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Information Systems
A Manager's Guide to Harnessing Technology

v10.0 John Gallaugher

1.3 Geek Up—Tech Is Everywhere and You’ll Need It to Thrive

Learning Objectives

  1. Appreciate the degree to which technology has permeated every management discipline.

  2. See that tech careers are varied, richly rewarding, and poised for continued growth.

Shortly after the dot-com bubble, there was a lot of concern that tech jobs would be outsourced, leading many to conclude that tech skills carried less value and that workers with tech backgrounds had little to offer. Turns out this thinking was stunningly wrong. Tech jobs boomed, and as technology pervades all other management disciplines, tech skills are becoming more important, not less. Today, tech knowledge can be a key differentiator for the job seeker. It’s the worker without tech skills who needs to be concerned.

As we’ll present in depth in a future chapter, there’s a principle called Moore’s Law that’s behind fast/cheap computing. And as computing gets both faster and cheaper, it gets “baked into” all sorts of products and shows up everywhere: in your pocket, in your vacuum, and on the radio frequency identification (RFID) tags that track your luggage at the airport.

Well, there’s also a sort of Moore’s Law corollary that’s taking place with people, too. As technology becomes faster and cheaper, and developments like open source software, cloud computing, generative artificial intelligence, software as a service (SaaS), and outsourcing push technology costs even lower, tech skills are being embedded inside more and more job functions. And ubiquitous tech fuels our current era of “Big Data,” where bits-based insights move decision-making from hunch to science. What this means is that, even if you’re not expecting to become the next Tech Titan, your career will doubtless be shaped by the forces of technology. Make no mistake about it—there isn’t a single modern managerial discipline that isn’t being deeply and profoundly impacted by tech.

Finance

Many business school students who study finance aspire to careers in investment banking. Many i-bankers will work on IPOs (initial public stock offerings), in effect helping value companies the first time these firms wish to sell their stock on the public markets. IPO markets need new firms, and the tech industry is a fertile ground that continually sprouts new businesses like no other. Other i-bankers will be involved in valuing merger and acquisition (M&A) deals, and tech firms are active in this space, too. The technology sector has become a major driver of global M&A activity. Leading tech firms are flush with cash and constantly on the hunt for new firms to acquire. In just five years, Google has bought a whopping 103 firms, IBM has bought sixty-four, Microsoft has bought sixty-three, Cisco has bought fifty-seven, and Intel has bought forty-eight! Yahoo! bought thirty-seven companies in a year and a half. Trader beware—software might take your job. Researchers in the United States and China have shown an AI approach that beat major trading indices as well as traditional investment portfolio allocation strategies. No wonder so-called robo-advisors like Betterment have been growing by billions of dollars. And even in nontech industries, technology impacts nearly every endeavor as an opportunity catalyst or a disruptive wealth destroyer. The aspiring investment banker who doesn’t understand the role of technology in firms and industries can’t possibly provide an accurate guess at how much a company is worth.

Those in other finance careers will be lending to tech firms and evaluating the role of technology in firms in an investment portfolio. Most of you will want to consider tech’s role as part of your personal investments. And modern finance simply wouldn’t exist without tech. When someone arranges for a bridge to be built in Shanghai, those funds aren’t carried over in a suitcase—they’re digitally transferred from bank to bank. And forces of technology blasted open the two-hundred-year-old floor trading mechanism of the New York Stock Exchange, in effect forcing the NYSE to sell shares in itself to finance the acquisition of technology-based trading platforms that were threatening to replace it. Computer-automated trading, where a human doesn’t touch the deal at all, is responsible for some 60 percent of U.S. equity trading volume. For a look at the importance of tech in finance, consider that banking giant Chase spends $4 billion a year on technology and employs over twelve thousand technologists, globally. Tech isn’t a commodity for finance—it’s the discipline’s lifeblood.

How AI Is Powering the Future of Financial Services, JPMorgan Chase & Co.

Hear JPMorgan Chase's Sameena Shah discuss “How AI Is Powering the Future of Financial Services” as part of the MIT Technology Review EmTech Digital Conference. 

Accounting

If you’re an accountant, your career is built on a foundation of technology. The numbers used by accountants are all recorded, stored, and reported by information systems, and the reliability of any audit is inherently tied to the reliability of the underlying technology. Increased regulation raises executive and board responsibility and ties criminal penalties to certain accounting and financial violations. Legislation has raised the stakes for mismanagement and misdeeds related to a firm’s accounting activities and have ratcheted up the importance of making sure accountants (and executives) get their numbers right. Negligence could mean jail time. This means the link between accounting and tech has never been tighter, and the stakes for ensuring systems accuracy have never been higher.

Business students might also consider that while accounting firms regularly rank near the top of Bloomberg Businessweek’s “Best Places to Start Your Career” list, many of the careers at these firms are highly tech-centric. Every major accounting firm has spawned a tech-focused consulting practice, and in many cases, these firms have grown to be larger than the accounting services functions from which they sprang. Today, Deloitte’s tech-centric consulting division is larger than the firm’s audit, tax, and risk practices. At the time of its spin-off, Accenture was larger than the accounting practice at former parent Arthur Andersen (Accenture executives are also grateful they split before Andersen’s collapse in the wake of the prior decade’s accounting scandals). Now, many accounting firms that had previously spun off technology practices are once again building up these functions, finding strong similarities between the skills of an auditor and skills needed in emerging disciplines such as information security and privacy.

Will AI Replace Accountants?

Interested in a career in accounting? Then you might be interested in this interview with Dr. Mike Willis, the head of the University of Cambridge (UK) Director of Accounting Grad Program, on the future of the accounting profession and the impact of AI on the field.

Marketing

Technology has thrown a grenade onto the marketing landscape, and as a result, the skill set needed by today’s marketers is radically different from what was leveraged by the prior generation. Online channels have provided a way to track and monitor consumer activities, and firms are leveraging this insight to understand how to get the right product to the right customer, through the right channel, with the right message, at the right price, at the right time. The success or failure of a campaign can often be assessed immediately based on online activity such as website visit patterns and whether a campaign results in an online purchase.

The ability to track customers, analyze campaign results, and modify tactics has amped up the return on investment of marketing dollars, with firms increasingly shifting spending from tough-to-track media such as print, radio, and television to the Web. And new channels continue to emerge: smartphone, tablet, smart TV, smartwatch and other wearables, smart auto, and more. Look to Apple to show how fast things grow: Within roughly four years, iOS devices were in the hands, backpacks, purses, and pockets of over two hundred million people worldwide, delivering location-based messages and services and even allowing for cashless payment. Apple has since sold over two billion iPhones worldwide, creating the primary channel for all sorts of customer engagement.

The rise of social media is also part of this blown-apart marketing landscape. Now all customers can leverage an enduring and permanent voice, capable of broadcasting word-of-mouth influence in ways that can benefit and harm a firm. Savvy firms are using social media to generate sales, improve their reputations, better serve customers, and innovate. Social media has also lead to the $21 billion+ influencer economy, while AI has ushered in the era of virtual influencers who have garnered millions of online followers and earned millions of promotional dollars along the way. Those who don’t understand this landscape risk being embarrassed, blindsided, and out of touch with their customers.

Search engine marketing (SEM), search engine optimization (SEO), customer relationship management (CRM), personalization systems, and a sensitivity to managing the delicate balance between gathering and leveraging data and respecting consumer privacy are all central components of the new marketing toolkit. And there’s no looking back—tech’s role in marketing will only grow in prominence. Analyst firm Gartner predicts that chief marketing officers are on a path to spend more on technology than any other function within the firm.

Computer-Generated Influencers on Social Media Give a Peek into the Future of Marketing

See how virtual influencers have garnered millions of followers. Also learn the term uncanny valley.

0.63 to 3.7199999999999998- That's Lil McKayla and Bermuda and Emma.
3.7199999999999998 to 6.42Social media stars with
millions of followers,
6.42 to 9.48sharing selfies with friends,
posting funny videos,
9.48 to 14.07typical teen stuff, except
these aren't teens. Hey wanna
14.07 to 16.02- Be friends. Who does
16.02 to 17.02- That?
17.02 to 18.21They're not even human.
18.21 to 22.23They're computer generated
robots, virtual influencers
22.23 to 23.82with plenty of fans.
23.82 to 27.24- As soon as I clicked on
her page, I think I was just
27.24 to 28.83first obviously confused,
28.83 to 31.5but then upon just looking
at it, it was just,
31.5 to 32.85it was just so cool to me.
32.85 to 34.74- Carla Montgomery has
been following little
34.74 to 36.09McKayla for years.
36.09 to 39.6- She like lives as a robot in our world,
39.6 to 42.12and I think, you know,
like her skincare routine,
42.12 to 44.55she is like, well, sorry
guys, it's just WD 40.
44.55 to 46.32I think it's just super unique
46.32 to 48.27- And super realistic.
48.27 to 50.37- In looking at little Miguel,
it's quite interesting.
50.37 to 53.1- Carl McDormand studies
human computer interaction
53.1 to 56.13and explains the theory
of the uncanny valley.
56.13 to 58.26This graph explains what that is.
58.26 to 60.9When a person looks at a
series of virtual faces,
60.9 to 62.105they feel comfortable.
62.105 to 63.45As those faces become more
63.45 to 66.36and more real, looking up
to a certain point right
66.36 to 68.61before they look a hundred percent human,
68.61 to 71.13that's when people start
feeling uncomfortable.
71.13 to 73.98And it's that cute to creepy reaction
73.98 to 76.47that researchers call the uncanny valley,
76.47 to 78.09but something else is happening
78.09 to 80.13with these virtual influencers.
80.13 to 82.62- People get used to characters.
82.62 to 86.01So if they're interacting
with a particular
86.01 to 90.66virtual influencer frequently, then
90.66 to 92.91it may have been uncanny at some point,
92.91 to 95.04but over time they'll get used to it.
95.04 to 96.84- The interactions can be entertaining,
96.84 to 98.88but researchers worry about the risk.
98.88 to 103.88- I think there are general
concerns about social media
104.31 to 107.76kind of shaping young
minds and young brains,
107.76 to 110.76and I think the concerns
would be even greater
110.76 to 113.01with the digital influencers
113.01 to 117.15because they're exploiting
the human interface, which is
117.15 to 119.61to say they look like people.
119.61 to 122.67And so this gives them a lot of leverage.
122.67 to 127.38It can be designed very
specifically to be liked,
127.38 to 130.11to be credible and so on.
130.11 to 133.26And that's a kind of new
danger that we haven't seen
133.26 to 134.85before in society.
134.85 to 137.28- These influencers
creating relationships,
137.28 to 140.91sharing stories appearing in
the real world alongside real
140.91 to 143.19people like Virtual Star Ray,
143.19 to 145.59showing up on this magazine cover.
145.59 to 149.73So if you look, can you tell
who's real and who's digital?
149.73 to 153.48- It questions the line of
reality versus virtual reality.
153.48 to 156.33- Virginia Tech professor
Donna Alek thinks these virtual
156.33 to 158.435influencers are becoming more popular
158.435 to 161.79because of their targeted
approach toward younger audiences,
161.79 to 163.83which can really pay off.
163.83 to 166.35Influencer marketing
overall is a huge industry
166.35 to 169.68with brands expected to
spend up to $15 billion on it
169.68 to 172.59by next year, and these
digital stars are coming
172.59 to 174.48for a bigger piece of that pie,
174.48 to 177.36making money from
sponsored posts, concerts,
177.36 to 179.44even virtual reality video games.
179.44 to 182.95Lil McKayla reportedly made
more than $11 million last year.
182.95 to 185.92- It's translated now to
somebody they can relate to.
185.92 to 188.17Someone that looks like
them, talks like them,
188.17 to 190.45feels like them, and then them translating
190.45 to 193.27to be an influencer for
these young communities.
193.27 to 196.605- There's a company behind that looking
196.605 to 198.195to sell something, right?
198.195 to 200.32Like this comes back to like branding
200.32 to 202.27and influencing at its very core.
202.27 to 205.99- Absolutely. And it is the
wizard behind the curtain
205.99 to 208.84that is playing like a puppeteer
208.84 to 211.24with these virtual beings.
211.24 to 212.38You're not only getting into it,
212.38 to 213.55you're getting into you know
213.55 to 214.99who they're friends with, who they're not.
214.99 to 216.4And then by the way, I'm using this
216.4 to 217.69product and that product.
217.69 to 220.45- One of the companies
creating virtual influencers
220.45 to 223.15emphasized it's a new way to story tell,
223.15 to 226.15but did not respond to a longer
list of questions from us.
226.15 to 229.33Another gave us a lengthy
statement from its CEO saying,
229.33 to 231.82in part, the next
generation of young people
231.82 to 234.16who can smell whether
something seems authentic
234.16 to 236.29or not, will be the main players in the
236.29 to 237.58age of the metaverse.
237.58 to 241.63Adding virtual humans are a
mirror to humans to teach us
241.63 to 243.34what we really should treasure.
243.34 to 245.95What do parents need
to know about the rise
245.95 to 247.845of virtual influencers online? Talk
247.845 to 250.18- To your kids and find
out what's going on.
250.18 to 252.85Get involved with them. We
need to be informed as parents
252.85 to 254.68and go a little bit beyond just the birds
254.68 to 258.01and bees these days because
there's a lot more influencers
258.01 to 260.8and impact out there that
could really change the future
260.8 to 262.93of our children based on how
they're being conditioned.
265.36 to 267.34- Thanks for watching our YouTube channel.
267.34 to 268.875Follow today's top stories
268.875 to 272.29and Breaking news by
downloading the NBC News app.

Operations

A firm’s operations management function is focused on producing goods and services, and operations students usually get the point that tech is the key to their future. Quality programs, process redesign, supply chain management, factory automation, and service operations are all tech-centric. These points are underscored in this book as we introduce several examples of how firms have designed fundamentally different ways of conducting business (and even entirely different industries), where value and competitive advantage are created through technology-enabled operations.

Google Cloud Manufacturing Operations Solutions

This short video from Google Cloud platform shows how Big Data, robotics, and other technologies are transforming the factory floor, and how goods are produced for market.

Human Resources

Technology helps firms harness the untapped power of employees. Knowledge management systems are morphing into social media technologies—social networks, wikis, and Twitter-style messaging systems that can accelerate the ability of a firm to quickly organize and leverage teams of experts. And crowdsourcing tools and question-and-answer sites like Quora and Stack Overflow allow firms to reach out for expertise beyond their organizations. Human resources (HR) directors are using technology for employee training, screening, and evaluation. The accessibility of end-user technology means that every employee can reach the public, creating an imperative for firms to set policy on issues such as firm representation and disclosure and to continually monitor and enforce policies as well as capture and push out best practices. The successful HR manager recognizes that technology continually changes an organization’s required skill sets as well as employee expectations.

The hiring and retention practices of the prior generation are also in flux. Recruiting hasn’t just moved online; it’s now grounded in information systems that scour databases for specific skill sets, allowing recruiters to cast a wider talent net than ever before. Job seekers are writing résumés with keywords in mind, aware that the first cut is likely made by a database search program, not a human being. The rise of professional social networks also puts added pressure on employee satisfaction and retention. Prior HR managers fiercely guarded employee directories for fear that a headhunter or competitive firm might raid top talent. Now the equivalent of a corporate directory can be easily pulled up via LinkedIn, a service complete with discrete messaging capabilities that can allow competitors to rifle-scope target your firm’s best and brightest. Thanks to technology, the firm that can’t keep employees happy, engaged, and feeling valued has never been more vulnerable.

And while many students have been wisely warned that inappropriate social posts can ruin their job candidacy, also know that the inverse is true. In many ways social media is “the new résumé.” Thoughtful blog posts, a compelling LinkedIn presence, Twitter activity reflecting an enthusiastic and engaged mind, and, for tech students, participation in collaborative coding communities like GitHub all work to set apart a candidate from the herd. If you can’t be found online, some employers may wonder if you have current skills, or if you have something to hide.

How Companies, Human Resource Departments Are Using AI When Hiring, Plus a Look at Regulation

Yahoo! Finance discusses new laws and a shifting set of concerns for organizations that are using more technology as part of their human resources functions and especially their hiring processes.

2.61 to 4.02- It is well known
4.02 to 7.86that a human is not always
the first one you see when
7.86 to 9.815to resume your apply to a new job.
9.815 to 13.2Often it's a computerized
system that scans your resume
13.2 to 15.42for the relevant skills
and qualifications.
15.42 to 16.44I don't know if everybody knows
16.44 to 20.46that nearly one in four
organizations use AI
20.46 to 23.91to support HR activities
according to the Society
23.91 to 26.1for Human Resource Management.
26.1 to 30.69Of those nearly eight in
10 use AI tools for hiring.
30.69 to 33.9Now those organizations are
getting hit with some new laws.
33.9 to 36.33They must comply with Yahoo Finances.
36.33 to 38.82Carry Hannin has that
story. Hi there, Carrie.
38.82 to 40.5What are we, what are we hearing?
40.5 to 42.81- Hey, Dave. Yeah, well,
you know, it's been a bit
42.81 to 46.145of the Wild West out there
in terms of regulations
46.145 to 49.5and restrictions on these AI tools
49.5 to 50.91that employers use in hiring.
50.91 to 52.65I mean, yeah, there's
things like, you know,
52.65 to 54.93they can game the system
for your, you know, trying
54.93 to 56.19to get the right keywords,
56.19 to 58.32but there's some inherent bias
58.32 to 61.59that can built be built into
these for, you know, race
61.59 to 63.66and gender and all kinds of things.
63.66 to 65.61So, you know, we haven't had seen a lot
65.61 to 67.29of regulation out there to date,
67.29 to 71.76but in our, in April in New
York City for the first time,
71.76 to 73.38there's going to be a law
73.38 to 75.75that actually addresses this issue.
75.75 to 77.37And so what's gonna happen there,
77.37 to 79.11and it sort of generally looks at
79.11 to 82.05how most employers are
using these AI tools
82.05 to 85.98and it's gonna really
put it on the employer
85.98 to 88.955to be transparent, to
tell people, Hey, this is
88.955 to 92.58what we're looking for, the
qualifications, these, you know,
92.58 to 94.44AI tools are gonna be scanning for.
94.44 to 96.24These are the characteristics,
96.24 to 98.43the job qual qualifications you need.
98.43 to 101.04And if someone feels
that it's not going to,
101.04 to 104.73they're not able to be
judged fairly in the system,
104.73 to 107.52they have the right to ask
to try a different way.
107.52 to 108.93Now that gets a little tricky.
108.93 to 110.82The whole thing is rather complicated
110.82 to 114.3and it's going to be pretty
messy as we move forward.
114.3 to 116.19But, so New York City,
that's the first one.
116.19 to 118.08It was supposed to start in January,
118.08 to 121.08but employers are scrambling
to figure out what do they need
121.08 to 122.555to do to find out
122.555 to 124.95and do these audits on their systems
124.95 to 127.86to see if there is bias that's happening.
127.86 to 129.18And we all can complain about it,
129.18 to 130.74but they are gonna be the ones
130.74 to 132.69that will be responsible if there are
132.69 to 134.64lawsuits on discrimination.
134.64 to 135.935- Certainly some changes coming there.
135.935 to 138.81Carrie, what are we hearing
just about the rise in AI aided
138.81 to 140.225video interviews?
140.225 to 142.5There's a little bit more talk about that.
142.5 to 144.78What can you tell us there?
And just there's some potential
144.78 to 146.94shortfalls you would also think We'll see.
146.94 to 148.95- Exactly. I'm so glad you asked that.
148.95 to 151.86Now this is something
that the EEOC, you know,
151.86 to 156.3the US Equal Employment
Opportunity Commission put out some
156.3 to 159.15things, some guidance on
this, particularly for people
159.15 to 161.375with disabilities because, oh my gosh,
161.375 to 164.7this has become the rage now
that you're interviewed by sort
164.7 to 167.43of a chat bot on this automated,
167.43 to 168.9you don't even have a person.
168.9 to 171.84There's no sort of, it's
really a bizarre experience.
171.84 to 176.16But what it does is people
are judged on their speech
176.16 to 179.74patterns, the words they
use, their facial muscles,
179.74 to 182.86all kinds of things that
the AI can, you know,
182.86 to 184.57the tool can judge you on.
184.57 to 188.2Now, if you have a
disability, say for example,
188.2 to 191.44a speech impediment,
that can really come up
191.44 to 194.17to really haunt you in
these sort of, in interviews
194.17 to 195.19and you can be discarded
195.19 to 198.28because they use the way your,
the rhythm of your speech,
198.28 to 203.2the cadence to determine if you
can make good decisions now.
203.2 to 204.76So that is something that, you know,
204.76 to 206.65opens up a whole other can of worms
206.65 to 209.89and I think we're gonna see
a lot more moving forward in
209.89 to 211.96that area as well.
211.96 to 213.91- All right. Great stuff
Kerry Hannan, thanks so much.

The Law

And for those looking for careers in corporate law, many of the hottest areas involve technology. Intellectual property, patents, piracy, and privacy are all areas where activity has escalated dramatically in recent years. The number of U.S. patent applications awaiting approval has tripled in the past decade, while China saw a threefold increase in patent applications in just five years. Firms planning to leverage new inventions and business methods need legal teams with the skills to sleuth out whether a firm can legally do what it plans to. Others will need legal expertise to help them protect proprietary methods and content, as well as to help enforce claims in the home country and abroad. Tech is increasingly being used to automate rote legal tasks, and generative AI is becoming mainstream in the legal profession; however, we’ve also seen examples of clueless lawyers who have violated professional practice standards by submitting ChatGPT-generated legal analyses that presented bogus, non-existent examples.

How CoCounsel's AI Assistant Is Making Lawyers Unstoppable!

Watch a brief demonstration of CoCounsel, one of many generative AI tools that are enhancing how attorneys do their jobs. 

0 to 2.67- The best way to demonstrate
that is to jump right in
2.67 to 4.44and take a look at co-counsel.
4.44 to 5.64So let's jump right in.
5.64 to 8.01- This is co-counsel. I'll start here like
8.01 to 9.45with other chat applications
9.45 to 10.95that people may have been aware of.
10.95 to 12.515If you want kind of ask coun
12.515 to 13.83to do anything for you via chat.
13.83 to 18.39So you can say, write up a
quick summary why legal tech
18.39 to 22.56enthusiasts should be excited
about large language models.
22.56 to 24.72We've programmed
co-counsel to have a number
24.72 to 25.86of legal specific skills.
25.86 to 27.96The six that we're launching
with are things like asking
27.96 to 30.21to review documents, to do legal research,
30.21 to 33.12to extract data from a
contract to summarize large
33.12 to 35.64and lengthy difficult
documents, other similar skills.
35.64 to 37.77So to give you a sense of
how this works in practice,
37.77 to 40.71I might ask my AI to do
legal research for me
40.71 to 44.67and say, what is the pleading
standard allegations under the
44.67 to 47.16False Claims Act in the ninth circuit?
47.16 to 49.14I'm just talking to this
like I would a colleague.
49.14 to 52.08What's happening behind
the scenes is it's deciding
52.08 to 54.12what search queries to
run based on this query.
54.12 to 56.31It is running those search
queries against our database
56.31 to 59.04of daily updating cases,
statutes, rules and regulations.
59.04 to 60.72It is reading hundreds of results
60.72 to 61.98that come back from these searches
61.98 to 63.03and then it's gonna use that
63.03 to 66.09to put together a legal
research memo in a response.
66.09 to 69.9- How can lawyers who are
using this know that the result
69.9 to 71.64that comes back is reliable?
71.64 to 74.49- It's a great question. The
short answer is two pieces.
74.49 to 77.58The first is I'll kick
off a doc review project
77.58 to 82.14that's gonna kind of similar,
uploading 33 speeches
82.14 to 84.15that Barack Obama's given
84.15 to 85.59and you can ask any questions in it.
85.59 to 89.88Does Obama make a joke? Does
Obama mention his daughter's?
89.88 to 91.29- Does he mention his dog?
91.29 to 94.59- Does the speech include veiled
94.59 to 96.39criticism of Republicans?
96.39 to 98.37What this is gonna do is bring back
98.37 to 99.42answers relatively quickly.
99.42 to 101.79It's also gonna empower
you to find the exact page
101.79 to 103.83where it claims it got the
answer from for you to verify
103.83 to 105.27for yourself for all of the skills.
105.27 to 107.555It's going to answer
questions based on the
107.555 to 108.84cases that it's itself reading.
108.84 to 111.78It's also going to answer
questions based on the documents
111.78 to 114.15that it's reading so you can
verify that entirely yourself.
114.15 to 115.38There's also another set of things
115.38 to 117.63that we do on the
product side to make sure
117.63 to 120.72that it's answering based
solely on the information
120.72 to 121.92that it's, that it's reading,
121.92 to 124.65that it's not pulling from
extraneous information from kind
124.65 to 126.93of from its memory already
it's, it's pulled dozens
126.93 to 129.12of cases will in a moment kind
129.12 to 131.94of start writing it's reason
why it believes this case is
131.94 to 133.38relevant for every single of these cases.
133.38 to 135.605- In addition to actually
generating the memo,
135.605 to 139.44it's also showing you the cases
from which it has drawn the
139.44 to 140.58expertise in the memo
140.58 to 142.8and the sections of those
cases that are relevant.
142.8 to 145.38- That's exactly right. You
can see right now is currently
145.38 to 147.48typing out it's memorandum.
147.48 to 148.62This is the kind of work that
148.62 to 150.24as an associate would take me hours.
150.24 to 151.35It's giving me this deep
151.35 to 152.73and nuanced nuance based on the career.
152.73 to 153.84- I think some people worry
153.84 to 155.315that this would replace a lawyer,
155.315 to 158.52but really what it's doing
is giving you an an extremely
158.52 to 162.065good place to start with the
memo as well as the cases so
162.065 to 165.15that you can go and look
at them and tweak the memo
165.15 to 169.08and save many hours of time
that you would've spent.
169.08 to 171.9It still requires the human
to look over it in the end
171.9 to 173.765and ensure that it's expressed the way
173.765 to 175.23that it would like to be expressed.
175.23 to 176.88- You go a step further than
that. First of all, I think
176.88 to 178.71that lawyer is empowered with
this are gonna be able to do
178.71 to 180.04so much more, so much better
180.04 to 181.9and so much faster for their clients
181.9 to 183.91that they're gonna be in much more demand
183.91 to 185.41and gonna be put in a position
185.41 to 187.27to do really great work for their clients.
187.27 to 188.65I think that's gonna be really empowering.
188.65 to 189.94They're not gonna see less work
189.94 to 191.26and see a lot more demand for their work
191.26 to 192.37at a cheaper lower price.
192.37 to 194.32I also think it's actually
knowing what questions to ask
194.32 to 196.72and knowing what to look for
in the documents, what to look
196.72 to 198.88for in the cases, how to
conduct that legal research.
198.88 to 201.13I think the new skill is
going to be about deciding
201.13 to 203.83how do you delegate to this
incredibly powerful asset
203.83 to 204.94that is going through hundreds
204.94 to 207.16or thousands of pages of
text incredibly quickly.
207.16 to 208.24So for example, for every one
208.24 to 210.34of these it's saying
did Obama make a joke?
210.34 to 211.75He jokes about not having
remarks in front of him
211.75 to 213.82and also has a letter to to
talk about this presidency.
213.82 to 216.37- But additional information
is so critical for lawyers
216.37 to 218.47and so useful to get that on the page.
218.47 to 221.2- Exactly. It's kind of
explainability, including pointing
221.2 to 222.67to the exact right page in the case
222.67 to 226.03and even open up the page
four of this speech to confirm
226.03 to 227.86or deny is a really critical aspect
227.86 to 229.12of working with this kind of ai. I
229.12 to 231.58- Can see that in some
cases it said insufficient.
231.58 to 232.87So if you ask it a question
232.87 to 235.57and it does not know the
answer, will it actually say,
235.57 to 236.59I don't know the answer
236.59 to 238.72or I have insufficient
information in order
238.72 to 240.075to respond to this question.
240.075 to 241.57- That's exactly right. That's
exactly what it's doing.
241.57 to 244.96- I had heard that there was
a limitation to the number
244.96 to 248.895of pages that this type of
technology is able to read,
248.895 to 251.89but you've just added many
pages worth of speeches.
251.89 to 254.14Have you architected
something on the backend
254.14 to 257.8that allows co-counsel to read many pages
257.8 to 259.15of documents? Is there a limit?
259.15 to 261.19- Yeah, that's one of the
major engineering breakthroughs
261.19 to 263.83that we are working on to make
this kind of technology work,
263.83 to 267.135which is putting it in a position
to read through thousands
267.135 to 269.05of pages, a hundred thousand
pages for the large ones
269.05 to 271.93that work with us, they can
read up to 500,000 pages
271.93 to 274.12of documents overnight
using this technology,
274.12 to 276.01putting them in position
to do an intense amount
276.01 to 279.64of e-discovery or categorizing
documents their CAM database.
279.64 to 281.14So that's major breakthrough
that as you're working
281.14 to 282.67- On Jake, this is fascinating.
282.67 to 284.895I noticed that one of the
skills is summarization,
284.895 to 287.62which is another use case we
are seeing in the industry.
287.62 to 291.28I'm very interested in the
extracting data from a contract
291.28 to 292.28- Here.
292.28 to 293.5For example, I'm uploading, you know,
293.5 to 294.855a few different commitment letters
294.855 to 297.4and private equity deals for
leveraged buyouts for loans.
297.4 to 298.75You can ask any question like
298.75 to 300.975who are the parties to the contract?
300.975 to 304.15What is the termination
fee? I can specify that.
304.15 to 306.13I want that in the form of a
number by the way, if it's,
306.13 to 307.99it's an interpretation
wrong to introducing kind
307.99 to 309.46of everywhere, give you the opportunity
309.46 to 310.63to re-edit your question
310.63 to 312.19and that way you'll
have a higher likelihood
312.19 to 314.17of it really finding
things you're looking for
314.17 to 317.075- Helping you engineer prompts
that are effective. Exactly.
317.075 to 319.72- Very clever This, this is
ultimately extremely flexible.
319.72 to 321.045So you may have as a firm
321.045 to 322.63or as an attorney your own set
322.63 to 324.76of questions you wanna
ask against contracts
324.76 to 326.68and this will like document reviews,
326.68 to 328.66pull out deep answers to those questions.
328.66 to 330.855- I'm curious about a
couple of other things.
330.855 to 332.26One is security.
332.26 to 334.605It's one of the things that
law firms have been concerned
334.605 to 337.75about with chat GBT,
which is an open source.
337.75 to 340.42Firms are often very
concerned about security
340.42 to 341.59around their own work product
341.59 to 343.45and contracts within a transaction.
343.45 to 347.05How can they feel safe using
co-counsel on their own
347.05 to 348.28internal work product?
348.28 to 350.2- Being in legal tech
for the last 10 years
350.2 to 351.37and working with 60
351.37 to 352.84of the largest law
firms in the trade means
352.84 to 354.7that we totally get the security concerns
354.7 to 356.29that firms may have around
these kinds of things.
356.29 to 358.015And part what's important
358.015 to 359.51to distinguish is when you're working
359.51 to 361.4with a public tool like A GPT,
361.4 to 362.87every time you put information into it in
362.87 to 364.73that model is learning the information
364.73 to 365.78from your discussions with it.
365.78 to 366.98Large companies like Amazon
366.98 to 369.32and even Microsoft, which is
a big investor in open ai,
369.32 to 370.88the folks who make chat GPT,
370.88 to 372.29so their employees don't put any
372.29 to 373.52confidential information in here.
373.52 to 375.11It can leak into the big model for us.
375.11 to 378.59We have our own private
servers that host these models.
378.59 to 380.72It never learns by design.
380.72 to 383.03Any of the information you're
putting into it never retains
383.03 to 384.29the information that
you're putting into it.
384.29 to 387.62The only way that the data
comes in contact with our AI is
387.62 to 388.7for the processing, for reading
388.7 to 389.69and understanding of putting it
389.69 to 390.8out and then it's gone forever.
390.8 to 393.14That's a really, really important
part of our architecture.
393.14 to 395.24Frankly, firms should ask
these kinds of questions.
395.24 to 397.52There'll be many more in
the future that attempt
397.52 to 399.62to do these things using
this brand of technology.
399.62 to 402.08There is a major difference
between the companies
402.08 to 403.94that can provide that
kind of level of security
403.94 to 406.435and capabilities and those
who cannot, firms have
406.435 to 407.78to look into that when they're trying
407.78 to 409.79to decide whether they
leverage this really impressive
409.79 to 411.62and exciting technology,
which they should.
411.62 to 412.82They have to look into whether
412.82 to 414.38or not the model is being trained on the
414.38 to 415.585documents we we put into it.
415.585 to 418.13- Are you able to save
a model effectively?
418.13 to 419.42So a list of questions so
419.42 to 421.34that you could reuse that for later.
421.34 to 424.975And with data that is extracted,
are you able to tell it
424.975 to 428.6to populate a summary table
so that it extracts data
428.6 to 431.45and then also populates a table
431.45 to 434.81or a database with the data
that has been extracted?
434.81 to 436.34- Absolutely. So what we do
436.34 to 439.52for example here is I've
uploaded these documents earlier.
439.52 to 441.355I'm gonna select them
and I can run the same
441.355 to 442.52analysis of those documents.
442.52 to 444.38It also saves previously used questions.
444.38 to 446Those are the questions
that we asked earlier.
446 to 448.22You can heart them so that you
at the top always I can kick
448.22 to 450.65off a doc review with exactly
those kinds of questions again
450.65 to 451.88and we're gonna make
it even easier in your
451.88 to 453.17future to save templates.
453.17 to 454.82In terms of getting the answers out,
454.82 to 456.29we always allow you to download things.
456.29 to 457.64For example, doing Excel spreadsheet
457.64 to 460.31or in the case of like a legal
research memo to download it
460.31 to 462.68as a Word document and
to you'll get, you know,
462.68 to 465.38all this context, all the
cases, the answer links
465.38 to 466.79to the cases, the quotes in the cases
466.79 to 467.87that are relevant, et cetera.
467.87 to 470.33All in a a version that
is easy for you to use.
470.33 to 471.47- Oh, this is fantastic. Jake,
471.47 to 474.38you're already working in beta
with a number of large firms
474.38 to 475.94and other entities as well.
475.94 to 477.895- That's right. We found it
very important over the last
477.895 to 480.205nearly six months now to engage with folks
480.205 to 483.44who are at large law firms,
small law firms, nonprofits,
483.44 to 485.99inhouse council, to
really through its paces
485.99 to 487.31and make sure that works really
487.31 to 488.57well in the early days of the beta.
488.57 to 491.48They, they caught issues with
the way you interact with it,
491.48 to 493.16what kind of skills we
should really prioritize
493.16 to 495.685and use, help it make it faster,
all those kinds of things.
495.685 to 497.48And then beta customers
did a really great work
497.48 to 499.1and had really great
feedback for us to get
499.1 to 500.15to the place we are now.
500.15 to 501.8It puts us in a position
that when we rolled out
501.8 to 503.21to the broader market, a lot
503.21 to 504.47of the kinks have been ironed out.
504.47 to 506.395Still new technology still
needs to be worked on
506.395 to 508.85and always will be like
we're constantly improving,
508.85 to 510.5but a lot of the kings
have been ironed out
510.5 to 512.6and we get to come out
with a pretty fully baked
512.6 to 513.6- Product.
513.6 to 515.9Well this is fantastic. It
looks like co-counsel is the
515.9 to 517.135lawyer's new best friend.
517.135 to 518.66- Coco their best friend. Yeah, Coco,
519.5 to 520.5- I love it.
520.5 to 521.99Exactly like their mascot.
Their mascot. Yeah.
521.99 to 524.87As of the day this goes live
March 1st, is it available?
524.87 to 528.59Can firms access this
call? You demo pilot?
528.59 to 530.48- We're gonna be working
with a smaller handful
530.48 to 531.86of firms at the very beginning
531.86 to 533.725because we wanna make sure that
533.725 to 534.92as we deploy this technology,
534.92 to 537.48it's un responsibly in a
well-trained kind of manner
537.48 to 540that we have the server
capacity, which is very expensive
540 to 541.5and difficult to obtain, to make sure
541.5 to 543.33that everybody's using it
gets a great experience.
543.33 to 545.765But we'll be rolling it out
to as many people as we can,
545.765 to 547.59as fast as we can in that, making sure
547.59 to 548.67that the first customers
548.67 to 550.145who are onboarded have a great experience.
550.145 to 552.03So if you're interested, come March 1st,
552.03 to 553.92we'll have very easy ways to get in touch
553.92 to 556.59and know that we're working
around the clock to make it so
556.59 to 558that after that first batch
558 to 559.56or two of clients who come in early
559.56 to 560.88and get rolled out, that we'll get
560.88 to 562.74to the wider market as soon as we can.
562.74 to 565.26- Jake, thank you so much
for showing us co-counsel
565.26 to 568.415or Coco, it looks like a
groundbreaking new product,
568.415 to 569.58so congratulations.
569.58 to 570.21Thank you so much.

Information Systems Careers

While the job market goes through ebbs and flows, recent surveys have shown there is no end in sight for workers with technology skills. In the U.S. News ranking of “Best Jobs in America” tech skills made up all of the top fifteen jobs that weren’t part of the health care industry. Glassdoor listed tech jobs in nine of the top ten slots in its “50 Best Jobs in America.” The World Economic Forum’s Future of Jobs Report ranked IT job titles in twelve of its top twenty, and demand for computer and information technology workers will outstrip supply for years to come. Information- and computer-related careers are expected to grow by double digits for at least the next decade. Want to work for a particular company? Chances are they’re looking for tech talent. Eighty-two percent of CEOs expect to need more tech workers. LinkedIn says, “In the next five years, we’ll see 150 million more technology-related jobs across industries globally, so demand for digital skills is very much on the rise.” The Harvard Business Review has declared “Data Scientist” the “Sexiest Job of the 21st Century,” and Bloomberg says undergraduates with data science training are now being hired with salaries greater than investment banking analysts. The recent pandemic not only showed the resilience of tech careers, but also the work-from-home flexibility offered to tech staff, who often just need a laptop and reliable Internet to get the job done. Fortune’s rankings of the “Best Companies to Work For” is full of technology firms and has been topped by tech businesses nearly every year since it started. And everyone wants to hire more coders from underrepresented groups. Apple, Etsy, Square, Meta, and Alphabet are among the firms with programs to prep and encourage more women and minorities to pursue tech careers (details in endnotes).

Students studying technology can leverage skills in ways that range from the highly technical to those that emphasize a tech-centric use of other skills. And why be restricted to just the classes taught on campus? Resources like Coursera, Codecademy, Udemy, edX, YouTube, and others provide a smorgasbord of learning where the smart and motivated can geek up. Carve out some time to give programming a shot—remember, the founders of Tumblr and Instagram were largely self-taught. Your author even offers hundreds of free videos on learning to code iOS apps from scratch, as well as tutorials on building robotics and other hardware products. The high demand for scarce technical talent has also led many tech firms to offer six-figure starting salaries to graduating seniors from top universities. Take some advice from the Harvard Business Review: “Leading a digital transformation? Learn to code.” Opportunities for programmers abound, particularly for those versed in new technologies. But there are also non-programming roles for experts in areas such as user-interface design (who work to make sure systems are easy to use), process design (who leverage technology to make firms more efficient), and strategy (who specialize in technology for competitive advantage). Nearly every large organization has its own information systems department. That group not only ensures that systems get built and keep running but also increasingly takes on strategic roles targeted at proposing solutions for how technology can give the firm a competitive edge. Career paths allow for developing expertise in a particular technology (e.g., business intelligence analyst, database administrator, social media manager), while project management careers leverage skills in taking projects from idea through deployment.

Even in consulting firms, careers range from hard-core programmers who “build stuff” to analysts who do no programming but might work identifying problems and developing a solutions blueprint that is then turned over to another team to code. Careers at tech giants like Apple, Google, and Microsoft don’t all involve coding end-user programs either. Each of these firms has its own client-facing staff that works with customers and partners to implement solutions. Field engineers at these firms may work as part of (often very lucratively compensated) sales teams to show how a given company’s software and services can be used. These engineers often put together prototypes that are then turned over to a client’s in-house staff for further development. An Apple field engineer might show how a firm can leverage iPads in its organization, while a Google field engineer can help a firm incorporate search, banner, and video ads into its online efforts. Careers that involve consulting and field engineering are often particularly attractive for those who are effective communicators who enjoy working with an ever-changing list of clients and problems across various industries and in many different geographies.

Upper-level career opportunities are also increasingly diverse. Consultants can become partners who work with the most senior executives of client firms, helping identify opportunities for those organizations to become more effective. Within a firm, technology specialists can rise to be chief information officer or chief technology officer—positions focused on overseeing a firm’s information systems development and deployment. And many firms are developing so-called C-level specialties in emerging areas with a technology focus, such as chief information security officer (CISO), and chief privacy officer (CPO). Senior technology positions may also be a ticket to the chief executive’s suite. Fortune pointed out how the prominence of technology provides a training ground for executives to learn the breadth and depth of a firm’s operations, and an understanding of the ways in which a firm is vulnerable to attack and where it can leverage opportunities for growth.

Generative AI and Programming, Peter Norvig, Director of Research, Google

No, tech jobs aren’t going away; tech will be baked into even more jobs. Peter Norvig, one of the world’s leading AI scientists, discusses the role of AI in the future of tech jobs, and how most jobs will become tech-enabled.

5.105 to 8.25- So my intro is I was
doing AI before it was cool,
10.11 to 12.99but I'm still here and,
12.99 to 15.06and I'm gonna talk
about programming today.
15.06 to 18.065And you know, I've had a
lot of roles in my career.
18.065 to 23.015I've been a researcher, an
author, a manager, a teacher.
23.015 to 25.59But at my at heart, I'm
really a programmer.
25.59 to 27.12That's what I love to do.
27.12 to 30.24So I wanna give you, I only got 15 minutes
30.24 to 34.17that's less than dj and
I'm not complaining,
35.52 to 37.8but I still wanna squeeze
in a short history
37.8 to 39.15of software engineering.
39.15 to 40.77So this is the Eating Act,
40.77 to 44.37the first general
purpose computer in 1945.
44.37 to 47.34And at that time, programming was kind
47.34 to 48.96of an afterthought, right?
48.96 to 51.36So all that really mattered
was the electrical engineering
51.36 to 54.03stuff and trying to actually
build this machine and,
54.03 to 56.61and programming somebody
else will figure that out.
56.61 to 58.29So that's why they let the women do it.
60.18 to 62.37In 1978 was about when I started
63.54 to 65.67the c programming language was invented,
65.67 to 69which is basically a high level
assembly language targeted
69 to 71.37at the PDP 11 so that
they could write Unix.
72.42 to 75.18By 2014, we kind of flipped things around
75.18 to 77.82and said, you know, maybe
we should stop focusing
77.82 to 80.315so much on making the
hardware more efficient,
80.315 to 82.83and maybe we should focus on
making the programmer more
82.83 to 86.82efficient so we got a
much nicer tool chain
86.82 to 90.48that's maybe not as efficient,
90.48 to 93.93but it makes better use of human effort.
94.89 to 99.06And now in 2023, we have headlines like,
99.06 to 102.45are these coding jobs going
to exist in three years?
102.45 to 105.42So first I wanna say don't panic.
105.42 to 106.71I agree with Eric,
106.71 to 109.47there's not gonna be massive unemployment.
109.47 to 112.59If you are a software
engineer, you're gonna be fine.
112.59 to 115.5And if you're not, you're
gonna be closer to one.
115.5 to 117.48You're going to be doing more, right?
117.48 to 121.83So I, I just had a, a talk
yesterday with data science
121.83 to 126.05who works in nature conservation,
who says, you know, I,
126.05 to 129.36I used to fiddle around
a little bit with data
129.36 to 131.67and I could make a couple plots and so on,
131.67 to 133.86but now I can do so much more.
133.86 to 138.09I can do things that, that are
what a real programmer can do
138.09 to 142.38because I can ask, you know,
can I go in this direction?
142.38 to 143.85Can I explore this data in this way?
143.85 to 147.06And I can do things I never
did before. So that's exciting.
148.11 to 150.96So let's take a, a step back
150.96 to 153.27and look more carefully
at this history, right?
153.27 to 155.07So we went from optimizing hardware
155.07 to 158.25to optimizing the programmer's effort.
158.25 to 161.315And in, in the future or, or, or now.
161.315 to 162.93And going forward, I think it'll be more
162.93 to 164.43assisting the user, right?
164.43 to 167.85So there won't, for a lot
of the tasks, you won't have
167.85 to 170.07to bring in a professional programmer.
170.07 to 172.38The users will be doing it for themselves,
172.38 to 174.93or they'll be modifying existing things.
176.28 to 179.2We went from low level code to high level
179.2 to 181.66- To now it's gonna be more of a dialogue.
181.66 to 184.21We're gonna be using natural language.
184.21 to 186.64And yes, there's gonna
be a programming language
186.64 to 189.97as an intermediary, but that
may not be as important.
189.97 to 192.85Maybe all the important stuff
about a programming project
192.85 to 196.33will be in natural language,
or it'll be in, in diagrams
196.33 to 200.71or a little sketch on the
back of a, of a napkin.
200.71 to 203.44And not necessarily in a
traditional programming language.
204.76 to 206.895In the 1970s, we had simple problems.
206.895 to 211.065So the ENIAC was, was built
to compute firing trajectories
211.065 to 213.94so that artillery could
hit the bad guys no matter
213.94 to 215.98what the weather or wind condition was.
217.69 to 221.02And then by the seventies we could
221.02 to 224.5do things like build
Unix, which was complex in
224.5 to 226.78that it was a whole operating system.
226.78 to 228.73But as you remember, the Lion's book
228.73 to 232.27that was only like a 200 page
book was the whole thing.
232.27 to 234.64Now, by the two thousands
234.64 to 237.52or so, we had operating
systems that were millions
237.52 to 240.64of lines rather than, than,
than just thousands of lines.
241.57 to 244.93And going forward, I think we
want problems that are, are,
244.93 to 246.88are even more complex than that.
246.88 to 251.05These so-called wicked
problems for which there is no
251.05 to 253.81exact specification, at least
with an operating system.
253.81 to 256.575You know, if it's right or
wrong with wicked problems,
256.575 to 258.52you don't even know what
the right answer is.
258.52 to 262.39Everything changes. There is
no complete specification.
262.39 to 267.07So Brian talked about this
character that you could talk to
267.07 to 268.9and he had a great conversation about the
268.9 to 270.85Brandenburg concertos.
270.85 to 273.075Nobody wrote a specification that says one
273.075 to 274.9of the things this guy
has to do is be able
274.9 to 277to talk about the
Brandenburg concertos, right?
277 to 279.97You couldn't have thought
of that ahead of time.
279.97 to 283.185And yet we're now in a
position where we want programs
283.185 to 286.78that will do things that the
designers never thought of.
286.78 to 289.3And that's brand new. We
didn't have that before.
291.82 to 294.4In the beginning it was a, it was ad hoc.
294.4 to 296.83Then we started to say, you know,
296.83 to 299.685maybe this computing stuff
is really a mathematical
299.685 to 301.63or a logical science.
301.63 to 303.73And we had this whole methodology for
303.73 to 305.89how you could prove programs, correct.
305.89 to 307.33Of course, nobody ever did that except
307.33 to 309.31for like the 10 line programs.
309.31 to 312.28But we kind of knew that
in theory we could do that.
312.28 to 314.41And we wrote some tests that convinced us
314.41 to 316.51that we were getting close to a proof.
318.04 to 320.5Nowadays in going forward, we're kind
320.5 to 321.7of stepping away from that.
321.7 to 323.71We're saying it's no longer
a mathematical science.
323.71 to 327.1It's more like a natural
science in several ways.
327.1 to 328.39One is the types of
328.39 to 331.51of problems we're solving
don't have mathematical
331.51 to 332.59specifications.
332.59 to 333.94It's, you know, I wanna be able
333.94 to 336.55to have a good conversation
about every everything,
336.55 to 339.07what's everything, what's
a good conversation?
339.07 to 341.41We can't mathematically define that.
341.41 to 344.32So we kind of define it empirically.
344.32 to 346.6The other way it's in natural
science is that, you know,
346.6 to 349.84when I started you wrote
everything yourself.
349.84 to 353.14The libraries meant you
had square root and sort
353.14 to 354.91and maybe a couple other things.
354.91 to 356.835Right? Now what do you do? Well, you
356.835 to 358.82- Go a bunch of packages
358.82 to 361.46and it's like being David Attenborough
361.46 to 364.25and you know, looking at
a strange animal out there
364.25 to 366.56and saying, ah, I gotta capture this one.
366.56 to 368.69And oh, there's a field manual that says
368.69 to 370.85what this strange animal does,
371.81 to 375.47but half the time the manual's
wrong and you make a call
375.47 to 376.58and it does the wrong thing.
376.58 to 379.55And now as a natural
scientist, you have to come up
379.55 to 381.74with your theory for this is
381.74 to 383.87what this program does
when I make this call
383.87 to 385.7to this API, right?
385.7 to 388.37So it's not a mathematical
proof that it's, it's correct,
388.37 to 389.6it's trial and error.
389.6 to 392.18I ran this, it didn't work.
I tried using it another way.
392.18 to 394.49Okay, now it works. I'm
not gonna worry about
394.49 to 395.54proving it correct.
395.54 to 399.14I'm just gonna go ahead. Used
399.14 to 402.02to be you were completely
on your own in programming.
402.02 to 405.92Then we came up with a
pretty nice set of tools
405.92 to 409.94for doing version control,
doing testing and so on.
409.94 to 412.4And, and we had that.
412.4 to 415.22But there was kind of a
separation of here's the program
415.22 to 418.7that you run and then here's
all the tool set around it.
418.7 to 420.23And those are two separate things.
420.23 to 421.315And I think
421.315 to 424.19and hope that in the future
we'll bring those all together
424.19 to 426.92and we'll talk about that a
little bit more in a minute.
428.48 to 430.88Okay, so how is this used today?
430.88 to 433.94And I wanna distinguish
basically two types of use.
433.94 to 437.09One is essentially API lookup
437.09 to 439.73or a smart manual
439.73 to 444.145or auto complete is like
I'm programming along and,
444.145 to 447.35and I forget how to do this
one thing and I type something
447.35 to 448.76and then it auto completes it for me
448.76 to 449.81and I say, yeah, that's right,
449.81 to 451.76or no, that's not quite
right and I fix it.
451.76 to 454.04And then the second is
complete problem solving
454.04 to 456.08where you just say,
here's a natural language
456.08 to 457.52description of the problem.
457.52 to 458.66Solve the whole thing for me
458.66 to 460.34and I'll give you one example of each.
461.66 to 465.02So here is an example
of building a video game
466.31 to 468.83through natural language
saying, well, okay,
468.83 to 471.92when the rockets clicked
display some texts saying firing
471.92 to 476.69thrusters and temporarily
speed up by four times
476.69 to 478.7for a quarter of a second.
478.7 to 481.52Now this is something
that somebody, you know,
481.52 to 486.14a programmer in an introductory
class could build this app,
486.14 to 489.56this little game, but they
would have to look up things,
489.56 to 494.56they'd have to know, you
know, my exposition that's
494.72 to 498.47specified by offset
left in this particular
498.47 to 499.58JavaScript framework.
500.84 to 503.12And once you know that
you have the answer,
503.12 to 505.13but it's easier to just
say it in natural language
505.13 to 508.19and similarly, temporarily speed up, well
508.19 to 511.76that means calling the set
timer on a function and so on.
512.63 to 515.24So it's not doing anything that
the programmer couldn't do,
515.24 to 518.51but it's just making it
faster by not having to know
518.51 to 523.19what the API is At
Google, we have this joke
523.19 to 526.97that most of what the software
engineers do is take data in
526.97 to 529.91one format, transform
it into another format
529.91 to 531.895and ship it off to another program.
531.895 to 535.145So that's Larry and Sergei's,
proto buff moving company.
535.145 to 538.74T-shirt is popular. Okay?
538.74 to 539.905The second type of
539.905 to 544.59of thing is we take a problem
description here from a,
544.59 to 547.92a programming contest and it's
mostly in natural language.
547.92 to 549.75There's a little bit of formalization
549.75 to 553.59of here's a sample input
and a sample output.
553.59 to 556.53And we say, give me the whole solution.
556.53 to 561.09And so this was the main
example from Alpha code from,
561.09 to 562.26geez, it seems ancient now.
562.26 to 563.46It was probably like a year ago
564.6 to 567.66and here's the solution
that it comes up with
567.66 to 569.25and this is correct.
569.25 to 570.6It gets the right answer
571.59 to 574.8and kudos for that.
575.7 to 577.17And I saw this and I said, yeah, okay,
577.17 to 578.25it's the right answer,
578.25 to 579.725but there's more
579.725 to 582.09to programming than just
getting the right answer.
582.09 to 585.15So I said, you know, if if
I was doing the code review
585.15 to 587.13for this, what would I say?
587.13 to 589.5And it turns out there's a
lot of markup going on here
589.5 to 592.56for things that are,
that aren't quite right
592.56 to 594.57that I would not let pass.
594.57 to 597.39One of the interesting things
there in the middle, this has
597.39 to 600.06to do with popping things off of a stack
600.06 to 602.435and it has the two stacks, A
602.435 to 603.96and B that makes a lot of sense.
603.96 to 606.27And then it invents this stack called C
606.27 to 611.1and every time it pops something
off of B, it saves it on C
611.94 to 614.58and then it never uses C anywhere else.
614.58 to 618.36And so what's happening is
this system is trained on
619.35 to 621.99on lots of programs, some
of them manipulate stacks.
621.99 to 624.69And a lot of times when you
manipulate a stack, a good thing
624.69 to 626.85to do is to save it somewhere.
626.85 to 628.23So it had that idea
628.23 to 631.17and then didn't realize it
didn't really need that idea.
631.17 to 634.44And a lot of programmers do
that and I don't wanna hire them
637.62 to 639.96or maybe I wanna train
them to, to do better.
641.31 to 643.38So here's a better answer.
643.38 to 646.38And, and Ben mentioned the, the blog post,
646.38 to 648.81this PI twos notebook
where I talk about that.
649.71 to 652.565But essentially what's happening here is
652.565 to 653.61it's getting things wrong.
653.61 to 655.92And I think the problem is
half the programmers are
655.92 to 658.05below average and they're
all in the training data.
660.03 to 661.2So you shouldn't expect the
661.2 to 662.58output to be better than average.
662.58 to 665.22And maybe we can fine
tune it to do better, but,
665.22 to 666.78but that's a fundamental problem.
666.78 to 667.95But I think we can do better.
668.91 to 673.08So how can we do better and
how can we do this tomorrow?
673.08 to 674.4So I mentioned
674.4 to 678.54that we have this whole software
development life cycle in
678.54 to 680.31which many things are going on.
680.31 to 682.56And program development
is just number three.
682.56 to 685.32There is just one of the many parts.
685.32 to 688.77And, and I've said some ways in which
688.77 to 691.89generative AI could be used
at each point in the cycle.
693.27 to 696.87And today, at each point in
those cycles, we have a team
696.87 to 699.57of people and they create a
bunch of documentation, some
699.57 to 702.24of it just written documentation, some
702.24 to 705.09of it formal things like tests and so on.
706.11 to 708.24But we really focus on this one part,
708.24 to 709.62the software development part.
709.62 to 714.62And that's a mix of, of code
in in or JavaScript or whatever
714.91 to 717.37and neural networks.
717.37 to 720.13And one of the amazing things is we can do
720.13 to 721.66this back propagation.
721.66 to 724.78So if there's an error
in the neural network,
724.78 to 727.66we show it more examples
and it gets better.
727.66 to 730.06And that's not true anywhere else.
730.06 to 732.25But what I would like to see is
732.25 to 735.34what if it was true everywhere else?
735.34 to 738.765What if we could take
everything associated
738.765 to 740.35with the software development process,
740.35 to 744.04all this informal stuff and
back propagate through that?
744.94 to 747.79And I've seen examples of
this all the time, right?
747.79 to 752.17So we start a new project, we
get the user experience people
752.17 to 755.62to say, you know, what's the
interface gonna look like?
755.62 to 757.75And they come up with 10 different ideas
757.75 to 760.96and they try them out and
they make these prototypes
760.96 to 762.67and they do these paper models
762.67 to 764.74and they do these Wizard of Oz experiments
764.74 to 767.2and they bring people into
the room with the mirror
767.2 to 769.72behind it and they examine
them using the interface
769.72 to 772.36and at the end they write this report
772.36 to 774.97and says, here's the best interface.
774.97 to 776.44Hand it off to the engineers
776.44 to 777.94and the engineers implement that.
778.78 to 781.84And then, you know, they
read the report, they say,
781.84 to 784.93this is an awesome report,
you guys did a great job.
784.93 to 786.31And then the report goes on the shelf
786.31 to 788.2and nobody ever looks at it again.
788.2 to 791.05'cause it's, it's not
considered part of the program.
791.05 to 793.66The program is the code that was built
794.83 to 798.04and they build it, it represents the,
798.04 to 800.74the interface faithfully,
everybody's happy.
800.74 to 804.49But over time the world
inevitably changes.
804.49 to 807.61Maybe people are using devices
807.61 to 810.94with a different size screen
than they were using before.
810.94 to 815.11And so the choices that the
user experience team made
815.11 to 817.42that were based on assumptions,
817.42 to 819.37those assumptions are no longer valid
820.21 to 822.22and people are kind of nervous
822.22 to 824.23and saying, you know, things
are getting a little bit worse,
824.23 to 826.21but I'm not sure why.
826.21 to 830.77Now if we could have
all that documentation
830.77 to 833.08as a formal part of the system
833.08 to 837.94and differentiate through it,
now we could say this is why
837.94 to 839.44we're having this problem.
839.44 to 841It's, we made this assumption,
841 to 843.22this assumption is no longer true.
843.22 to 846.88And maybe the system could
automatically correct itself
846.88 to 849.55or maybe it would just
have an alert to say,
849.55 to 851.74here's why we're having problems, here's
851.74 to 853.15where the difficulty is,
853.15 to 856let's do a better job in fixing that.
856 to 858.1So that's where I think the future
858.1 to 860.29of software is going to be.
860.29 to 862.33I think we can build systems like that.
862.33 to 865I think we have everything
we need to do it,
866.62 to 869.32and I think it'll be an exciting time
869.32 to 871.27for everyone involved, right?
871.27 to 875.2It will mean professional
programmers can do a better job
875.2 to 878.38and, and have more pride in their work.
878.38 to 881.74And means amateur
programmers like my friend,
881.74 to 883.815the data scientists can do more
883.815 to 885.37and build things that are bigger
885.37 to 888.07and more ambitious than
they ever could before.
888.07 to 892.07And even non-programmers
can get into the act, right?
892.07 to 895.1So I think about the, the
small enterprises, right?
895.1 to 898.7For, you know, we got a lot
of enterprise vendors here
898.7 to 899.87and they do awesome stuff
899.87 to 901.46and you heard some about it, you'll,
901.46 to 903.35you'll hear some more later on.
903.35 to 904.855But they're sort
904.855 to 908.78of focused more on the larger
size companies in which you
908.78 to 909.95can make an investment
909.95 to 914.78and amortize it over hundreds
of users within your company
914.78 to 916.73that are gonna use this system.
916.73 to 919.4What if you only had two
users within your company?
919.4 to 922.79You can't really amortize
bringing in a programmer
922.79 to 925.37to create something for those two users,
925.37 to 927.23but maybe with a system like this,
927.23 to 929.21they could build it themselves.
929.21 to 930.8And that's where I think the future is.
930.8 to 933.14And it's an exciting time
and I can't wait to see it.
933.14 to 936.35And now I'm no longer standing
between you and the break.
936.35 to 936.98So thank you.

Your Future

With tech at the center of so much change, realize that you may very well be preparing for careers that don’t yet exist. But by studying the intersection of business and technology today, you develop a base to build upon and critical thinking skills that will help you evaluate new, emerging technologies. Think you can afford to wait on tech study, and then quickly get up to speed at a later date? Whom do you expect to have an easier time adapting and leveraging a technology like social media—today’s college students who are immersed in technology or their parents who are embarrassingly dipping their toes into the waters of TikTok? Those who put off an understanding of technology risk being left in the dust.

Consider the nontechnologists who tried to enter the technology space in the early part of this century’s tech growth. News Corp head Rupert Murdoch piloted his firm to the purchase of MySpace only to see this one-time leader lose share to rivals. Former Warner executive Terry Semel presided over Yahoo!’s malaise as Google blasted past it. Barry Diller, the man widely credited with creating the Fox Network, led InterActive Corp (IAC) in the acquisition of a slew of tech firms ranging from Expedia to Ask.com, only to break the empire up as it foundered. And Time Warner head Jerry Levin presided over the acquisition of AOL, executing what many consider to be one of the most disastrous mergers in U.S. business history. Contrast these guys against the technology-centric successes of Mark Zuckerberg (Facebook), Steve Jobs (Apple), and Sergey Brin and Larry Page (Google).

While we’ll make it abundantly clear that a focus solely on technology is a recipe for disaster, a business perspective that lacks an appreciation for tech’s role is also likely to be doomed. At this point in history, technology and business are inexorably linked, and those not trained to evaluate and make decisions in this ever-shifting space risk irrelevance, marginalization, and failure.

Key Takeaways

  1. As technology becomes cheaper and more powerful, it pervades more industries and is becoming increasingly baked into what were once nontech functional areas.

  2. Technology is impacting every major business discipline, including finance, accounting, marketing, operations, human resources, and the law.

  3. Tech jobs rank among the best and highest-growth positions, and tech firms rank among the best and highest-paying firms to work for.

  4. Information systems (IS) jobs are profoundly diverse, ranging from those that require heavy programming skills to those that are focused on design, process, project management, privacy, and strategy.

Questions and Exercises

  1. Look at Fortune’s “Best Companies to Work For” list. How many of these firms are technology firms? Which firm would you like to work for? Is it represented on this list?

  2. Look at Bloomberg Businessweek’s “Best Places to Start Your Career” list. Is the firm you mentioned above also on this list?

  3. What are you considering studying? What are your short-term and long-term job goals? What role will technology play in that career path? What should you be doing to ensure that you have the skills needed to compete?

  4. Which jobs that exist today likely won’t exist at the start of the next decade? Based on your best guess on how technology will develop, can you think of jobs and skill sets that will likely emerge as critical five and ten years from now?

  5. Explore online resources to learn technology on your own and search for programs that encourage college students. If you are from an underrepresented group in technology (i.e., a woman or minority), search for programs that provide learning and opportunity for those seeking tech careers. Share your resources with your professor via a class wiki or other mechanism to create a common resource everyone can use to #geekup. Then tweet what you create using that hashtag!