Warning: imminently practical #ArtificialIntelligence advice for medium-sized businesses.
In this video, I revisit my thesis that it is plausibly high-impact to take a careful look at what your employees are doing with #GenerativeAI on their own initiative, and note why this advice is likely most relevant to businesses that are not too big and not too small.
AI for right now: how are people in your org already using AI?
If you are a business leader of any sort it's likely that the most important action you can take apropos of #ArtificialIntelligence is to get a handle on what people in your organization are doing already on their own initiative. I'm blessed to talk to all sorts of people about #AI and the world is doing amazing work right now that is both under-utilized and under-regulated. The remedy is not rare chips or rocket science algorithms but knowledge sharing, training, and just asking around the office. It's not glamorous, but if you ask me the most valuable revelations around running a business never are.
If I am making sense here, please get in touch with me. Now is the time to get started right.
Amazon's supercomputer and AI centralization (or not)
Amazon and Anthropic announcement about their "Ultracluster" supercomputer got me thinking about how #ArtificialIntelligence might be standing at a bit of a crossroads...
How centralized will the high-powered #AI of the future be? Does it require the most unique of hardware? Or will people be able to run it on a bunch of old gaming PCs chained together? The technology will determine a lot about the business, and then the business will in turn shape a lot about what technology researchers pursue.
More detail on my (admittedly speculative) perspective in this video...
The strategic landscape of AI via the lens of Amazon's recent announcements
The strategic landscape around advanced #ArtificialIntelligence models is defined by a bottleneck...
1.) in talent on the software side.
2.) in specialized chips on the hardware side.
In this video, I paint this landscape through the lens of two recent announcement by Amazon that they are
1.) making another large investment in leading #AI startup Anthropic.
2.) flirting with entering the chip-making business.
To continue my pattern of pairs of bullet points, there is narrative also sheds light on...
1.) the recent stumbles of Google
2.) the recent success of NVIDIA
What the history of firearms can teach about the future of AI in business
For many watching this video, the right frame for thinking about #ArtificialIntelligence is people and not technology. In this video, I examine some history around the development and adoption of firearms to point out how innovation in how to organize people is often what determines winners and losers in a wave of new technology.
Get some people and process going for your technology!
My general observation is there is always a bit more technology out there lately than people and process can easily integrate. For many people out there leading businesses the prosaic, impactful thing to do right now is to develop on policies, training, and culture to properly integrate all the #ArtificialIntelligence tools that are going to appear in front of you whether you want them or not.
Perplexity's interesting competitive and legal adventures
The Wall Street Journal and the New York Post are suing nouveau #ArtificialIntelligence search engine Perplexity. In this video, I give some general facts and backstory before explaining why this lawsuit is a bit of a novel combo of old and new types tech lawsuits and explain why it is an unusually good case study to watch if you are interested in the competitive pressures #AI is putting on companies like Google.
AI's romance with nuclear energy
You see #ArtificialIntelligence in the news next to nuclear energy quite a bit these days with Microsoft deal with Constellation to partly reopen Three Mile Island and Google's deal with Kairos Power to open a number of new, smaller reactors. In this video, I discuss the "Why?" of these unions, compare and contrast Google's and Microsoft's approaches, flirt with analyzing the risks, and then give a bit of interesting backstory on the long romance between nuclear and your favorite tech billionaires.
Will OpenAI be there for you down the road?
It can be true that #ArtificialIntelligence is infinitely promising, and true that OpenAI is very good at it, yet still true that things don't work out for them to offer you an #AI product sustainably in a way that suits your vision for your organization. They are on a financial trajectory that will require them to #IPO to continue existing and such things do not always go well, even when people really love the product. Broadly, the companies in the AI space have unusual levels of risk around their stability and that is a business risk the rest of us cannot carelessly ignore.
AI regulation is necessary but very hard
In this video, I share a sense why 1) #ArtificialIntelligence #Regulation is necessary, and 2) why this area of policy is very challenging and thus likely to remain unsettled for some time.
Weaving all the OpenAI stories together...
There are several notable OpenAI stories out today competing with each other for oxygen and I think it’s worth examining them all together. In this video, I recap some important history and weave a number of current events into a broader narrative including...
-the departures of CTO Mira Murati and Chief Research Officer Bob McGrew -OpenAI's efforts to reform itself as a for-profit corporation
-the adventures of OpenAI alums like Ilya Sutskever at other startups
OpenAI always looks less like a research organization and more like a company commercializing existing technology. If there is a red flag in the mix here it is that OpenAI may not be able to make the breakthroughs required to sustain the image that (at least in part) fuels its massive fundraising. #ArtificialIntelligence
AI is for people of influence, adventurers, and the far-sighted
This motivational speech is only for people of influence, adventurers, and those of us compelled by nature to think long-term...
#ArtificialIntelligence
It's tough sledding doing AI research in a large organization
#ArtificialIntelligence is both very mature and very immature from a megacorporate perspective.
You may have seen discussion elsewhere of The Wall Street Journal's coverage regarding Amazon's internal #AI team and its struggles. Google, despite its once dominant position, has often seemed a step behind OpenAI and OpenAI in turn may have done its best work when it was a small not-for-profit research lab...
It's difficult to do these things well in a large, private-sector organization and in this video I explore a variety of reasons why including fundamental cultural requirements around anything resembling basic research as well as the burden coming from megacorporate guardrails in areas like #DataPrivacy.
Future pitfalls buying AI
For many companies the #ArtificialIntelligence revolution will be purchased from a vendor.
In this video, I sketch a (hopefully familiar) prototype for a flawed software evaluation process and indicate why I think #AI could make these kinds of dynamics much worse. The story is approximately is that the more sophisticated and flexible these applications become the more evaluating them will have the subtlety of hiring an employee. It will not be a process that is easily compartmentalized and quantified into a spreadsheet.
Three things to watch to know the fate of OpenAI
It's plausible that as goes OpenAI so goes hashtag#ArtificialIntelligence as well. In this video I discuss three things to watch if you hope to unravel the arcane strands of the future...
1) Does OpenAI continue to lead in qualitatively new and different R&D?
2) Do corporations find prosaic applications of hashtag#LLM or is it just a toy?
3) Do we return to low-interest rate, high liquidity macroeconomic conditions as prevailed before the pandemic? Or have we durably entered a new era?
The Fed's 50 bps cut and the many reasons you should care
You may have heard the #FederalReserve cut interest rates by 50 bps yesterday and this video attempts to answer the question "What does that mean and why do I care?" I go into detail about...
- what the Fed is trying achieve and how its going past and (potential) future
- just how it manipulates interest rates and why this matters on Main Street
- the consequences of the two ways of screwing it up
- what happens on #WallStreet and in your stock portfolio
but then also more unusually
- why #COVID19 is still lurking in the background
- how this sets the stage for the arc of companies like 23andMe and WeWork
To bring it all back to my core wheelhouse, I outline why the yet-to-be-discovered aftermath of this rate cut matters a lot for the fortunes of companies like OpenAI and thus for #ArtificialIntelligence more broadly.
Backstory and relevance of the mass 23andMe board resignations
In this video I discuss not only the recent mass resignations from 23andMe's board but also the longer history of the company and the circumstances, in the company and in broader society, that set the stage for these events. While this is not an #ArtificialIntelligence or #DataPrivacy story per se, it's a great case study in a variety of business themes that are very relevant including - how a macroeconomic shift has altered investor expectations - the legacy of "data is the new oil" - corporate governance at young, entrepreneurial companies I close with the observation that we are really just learning about the downhill stretch of a particular sort of rollercoaster for the first time...
AI is much better at cheating on tests than humans
Are #LargeLanguageModel s, in a sense, "cheating" on the test?
In this video, I share some observations regarding formal and informal metrics for #LLM s, recap (implicitly) some bias-variance theory from machine learning, and reminisce on my days teaching calculus in order to argue there is a danger what looks like intelligence might in some cases might really be more like memorization. The latter is not a terribly cool trick in a machine. If you are in the tricky, philosophically perilous business of comparing humans and #AI, then for many of these tests you should probably do some handicapping in favor of the humans.
#ArtificialIntelligence
Dream with discipline about AI
It's important to dream with discipline about #ArtificialIntelligence.
You will hear me say again and again that #AI v. business is a long-term conversation. Adoption is an urgent matter in some industries while for others more refinement of the technology will be required. What any business can and should do right now is invest in clear, explicit planning to clarify both...
1) ("dream") What is required of a game-changing application? What is the universe of plausible game-changing applications?
2) ("with discipline") Is this available now? If not, what are the warts on current technology that prevent it? How will I know when it is almost ready?
There will come a moment when a starting gun goes off and you want to be finishing a challenging conversation about AI and not just starting one.
The deets on OpenAI's "Strawberry" model
In this video I digest the news around Strawberry, OpenAI's recently released and much anticipated new model with "reasoning" capabilities. Topics discussed include...
-the sorts of problems Strawberry is intended to solve better
-the mostly limited and vague known details of new training techniques
-similarity of form and function with past LLMs, including "hallucinations"
-what OpenAI might be trying to accomplish from a business standpoint