The In-Person Economy: AI, Work and Real Estate in Jacksonville

Executive summary

Artificial intelligence is changing which work can be done from anywhere, which work can be done by software, and which work still needs a person in a specific place. We think that last group will anchor where households live over the next decade. This paper explains why, what we buy because of it, and what would prove us wrong.

• AI reaches screen work first. In the 2024 Science version of Eloundou and co-authors, just over 46% of jobs could have at least half their tasks affected once complementary software is counted [1]. Early payroll data show falling employment for young workers in the most exposed occupations, though the authors call this descriptive, not causal [2].
• In-person work is the durable core. Healthcare and social assistance is projected to add about 1,982,700 jobs from 2024 to 2034, the largest gain of any sector [3]. Nursing, skilled trades, ports, defense and construction all require a body on site.
• Where work goes, housing follows. Remote work explains a large share of the pandemic-era rise in U.S. house prices and rents [4]. We believe the reverse also holds: as more durable jobs are location-bound, households will cluster within commuting distance of the employers that need them.
• Jacksonville fits the profile. A metro of 1,785,500 people that is still growing [5], a port whose cargo supports more than 258,800 Florida jobs [6], a deep Navy presence, major health systems, a manufacturing pipeline and no state personal income tax [7].
• Income support is upside, not underwriting. Broad cash support becomes more plausible as displacement grows, and it stretches furthest where housing is affordable. We model it at zero and buy on today’s rents and today’s employers.
• The risks are real and named. AI’s macro effects may be modest [8], HUD calls the local rental market soft because of new supply [9], industrial vacancy rose on speculative buildings [10], and coastal Florida carries insurance and sea-level risk [11].

Section 1

What AI is automating first

The current wave of AI is a screen-and-keyboard technology. It reads, writes, summarizes, classifies and codes. So the first exposure is easy to predict: jobs whose output is text, numbers or code delivered through a computer.

The research agrees on the direction, even where it disagrees on the size.

How much work is exposed

Eloundou, Manning, Mishkin and Rock mapped large language model capabilities onto occupational tasks. In the 2024 Science version, about 1.8% of jobs could have over half their tasks affected by LLMs used through simple interfaces. Once likely complementary software is accounted for, that share rises to just over 46% of jobs [1]. Higher-wage occupations show higher exposure [1]. The 2023 preprint used different thresholds and reported different headline figures, so we cite the published version.

Felten, Raj and Seamans built an AI Occupational Exposure index from O*NET abilities. The most exposed industries include financial services, accounting, insurance and legal services, and accountants sit at the 99th percentile of exposure [12].

The International Monetary Fund estimates that almost 40% of global employment is exposed to AI, and about 60% of jobs in advanced economies, because those economies lean on cognitive-task-oriented work. Roughly half of the exposed jobs in advanced economies may benefit, because AI complements the work rather than substituting for it [13]. Goldman Sachs economists, in a client note reported by CNBC, estimated that roughly two-thirds of current U.S. and European jobs are exposed to some degree of AI automation, and that generative AI could expose the equivalent of 300 million full-time jobs worldwide [14].

What people actually use it for

Usage data tell the same story. In Anthropic’s first Economic Index report, 37.2% of conversations with its Claude model were about computer and mathematical tasks, mostly software. Farming, fishing and forestry accounted for 0.1% [15]. About 36% of occupations used AI for at least a quarter of their tasks, but only about 4% used it for at least three-quarters [15].

That balance is shifting toward delegation. Anthropic’s September 2025 report was the first in which automation usage exceeded augmentation, and 77% of enterprise API transcripts showed automation patterns [16]. It also cites Census survey data showing firm-level AI use rising from 3.7% in fall 2023 to 9.7% in early August 2025 [16].

Early labor-market signals

Where AI has been deployed carefully, it raises output. Brynjolfsson, Li and Raymond studied a staggered rollout of an AI assistant to 5,172 customer-support agents. The NBER working-paper version reported a 14% average productivity gain and 34% for novice and lower-skilled agents. The published Quarterly Journal of Economics version reports 15% on average, with the largest gains for the least experienced [17].

Higher productivity per worker can also mean fewer workers hired. Using ADP payroll data, Brynjolfsson, Chandar and Chen found that early-career workers aged 22 to 25 in the most AI-exposed occupations saw a 13% relative employment decline in the August 2025 version of their paper, and 16% in the November 2025 version [2]. Software developers and customer-service workers show the clearest declines [2].

These are important caveats. The authors describe their findings as early descriptive indicators, not causal estimates. Their later notes stress that causality cannot be confirmed and that the effects are larger in ADP data than in national surveys [2]. We read the paper as a signal worth watching, not as proof.

A simple map of exposure

We sort work into four groups by two questions: is the work done on a screen or in the physical world, and is it routine or does it require judgment?

Some jobs split down the middle. Industrial automation is a good example: AI can increasingly write the controller code, but someone still pulls the wire, mounts the rack, and commissions the system on site. We expect the programming half of those jobs to shrink and the installing half to hold.

AI also creates in-person work of its own. Data centers, transmission lines and port expansions need electricians, linemen, crane operators and construction crews. We count this as a tailwind for the trades, not as a Jacksonville-specific bet. JEA, the local utility, has received inquiries for large data centers but none are under evaluation, and it says it lacks capacity for a hyperscale facility without a new substation [18].

Section 2

Why in-person work is durable

The economics of automation have a long record, and it points one way. David Autor’s review of workplace automation argues that automation substitutes for some labor but also complements it, raising output and the demand for labor, and that commentators tend to overstate substitution and ignore the complements. The tasks hardest to automate are those that require flexibility, judgment, physical adaptability and in-person interaction [19].

The last big wave of office technology shows what happens next. Autor and Dorn documented the growth of low-skill service occupations from 1980 to 2005. Local labor markets that specialized in routine work adopted computers more heavily, and their workers moved into in-person service jobs [20]. When software took over routine desk tasks, work shifted toward jobs done face to face.

We expect a similar pattern now, with one difference. This time the exposed work reaches further up the skill ladder, into analysis, drafting and code. The in-person side of the economy, meanwhile, keeps growing for reasons unrelated to AI.

The Bureau of Labor Statistics projects that healthcare and social assistance will be the fastest-growing sector and add the most jobs from 2024 to 2034: about 1,982,700 positions, or 8.4%, against 5.2 million jobs (3.1%) economy-wide. Healthcare support is the fastest-growing occupational group, at 12.4%. BLS ties this to an aging population [3].

None of this means in-person jobs are untouched. AI will speed up a nurse’s charting, an electrician’s estimate, and a shipyard planner’s schedule. That is augmentation. The point for real estate is simpler: the job still needs a person in a particular building on a particular shift, and that person needs a home within a reasonable commute.

Section 3

What it means for where people live

We have a recent natural experiment in how work location moves housing markets: the shift to remote work.

The remote-work evidence

Barrero, Bloom and Davis projected that 20% of full workdays would be supplied from home after the pandemic, compared with about 5% before [21]. That shift moved real estate values.

Mondragon and Wieland estimate that each additional percentage point of remote work raised house prices by about 0.93% (0.92% in a later version). In the 2022 version, the shift to remote work explains over half of the 23.8% national house price increase; a later version attributes over half of the 18.9% rise in real house prices from 2019 to 2023 to remote work. Effects on rents were almost identical to effects on prices [4].

Commercial property felt it too. In the NBER working-paper version of their study, Gupta, Mittal and Van Nieuwerburgh estimate a 45% decline in New York City office values in 2020 and 39% in the longer run [22]. The figures differ across versions of the paper, but the direction is consistent.

Running it in reverse

Remote work untethered screen workers from their offices, and housing demand followed them. AI acts on the same group of workers from another direction. If AI compresses screen-based work, a larger share of durable jobs will be location-bound: the nurse, the electrician, the welder at the shipyard, the technician at the plant.

Those workers cannot live anywhere. They live within a commute of the hospital, the terminal, the base and the plant. We believe that favors two things: metros where in-person employers are large and growing, and housing within easy reach of those employers.

An honest limit

The pandemic evidence also shows that most moves were local. Ramani and Bloom found that households, businesses and real estate demand shifted from dense city centers to lower-density suburbs within the same metro, with little reallocation across cities. The effect was strongest in the 12 largest cities, small in cities ranked 13 to 50, and absent in cities ranked 51 to 365 [23].

So we do not assume a wave of migration into Jacksonville driven by AI. Our thesis works at the metro level and at the neighborhood level. Within the metro, proximity to in-person employers matters more. Any inflow from other metros is a tailwind, not a requirement.

Why affordable metros

Glaeser and Gyourko compare home prices with the minimum profitable cost of building a new home. More than three-quarters of homeowners across 98 metros live in homes valued around or below that cost. Most of the country, in other words, is not supply-constrained the way San Francisco is [24]. In elastic markets, new supply responds to demand. That caps rent spikes, but it also makes markets easier to underwrite.

Affordable markets have also produced steadier returns. Demers and Eisfeldt find that single-family rental total returns across the 30 largest metros from 1986 to 2014 roughly equalized at about 8.5%. Low-priced cities returned about 9.2% versus 7.9% for high-priced cities, and they earned more of that return from net rental yield, which is less volatile than price appreciation [25]. Over a much longer horizon, Jordà and co-authors find that residential real estate across 16 advanced economies earned real total returns of about 7% a year from 1870 to 2015, similar to equities, with a standard deviation of about 10% against roughly 22% for equities [26].

We prefer income we can see over appreciation we have to hope for. Affordable, growing metros give us more of the former.

Section 4

Income support as upside, not underwriting

If AI displaces a meaningful share of screen-based work, we believe pressure for broad income support, whether called universal basic income or guaranteed income, will grow. We take the possibility seriously. We also refuse to pay for it.

What the evidence shows

The best evidence on cash transfers and work comes from a few programs and trials.

• Alaska Permanent Fund. Jones and Marinescu find that Alaska’s universal annual dividend had no effect on employment and increased part-time work by 1.8 percentage points (17%). The pattern is consistent with cash supporting local, in-person demand [27].
• OpenResearch two-state trial. About 1,000 low-income recipients received $1,000 a month for three years. In the July 2024 version of the paper, income excluding transfers fell by about $1,500 a year relative to the control group, labor-market participation fell 2.0 percentage points, and hours fell by 1.3 to 1.4 a week. Later revisions report larger effects [28].
• Stockton, California (SEED). 125 residents received $500 a month, unconditionally, for 24 months. Preliminary first-year results showed full-time employment among recipients rising from 28% to 40%, versus 32% to 37% for the control group [29]. The sample was small and the results were preliminary and pre-pandemic. The peer-reviewed write-up emphasizes lower income volatility and better health and agency, with effects much less pronounced during the pandemic [30].

The honest reading is that cash support produces modest reductions in paid work in some designs and no aggregate employment effect in others. It is not a clean story in either direction.

Why it would matter for real estate

A fixed-dollar transfer buys more housing where housing costs less. If a national program arrives, we believe it would support rent-paying capacity most in affordable metros, and Florida levies no state personal income tax on individuals [7].

Why we give it zero weight

No national program exists. Its size, design, funding and timing are unknown, and the politics are uncertain. The labor-supply evidence is mixed. So every deal we make has to work without it. If broad income support arrives, our residents and our investors benefit. If it never does, nothing in our underwriting changes.

Section 5

Why Jacksonville

We want a metro where in-person employers are large and growing, households are arriving, and housing is still affordable. Jacksonville checks each box, with caveats we cover in Section 7.

Population growth

The Jacksonville metro had 1,785,500 residents on July 1, 2025, up from 1,759,347 a year earlier in the Census Bureau’s Vintage 2025 estimates. By our calculation that is a gain of about 26,150 people, or about 1.5%, in one year, and roughly 11% since 2020 [5].

The City of Jacksonville passed one million residents, reaching 1,009,833 on July 1, 2024 after adding about 16,365 people in a year. That ranked 10th among U.S. cities for numeric increase [33]. St. Johns County, to the south, reached 346,328 in 2025, up 26.7% since April 2020 [34]. A Florida State University study found it grew 60.4% from 2010 to 2022, the fastest of any Florida county [35].

Growth is slowing, though. Census attributes the 2025 slowdown in many large counties to lower net international migration, and St. Johns County’s 2025 gain was its smallest since 2020 [5, 34].

The port

JAXPORT handled 1,388,841 TEUs and 506,237 vehicle units in fiscal year 2025 [36]. That is not a container record; fiscal 2021 was higher, at 1,407,310 TEUs [36]. The port’s economic-impact study found that cargo activity supported more than 258,800 jobs in Florida and $44 billion in annual economic output in 2024 [6]. Longshoremen, truckers, mechanics, crane operators and warehouse crews do that work in person.

The Navy and defense

The region hosts four major military installations [31]. In Duval County, the state’s 2024 defense economic-impact summary, as presented by the City of Jacksonville, puts defense-related gross regional product at about $9.1 billion, roughly 17% of the local economy, and 68,060 jobs [31]. Sailors, ship-repair crews, aviation maintainers and their families need housing near the bases.

Healthcare

Baptist Health reported more than 15,350 team members in its FY2024 fact sheet [32]. Mayo Clinic’s Jacksonville campus had about 9,000 employees as of 2023 reporting. Mayo is adding five floors to its hospital in a $432 million project due for completion by the end of 2026, and it announced a 210-acre North Campus in 2023 [37]. Along with UF Health Jacksonville and others, these employers anchor demand for nurses, technicians and support staff, many of whom rent.

Manufacturing and logistics pipeline

JAXUSA Partnership, the regional economic-development group, counted 12 project announcements in 2025 with more than 2,400 new jobs and nearly $1 billion in capital investment [38]. They include Otto Aerospace (more than 1,200 advanced-manufacturing jobs), a Sam’s Club distribution center (250 jobs), BAE Systems with Phoenix Products (320 jobs), and Sysco with Eckart Supply (145 logistics jobs) [38].

Johnson & Johnson announced more than $1 billion to expand ACUVUE contact-lens manufacturing, packaging and a new distribution facility near Jacksonville International Airport. The roughly 3,500 people often cited are existing local employees, not new hires; we read the investment as securing those jobs rather than adding many more [39]. Holon and Benteler announced a planned plant of about 500,000 square feet with 800 or more jobs [40]. We treat that as announced, not built.

Taxes and price

Florida does not impose a personal income tax on individuals [7]. That helps the take-home pay of every worker in the metro, and it helps recruiting.

Among the large Florida metros with available Zillow data, Jacksonville is the least expensive, though the comparisons mix dates and geographies [41]. That gives us room to buy at prices where rental income does most of the work.

Section 6

What we buy and why

We buy two kinds of real estate: homes for the people who do in-person work, and specialized buildings where that work happens. Every acquisition has to make sense on current rents from current employers.

Residential: why workforce housing

Florida’s statewide rental study finds a large and growing number of low-income renter households are cost-burdened [42]. Demand for decent, reasonably priced rentals near work is not the constraint. Price discipline is.

Competition shapes where we play. Among six metros the GAO studied, Jacksonville had the highest institutional-investor share of single-family rentals, at 22%, and investor-owned single-family rentals there roughly doubled between 2020 and 2024 [43]. We focus where large institutions are less active: small multifamily, scattered-site homes that need hands-on work, and furnished mid-term units. We found no published research specific to mid-term medical-staff rentals in Jacksonville, so we underwrite those units against long-term rents and treat any furnished premium as upside.

Commercial: why small and specialized

Small industrial buildings have held up better than large ones. Cushman & Wakefield data show shallow-bay multitenant warehouses under 150,000 square feet ended 2024 at 4.8% vacancy, against 10.8% for big-box warehouses of 250,000 square feet or more [44]. Trades and contractors need a shop, a bay door and a yard close to their jobs. They cannot do that work from a laptop.

Medical outpatient space has also been resilient. JLL reports record medical outpatient building occupancy of 92.7% in the fourth quarter of 2025, with rent growth of 3.3% year over year. Avison Young puts medical outpatient occupancy at 92.3% against 80.3% for U.S. office in the same quarter [45].

Public markets pointed the same way in 2025. Health care REITs returned 28.5% and industrial REITs 17.0%, against 2.3% for all equity REITs [46]. Health care REIT returns were driven heavily by senior housing, not only medical outpatient, so we read this as directional support rather than proof [46].

What we avoid

Commodity office, large speculative warehouses, and properties whose value depends on a story rather than a tenant. We do not count on special zoning designations to lift values: Chen, Glaeser and Wessel find that Opportunity Zone status had little measurable effect on home prices, ruling out effects larger than about half a percentage point [47].

Section 7

Risks and what would prove us wrong

An investment thesis is only useful if it can fail. Here is where ours could.

What would change our mind

• Robotics begins to automate physical-judgment work, such as nursing, field service or ship repair, at scale and at a reasonable cost.
• In-person employment in Northeast Florida stops growing for several years, or a major employer we depend on leaves.
• Rents in our target submarkets fall below our underwriting and stay there after new supply is absorbed.
• Insurance and flood costs rise faster than rents in ways we cannot price or mitigate.

We will report on these signals to investors, plainly and on a schedule.

Section 8

How we underwrite

Our edge is not a forecast. It is a process that lets a small team look at far more deals than a traditional shop, pass on almost all of them, and act quickly on the few that work.

1. Source. Listings, off-market leads, tax records and employer announcements, tracked continuously. We review nearly every relevant listing in our target submarkets.
2. Screen. Rent comparables, insurance and flood risk, taxes, building-code vintage and commute time to major in-person employers.
3. Underwrite. A first-pass model in minutes, stress-tested on interest rates, vacancy, concessions and insurance.
4. Diligence. Lease, title and inspection review, drafted by AI and checked by people.
5. Decide. A principal tours every property, negotiates and approves every acquisition.
6. Operate. Rent and expense monitoring, maintenance triage and regular investor reporting.

The rules we hold to

• Today’s rents, today’s employers. No premium for AI-driven migration, income support, or announced projects that are not yet built.
• Income first. We look for deals where net rental yield carries the return, consistent with the evidence that cheaper markets earn more of their return from yield [25].
• Supply-aware. We track deliveries by submarket and underwrite current concessions, not hoped-for burn-off.
• Risk priced in. Insurance, flood, wind and code vintage are line items, not footnotes.
• Lean by design. AI handles the repetitive analyst work, so overhead stays low and more of the return stays with investors. People make every decision.

AI is changing who can work from where. We think the people who build, repair, heal, ship and defend will keep showing up in person, and they will need good places to live and work near the jobs. That is what we buy, in Jacksonville, at prices that work today.

References

Figures for working papers are cited to the version named in the text. Some sources were verified through published summaries and coverage rather than the original document; those entries say so.

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2. 2.Brynjolfsson, E., Chandar, B. & Chen, R. (2025). Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence. Stanford Digital Economy Lab working paper (August 2025; revised November 2025; further update August 2026). Figures differ by version; the version is named in the text. digitaleconomy.stanford.edu
3. 3.U.S. Bureau of Labor Statistics (2025, August 28). Employment Projections — 2024–2034 (news release). bls.gov
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This white paper is for informational purposes only and is not an offer to sell or a solicitation of an offer to buy any security. Any offering will be made only through definitive offering documents to eligible investors. Real estate investing involves risk, including loss of principal. Views on AI, labor markets, public policy including universal basic income, and real estate markets are forward-looking opinions, not guarantees. Employer names describe the local economy only and imply no relationship or endorsement.