customers in large markets, pulling away the ladder that developing countries like China and the “Asian Tigers” of South Korea and Singapore climbed up on their way to becoming high-income, technology-driven economies. The gap between global haves and have-nots will widen, with no known path toward closing it.
The AI world order will combine winner-take-all economics with an unprecedented concentration of wealth in the hands of a few companies in China and the United States. This, I believe, is the real underlying threat posed by artificial intelligence: tremendous social disorder and political collapse stemming from widespread unemployment and gaping inequality. (emphasis added)
Why it matters
We’re already seeing Microsoft invest in nuclear power because the computing demands brought on by AI are having a major impact on our energy needs. Access to energy, combined with access to engineering talent, in a safe geography, will shape who gets ahead and who falls behind. I’m betting on the US in this race. Demographics are still in the US’ favour, and US-based firms are better capitalized to do things like, buy the entire generation capacity of a once-dead nuclear power plant.
CSR initiatives will be shaped by the social disorder outlined by Kai-Fu Lee. Activists will call on multinationals to adopt CSR practices that consider the socioeconomic impacts of relocating manufacturing facilities with demands to mitigate negative effects on developing economies.
Anti-trust laws battles will focus on these AI-driven changes, as will debates around the ethics of AI.
There’s data. And then there’s offline data: the new oil
Silicon Valley juggernauts are amassing data from your activity on their platforms, but that data concentrates heavily in your online behaviour, such as searches made, photos uploaded, YouTube videos watches, and posts “liked.” Chinese companies are instead gathering data from the real world: the what, when, and where of physical purchases, meals makeovers, and transportation. Deep learning can only optimize what it can “see” by way of data, and China’s physically grounded technology ecosystem gives these algorithms many more eyes into the content of our daily lives. As AI begins to “electrify” new industries, China’s embrace of the messy details of the real world will give it an edge on Silicon Valley.
Why it matters:
Data is the new oil and right now Chinese companies have a massive edge: collecting the type of data that is more difficult to collect in jurisdictions where privacy laws mean something. By contrast, the Chinese government’s support for data accumulation enhances the capabilities of domestic companies.
I wonder if this will be the next frontier on data privacy. If China’s non-existent privacy laws give its AI firms a data advantage, how do US-based firms respond? At what point does the desire to maintain an edge prompt policymakers to prioritize initiatives that enhance domestic AI capabilities in ways that allow for the use of offline data, even if anonymized and for learning purposes.
To be clear, offline data could be helpful on so many fronts. There might be increased advocacy for government funding in AI research and development, focusing on areas where real-world data integration is crucial and of net benefit to society. Perhaps this is how we rationalize the integration of offline data.
Either way, advocates will push for stronger data security protocols to protect sensitive information from being accessed by big firms, let alone foreign adversaries.
New nation states will blur the lines
Of the hundreds of companies pouring resources into AI research, lets return to the seven that have emerged as the new giants of corporate AI research - Google, Facebook, Amazon, Microsoft, Baidu, Alibaba, and Tencent. These Seven Giants have, in effect, morphed into what nations were fifty years ago — that is, large and relatively closed-off systems that concentrate talent, and resources on breakthroughs that will mostly remain “in house.”
Why it matters
More regulations will come, especially if governments consider these tech giants as strategic assets. At what point does this all become a matter of national security interest?
Advocates will push for stricter data protection laws to safeguard citizens’ information from potential misuse, especially when companies operate across international borders.
Governments will use the cover of public interest to encourage mechanisms for public input and oversight in AI development processes. “Consumer protection” will be the driving theme of these efforts.
Academics and non-profits will ask for open-source initiatives to foster a more inclusive innovation environment. This may come with a fresh look at IP laws to balance protecting proprietary research and encouraging knowledge sharing to disperse innovation across society, not the giants.
The restrictions we see on political and social/issue-based advertising may be the tip of the iceberg. With access to so much data, regulatory campaigns will need to address how this power affects democratic institutions. I see a growing movement of advocates calling for transparency and accountability, especially as the opacity around AI for decision-making grows.
Self-driving cars will be the tipping point
In 2016, the United States lost forty thousand people to traffic accidents. The annual death toll is equivalent to the 9/11 terrorist attacks occurring every month from January through November, and twice in December. The World Health Organization estimates that there are around 260,000 annual road fatalities in China and 1.25 million around the globe.
Autonomous vehicles are on the path to eventually being far safer than human-driven vehicles, and widespread deployment of the technology will dramatically decrease these fatalities. It will also lead to huge increases in efficiency of transportation and logistics networks, gains that will echo throughout the entire economy.
But alongside the lives saved and productivity gained, there will be other instances in which jobs or even lives are lost due to the very same technology. For starters, taxi, truck, bus, and delivery drivers will be largely out of luck in a self-driving world.
Why it matters:
Take a look at the evolution of Waymo’s pilot programs in cities like Los Angelas, San Francisco, Phoenix, and now Austin. The TikTok and social media reaction to these autonomous rides has been extremely positive. People feel the cars drive more safely than humans, and they like that they don’t need to share a cabin with total stranger. Public acceptance of autonomous vehicles is growing rapidly.
Once people get over their initial hesitation, they love the experience. The speed with which consumers will want to adopt this technology will be much faster than what regulators will be capable of handling. Either way, self-driving technology will be the technology that serves as the battleground for regulations, labour issues, the future of transportation, ethical considerations. So much is wrapped up in this space that once we get through it, the floodgates will open up. We’ll suddenly become comfortable with AI in every aspect of our life.
But first, we’ll have to deal with a lot of heartburn. Existing traffic laws and vehicle safety standards are not equipped to address the nuances of autonomous technology. Differing regulations at municipal, state, and federal levels can create a fragmented legal landscape. Millions of people employed as taxi, truck, bus, and delivery drivers will face unemployment, a prospect they won’t take lightly. American politicians will absolutely pump the breaks on widespread self-driving vehicle deployment the moment they become the subject of attack ads.
The future campaign is seamlessly online and offline
As perception AI gets better at recognizing our faces, understanding our voices, and seeing the world around us, it will add millions of seamless points of contact between the online and offline worlds. Those nodes will be so pervasive that it no longer makes send to think of oneself as “going online.” When you order a full meal just be speaking a sentence from your couch, are you online or offline?
…I call these blended environments OMO: online-merge-offline.
Why it matters
The future of campaigns will change. Traditional campaign strategies will evolve to effectively engage with constituents. The blurring of boundaries between the digital and physical presents both opportunities and challenges. Here’s how:
Real-time interaction. Implement systems that allow for instant feedback and dialogue with constituents/supporters, enhancing responsiveness and fostering a sense of connection.
Personalized messaging on a whole new level. Use AI to tailor messages that resonate with individual voters’ values, concerns, and preferences, regardless of where the interaction occurs. The content would be so customized, there would be no way to replicate it with any other person.
Virtual Town Halls: Interactive events where constituents can engage with candidates in a virtual space that feels personal and immediate.
Experience-Based Messaging: Develop simulations or experiences that allow voters to visualize the impact of policies and proposals.
Dynamic Content: Employ AI to adjust campaign messages in real-time based on current events, public sentiment, and feedback.
Narrative Personalization: Craft stories and narratives that align with individual voter experiences, making the campaign more relatable
We’re on the cusp of big change.