The same jobs. A different AI exposure ranking.

THE PUBLIC RECORD / PALORENFIELD NOTES / 1025 OCTOBER 2026

Work / United States

23.2% of matched US jobs are in occupations with high task exposure. The rating is useful for showing where work could change, but the local rank moves with the score and says nothing on its own about layoffs.

Read the analysis ↓Find your place ↗

The national finding / OEWS-0123.2%

of matched US jobs in highly exposed occupations

Approximate 95% interval: 23.1% to 23.2%. Matched employment: n = 143,751,120.

May 2025 employment / 2023 exposure scores

READ THIS WITH THE NUMBER

Exposure means tasks an LLM could speed up. It isn't a job-loss forecast. We don't measure actual AI use or count people who will be replaced.

01 / The story in the record

Analysis by
Paloren Research

Inspect the evidence ↓

What we set out to find.

We matched 143,751,120 US jobs to occupation-level AI exposure scores. Where could tasks change most? To check whether the answer rests on the score we chose, we ranked 393 reportable metros twice using the same jobs data.

What we found

We classified 23.2% of matched jobs as highly exposed, with an approximate 95% interval of 23.1% to 23.2%. Then the local order moved. Boulder rises under the second score; Olympia falls. No jobs have changed between those calculations.

How we did it, and what it cannot tell us ↗

Find your place.
Take the evidence.

03 / The local record

We've made 393 reportable metro areas searchable. Start with a name. We keep the count and caveat beside each finding so you can check what you're quoting. A missing place isn't a zero.

GeographyFind a place Order

393 reportable locations

Metro areas

Metro areas: reportable rows only. Highly exposed / matched jobs (%).
Rank	Place	Highly exposed / matched jobs (%)	Matched jobs (n)	Approximate 95% interval
1	Lexington Park, MD	32.3%	61,610	31.6 to 33
2	Olympia-Lacey-Tumwater, WA	32%	115,100	31.6 to 32.4
3	Durham-Chapel Hill, NC	31.9%	282,010	31.5 to 32.4
4	Trenton-Princeton, NJ	31.8%	205,330	31.5 to 32.2
5	Boulder, CO	31.7%	170,070	31 to 32.5
6	Tallahassee, FL	31.3%	165,850	30.9 to 31.7
7	Washington-Arlington-Alexandria, DC-VA-MD-WV	29.4%	2,916,220	29.1 to 29.7
8	San Jose-Sunnyvale-Santa Clara, CA	28.8%	1,016,950	28.5 to 29.2
9	Raleigh-Cary, NC	28.8%	694,300	28.2 to 29.3
10	Austin-Round Rock-San Marcos, TX	28.6%	1,203,840	28.2 to 29
11	Denver-Aurora-Centennial, CO	28.4%	1,479,590	27.7 to 29
12	Lansing-East Lansing, MI	28.1%	190,530	27.8 to 28.4
13	Huntsville, AL	28%	235,090	27.4 to 28.6
14	Seattle-Tacoma-Bellevue, WA	27.9%	1,923,730	27.2 to 28.6
15	Helena, MT	27.9%	36,300	27.3 to 28.5
16	San Francisco-Oakland-Fremont, CA	27.6%	2,110,070	27.3 to 28
17	Salt Lake City-Murray, UT	27.4%	750,850	27 to 27.9
18	Boston-Cambridge-Newton, MA-NH	27.3%	2,477,050	27 to 27.7
19	Des Moines-West Des Moines, IA	27%	371,400	26.5 to 27.5
20	Albany-Schenectady-Troy, NY	26.6%	409,630	26.3 to 26.9
21	Carson City, NV	26.4%	24,410	25.8 to 27
22	Madison, WI	26.4%	372,310	26 to 26.8
23	New York-Newark-Jersey City, NY-NJ	26.3%	8,428,030	26.1 to 26.6
24	Atlanta-Sandy Springs-Roswell, GA	26.3%	2,673,490	25.8 to 26.7
25	Springfield, IL	26.2%	88,470	25.9 to 26.5
26	Minneapolis-St. Paul-Bloomington, MN-WI	26.1%	1,731,770	25.8 to 26.5
27	Charlotte-Concord-Gastonia, NC-SC	26.1%	1,275,830	25.8 to 26.4
28	Tampa-St. Petersburg-Clearwater, FL	26%	1,351,550	25.6 to 26.4
29	Ithaca, NY	26%	37,240	25.6 to 26.3
30	Warner Robins, GA	25.9%	63,630	25.5 to 26.3
31	San Juan-Bayamon-Caguas, PR	25.7%	627,240	25.1 to 26.2
32	Baltimore-Columbia-Towson, MD	25.6%	1,226,870	25.1 to 26
33	Charlottesville, VA	25.5%	91,620	25.1 to 25.9
34	Jefferson City, MO	25.4%	67,120	25.1 to 25.8
35	Dallas-Fort Worth-Arlington, TX	25.3%	3,800,140	25 to 25.6
36	Sacramento-Roseville-Folsom, CA	25.3%	950,230	24.9 to 25.6
37	Santa Fe, NM	25.2%	53,140	24.6 to 25.8
38	Colorado Springs, CO	25.1%	282,890	24.4 to 25.8
39	Richmond, VA	24.7%	601,190	24.4 to 25
40	Jacksonville, FL	24.6%	719,180	24.3 to 25
41	Provo-Orem-Lehi, UT	24.6%	275,200	24.1 to 25.2
42	Elizabethtown, KY	24.6%	41,090	23.7 to 25.5
43	Columbia, SC	24.5%	362,080	24.1 to 24.8
44	Bloomington, IL	24.4%	68,270	24 to 24.9
45	Albuquerque, NM	24.4%	357,510	23.9 to 24.8
46	Palm Bay-Melbourne-Titusville, FL	24.4%	226,140	23.9 to 24.8
47	Phoenix-Mesa-Chandler, AZ	24.3%	2,197,210	23.9 to 24.7
48	Topeka, KS	24.2%	101,640	23.7 to 24.7
49	Miami-Fort Lauderdale-West Palm Beach, FL	24.2%	2,595,580	23.6 to 24.7
50	Ann Arbor, MI	24.1%	196,750	23.8 to 24.4
51	Los Angeles-Long Beach-Anaheim, CA	24.1%	5,495,530	23.6 to 24.6
52	Bridgeport-Stamford-Danbury, CT	24.1%	359,170	23.5 to 24.7
53	Ogden, UT	24%	244,240	23.4 to 24.5
54	Nashville-Davidson--Murfreesboro--Franklin, TN	23.9%	1,011,730	23.4 to 24.4
55	San Antonio-New Braunfels, TX	23.9%	1,032,390	23.6 to 24.2
56	Detroit-Warren-Dearborn, MI	23.8%	1,725,390	23.4 to 24.1
57	Hartford-West Hartford-East Hartford, CT	23.8%	539,850	23.4 to 24.2
58	Manchester-Nashua, NH	23.7%	180,210	23.2 to 24.1
59	Amherst Town-Northampton, MA	23.6%	54,000	23.2 to 24
60	Fort Collins-Loveland, CO	23.6%	139,520	23 to 24.2
61	Midland, MI	23.6%	26,610	23.1 to 24.1
62	Columbia, MO	23.6%	93,900	23.2 to 24
63	Lincoln, NE	23.6%	165,630	23.2 to 24
64	Corvallis, OR	23.5%	28,640	23 to 24
65	Charleston-North Charleston, SC	23.5%	362,560	23 to 23.9
66	Orlando-Kissimmee-Sanford, FL	23.5%	1,333,970	23.1 to 23.8
67	Kansas City, MO-KS	23.5%	1,010,030	23.1 to 23.8
68	Dover, DE	23.4%	55,840	22.9 to 24
69	Columbus, OH	23.4%	1,013,660	23.1 to 23.7
70	Harrisburg-Carlisle, PA	23.3%	301,220	23 to 23.6
71	Salem, OR	23.3%	160,630	22.9 to 23.7
72	Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	23.3%	2,602,260	23.1 to 23.6
73	Milwaukee-Waukesha, WI	23.2%	735,010	22.9 to 23.6
74	Oklahoma City, OK	23.1%	639,890	22.7 to 23.5
75	San Diego-Chula Vista-Carlsbad, CA	23%	1,374,490	22.6 to 23.3
76	El Paso, TX	23%	302,780	22.6 to 23.3
77	Dubuque, IA	23%	47,780	22.4 to 23.5
78	Portland-Vancouver-Hillsboro, OR-WA	22.9%	1,089,980	22.5 to 23.3
79	Oshkosh-Neenah, WI	22.9%	78,490	22.6 to 23.2
80	Chicago-Naperville-Elgin, IL-IN	22.9%	4,174,800	22.5 to 23.2
81	Omaha, NE-IA	22.9%	463,540	22.5 to 23.3
82	Sierra Vista-Douglas, AZ	22.8%	27,360	22.2 to 23.4
83	Albany, GA	22.7%	45,980	22.2 to 23.3
84	Burlington-South Burlington, VT	22.7%	102,150	22.1 to 23.3
85	Rochester, NY	22.7%	438,350	22.4 to 23
86	Lawrence, KS	22.6%	42,350	22 to 23.2
87	Pensacola-Ferry Pass-Brent, FL	22.6%	173,680	22.2 to 23
88	Crestview-Fort Walton Beach-Destin, FL	22.5%	115,010	21.8 to 23.2
89	Abilene, TX	22.5%	64,020	22.1 to 22.9
90	Montgomery, AL	22.5%	143,770	22.1 to 22.8
91	Cincinnati, OH-KY-IN	22.4%	1,027,680	22.1 to 22.7
92	Greenville-Anderson-Greer, SC	22.3%	396,560	21.9 to 22.7
93	St. Louis, MO-IL	22.3%	1,216,730	21.9 to 22.6
94	Columbus, IN	22.3%	37,510	21.8 to 22.7
95	Ames, IA	22.2%	45,060	21.7 to 22.7
96	Cedar Rapids, IA	22.1%	123,490	21.6 to 22.6
97	Fayetteville-Springdale-Rogers, AR	22.1%	246,940	21.6 to 22.5
98	Boise City, ID	22%	355,520	21.5 to 22.6
99	Houston-Pasadena-The Woodlands, TX	22%	3,051,510	21.7 to 22.3
100	Virginia Beach-Chesapeake-Norfolk, VA-NC	21.9%	699,820	21.6 to 22.3
101	Greenville, NC	21.9%	69,150	21.5 to 22.3
102	Peoria, IL	21.9%	148,140	21.5 to 22.2
103	Lexington-Fayette, KY	21.9%	256,820	21.4 to 22.3
104	Dayton-Kettering-Beavercreek, OH	21.9%	337,380	21.5 to 22.2
105	Buffalo-Cheektowaga, NY	21.8%	482,640	21.5 to 22
106	Cleveland, OH	21.8%	951,880	21.4 to 22.1
107	Columbus, GA-AL	21.7%	100,770	21.4 to 22.1
108	Fairbanks-College, AK	21.7%	27,100	21.2 to 22.3
109	Oxnard-Thousand Oaks-Ventura, CA	21.7%	279,030	21.3 to 22.1
110	Tucson, AZ	21.7%	350,620	21.4 to 22.1
111	Las Cruces, NM	21.7%	63,390	21.2 to 22.2
112	Knoxville, TN	21.6%	388,200	21.3 to 22
113	Aguadilla, PR	21.6%	38,670	20.6 to 22.7
114	Bremerton-Silverdale-Port Orchard, WA	21.6%	81,660	21.2 to 22
115	Santa Cruz-Watsonville, CA	21.6%	83,740	21.2 to 21.9
116	Indianapolis-Carmel-Greenwood, IN	21.5%	1,024,380	21.2 to 21.9
117	Pittsburgh, PA	21.5%	1,017,970	21.1 to 21.8
118	Manhattan, KS	21.4%	43,070	20.9 to 22
119	Syracuse, NY	21.4%	270,640	21.1 to 21.7
120	State College, PA	21.4%	51,150	21 to 21.8
121	Logan, UT-ID	21.4%	55,680	20.9 to 21.8
122	Jackson, MI	21.4%	42,720	20.9 to 21.9
123	Fayetteville, NC	21.4%	115,160	21 to 21.8
124	Gainesville, FL	21.4%	125,870	21 to 21.7
125	Bloomington, IN	21.3%	57,240	20.9 to 21.8
126	Greensboro-High Point, NC	21.3%	335,080	20.9 to 21.7
127	Sumter, SC	21.3%	25,740	20.8 to 21.8
128	Winchester, VA-WV	21.3%	56,940	20.9 to 21.7
129	Charleston, WV	21.3%	88,560	20.8 to 21.7
130	Battle Creek, MI	21.3%	42,720	20.9 to 21.7
131	Worcester, MA	21.3%	307,500	20.9 to 21.6
132	Johnson City, TN	21.3%	69,520	20.8 to 21.7
133	Birmingham, AL	21.2%	481,260	20.8 to 21.6
134	San Angelo, TX	21.2%	41,560	20.8 to 21.6
135	New Haven, CT	21.2%	241,930	20.8 to 21.5
136	Eagle Pass, TX	21.1%	10,470	20.4 to 21.8
137	Little Rock-North Little Rock-Conway, AR	21.1%	331,620	20.7 to 21.5
138	Salisbury, MD	21.1%	43,450	20.6 to 21.6
139	Utica-Rome, NY	21.1%	97,930	20.7 to 21.4
140	Tulsa, OK	21.1%	427,780	20.7 to 21.5
141	Saginaw, MI	21.1%	66,920	20.7 to 21.4
142	Chattanooga, TN-GA	21%	241,430	20.7 to 21.4
143	Fargo, ND-MN	21%	131,310	20.5 to 21.5
144	Providence-Warwick, RI-MA	21%	640,160	20.6 to 21.4
145	Kalamazoo-Portage, MI	21%	105,570	20.6 to 21.4
146	Akron, OH	20.9%	291,770	20.6 to 21.3
147	Brownsville-Harlingen, TX	20.9%	120,870	20.6 to 21.3
148	Urban Honolulu, HI	20.9%	400,620	20.4 to 21.4
149	Lubbock, TX	20.9%	150,010	20.5 to 21.2
150	Spokane-Spokane Valley, WA	20.8%	224,440	20.5 to 21.2
151	Davenport-Moline-Rock Island, IA-IL	20.8%	159,760	20.5 to 21.1
152	Roanoke, VA	20.8%	133,060	20.4 to 21.1
153	Waco, TX	20.6%	110,320	20.3 to 21
154	Champaign-Urbana, IL	20.6%	89,950	20.3 to 21
155	McAllen-Edinburg-Mission, TX	20.6%	231,310	20.3 to 21
156	Rochester, MN	20.6%	96,850	20.3 to 20.9
157	Cheyenne, WY	20.5%	39,830	19.9 to 21.2
158	Jacksonville, NC	20.4%	43,170	19.9 to 21
159	Anchorage, AK	20.4%	158,300	19.9 to 20.9
160	Louisville/Jefferson County, KY-IN	20.4%	617,920	20 to 20.8
161	Iowa City, IA	20.4%	77,860	20 to 20.8
162	Savannah, GA	20.3%	170,410	19.9 to 20.8
163	Idaho Falls, ID	20.3%	66,990	19.8 to 20.9
164	Green Bay, WI	20.3%	156,590	20 to 20.6
165	Eugene-Springfield, OR	20.3%	135,670	19.9 to 20.7
166	Asheville, NC	20.3%	162,340	19.8 to 20.8
167	Laredo, TX	20.2%	90,710	19.5 to 20.9
168	Pinehurst-Southern Pines, NC	20.2%	35,070	19.6 to 20.7
169	Springfield, MO	20.2%	200,830	19.8 to 20.6
170	Wilmington, NC	20.1%	175,570	19.6 to 20.6
171	Appleton, WI	20.1%	110,050	19.7 to 20.5
172	Lawton, OK	20.1%	34,790	19.4 to 20.8
173	Grand Rapids-Wyoming-Kentwood, MI	20.1%	539,440	19.8 to 20.3
174	Panama City-Panama City Beach, FL	20%	80,990	19.6 to 20.5
175	Jackson, MS	20%	241,680	19.6 to 20.5
176	Athens-Clarke County, GA	20%	76,370	19.6 to 20.5
177	North Port-Bradenton-Sarasota, FL	20%	313,540	19.5 to 20.6
178	Springfield, MA	20%	166,930	19.7 to 20.3
179	Mankato, MN	19.9%	43,070	19.4 to 20.4
180	St. Cloud, MN	19.9%	88,610	19.6 to 20.3
181	Bismarck, ND	19.9%	60,750	19.3 to 20.5
182	Augusta-Richmond County, GA-SC	19.9%	204,840	19.6 to 20.1
183	Winston-Salem, NC	19.9%	243,520	19.5 to 20.3
184	Memphis, TN-MS-AR	19.8%	558,700	19.5 to 20.1
185	Waterbury-Shelton, CT	19.8%	139,450	19.4 to 20.2
186	Santa Maria-Santa Barbara, CA	19.8%	176,620	19.5 to 20.2
187	Lewiston, ID-WA	19.8%	21,000	19.2 to 20.4
188	Pocatello, ID	19.8%	26,860	19.3 to 20.3
189	Wichita, KS	19.8%	280,660	19.4 to 20.2
190	Ponce, PR	19.8%	52,170	19.3 to 20.3
191	Lakeland-Winter Haven, FL	19.8%	240,990	19.5 to 20.1
192	Eau Claire, WI	19.7%	73,570	19.4 to 20.1
193	Fond du Lac, WI	19.7%	34,080	19.2 to 20.2
194	Lafayette-West Lafayette, IN	19.7%	79,820	19.3 to 20
195	Portland-South Portland, ME	19.7%	255,400	19.1 to 20.3
196	Cape Coral-Fort Myers, FL	19.7%	281,730	19.1 to 20.2
197	Arecibo, PR	19.6%	27,750	18.8 to 20.5
198	Bellingham, WA	19.6%	78,320	19.2 to 20.1
199	South Bend-Mishawaka, IN-MI	19.6%	116,720	19.3 to 20
200	Sioux Falls, SD-MN	19.6%	163,450	19 to 20.2
201	Missoula, MT	19.6%	56,180	18.9 to 20.3
202	Pittsfield, MA	19.6%	46,080	19.1 to 20
203	Macon-Bibb County, GA	19.6%	81,780	19.1 to 20
204	Chico, CA	19.6%	61,170	19 to 20.1
205	Amarillo, TX	19.5%	106,600	19.1 to 20
206	Binghamton, NY	19.5%	81,910	19.1 to 19.8
207	Mayaguez, PR	19.5%	43,290	18.8 to 20.1
208	Baton Rouge, LA	19.4%	368,210	19.1 to 19.8
209	New Orleans-Metairie, LA	19.4%	399,720	19 to 19.8
210	Walla Walla, WA	19.4%	17,640	18.9 to 19.9
211	Sebring, FL	19.4%	22,450	18.8 to 19.9
212	Redding, CA	19.4%	56,520	19 to 19.8
213	Bend, OR	19.3%	95,090	18.7 to 19.9
214	Traverse City, MI	19.3%	59,090	18.9 to 19.7
215	La Crosse-Onalaska, WI-MN	19.3%	73,050	19 to 19.6
216	Santa Rosa-Petaluma, CA	19.3%	176,890	18.9 to 19.7
217	Deltona-Daytona Beach-Ormond Beach, FL	19.3%	201,540	18.8 to 19.7
218	Fort Wayne, IN	19.3%	197,210	18.9 to 19.6
219	Parkersburg-Vienna, WV	19.2%	27,600	18.6 to 19.8
220	Killeen-Temple, TX	19.2%	132,750	18.8 to 19.6
221	Kingsport-Bristol, TN-VA	19.2%	104,110	18.9 to 19.5
222	Mobile, AL	19.2%	155,190	18.8 to 19.6
223	Grand Junction, CO	19.2%	56,140	18.6 to 19.7
224	Greeley, CO	19.1%	103,070	18.7 to 19.6
225	Medford, OR	19.1%	75,380	18.7 to 19.5
226	Blacksburg-Christiansburg-Radford, VA	19.1%	53,260	18.8 to 19.5
227	Wausau, WI	19.1%	58,570	18.7 to 19.5
228	Hinesville, GA	19.1%	13,280	18.5 to 19.6
229	Rocky Mount, NC	19%	44,520	18.5 to 19.5
230	Kiryas Joel-Poughkeepsie-Newburgh, NY	19%	236,070	18.6 to 19.4
231	Slidell-Mandeville-Covington, LA	19%	84,090	18.4 to 19.6
232	Burlington, NC	19%	55,600	18.5 to 19.4
233	Bozeman, MT	18.9%	60,530	18.2 to 19.7
234	Niles, MI	18.9%	49,790	18.5 to 19.3
235	Ocala, FL	18.9%	104,910	18.5 to 19.3
236	Rome, GA	18.9%	34,550	17.7 to 20.1
237	Morgantown, WV	18.9%	55,540	18.3 to 19.4
238	College Station-Bryan, TX	18.8%	111,690	18.5 to 19.2
239	Punta Gorda, FL	18.8%	48,010	18.3 to 19.4
240	Racine-Mount Pleasant, WI	18.8%	62,150	18.4 to 19.2
241	Billings, MT	18.8%	81,360	18.2 to 19.4
242	Muncie, IN	18.8%	35,280	18.3 to 19.2
243	Hilton Head Island-Bluffton-Port Royal, SC	18.8%	73,960	18.2 to 19.4
244	Hagerstown-Martinsburg, MD-WV	18.8%	93,720	18.4 to 19.2
245	Florence, SC	18.8%	76,690	18.4 to 19.1
246	San Luis Obispo-Paso Robles, CA	18.7%	104,700	18.4 to 19.1
247	Elmira, NY	18.7%	25,980	18.2 to 19.2
248	Lynchburg, VA	18.7%	82,180	18.3 to 19.2
249	Erie, PA	18.7%	100,780	18.4 to 19
250	Williamsport, PA	18.6%	38,910	18.2 to 19.1
251	Gainesville, GA	18.6%	86,340	18.2 to 19
252	Janesville-Beloit, WI	18.6%	59,240	18.2 to 19
253	Reno, NV	18.6%	247,240	18.1 to 19.1
254	Sebastian-Vero Beach-West Vero Corridor, FL	18.5%	50,030	18 to 19.1
255	Brunswick-St. Simons, GA	18.5%	37,910	18.1 to 19
256	Clarksville, TN-KY	18.5%	87,760	18.1 to 18.8
257	Auburn-Opelika, AL	18.4%	59,060	17.9 to 18.9
258	Kennewick-Richland, WA	18.4%	110,760	18.1 to 18.7
259	Evansville, IN	18.4%	120,930	18 to 18.8
260	Reading, PA	18.3%	151,570	18 to 18.7
261	Bangor, ME	18.3%	61,350	17.9 to 18.7
262	Port St. Lucie, FL	18.3%	153,770	17.8 to 18.8
263	Yuma, AZ	18.3%	58,750	17.8 to 18.8
264	Duluth, MN-WI	18.3%	106,660	18 to 18.6
265	Allentown-Bethlehem-Easton, PA-NJ	18.3%	341,600	18 to 18.5
266	St. George, UT	18.2%	79,080	17.6 to 18.8
267	Victoria, TX	18.2%	31,040	17.6 to 18.8
268	Wichita Falls, TX	18.2%	45,200	17.8 to 18.6
269	Dalton, GA	18.2%	55,610	17.6 to 18.8
270	Monroe, LA	18.2%	70,950	17.7 to 18.6
271	Tyler, TX	18.1%	97,010	17.7 to 18.4
272	Harrisonburg, VA	18.1%	53,790	17.5 to 18.6
273	Naples-Marco Island, FL	18%	158,680	17.5 to 18.5
274	Wildwood-The Villages, FL	18%	33,880	17.5 to 18.6
275	Shreveport-Bossier City, LA	18%	144,240	17.6 to 18.4
276	Spartanburg, SC	18%	139,300	17.7 to 18.3
277	Flint, MI	18%	112,960	17.6 to 18.4
278	Corpus Christi, TX	18%	170,550	17.6 to 18.3
279	Kingston, NY	17.9%	48,090	17.4 to 18.4
280	Fresno, CA	17.9%	412,410	17.5 to 18.4
281	Glens Falls, NY	17.8%	41,130	17.4 to 18.3
282	Lancaster, PA	17.8%	222,720	17.5 to 18.1
283	Las Vegas-Henderson-North Las Vegas, NV	17.8%	1,067,080	17.4 to 18.3
284	Longview, TX	17.8%	102,180	17.4 to 18.1
285	Prescott Valley-Prescott, AZ	17.8%	62,320	17.2 to 18.3
286	Cleveland, TN	17.8%	33,130	17.2 to 18.3
287	Coeur d'Alene, ID	17.8%	61,000	17.2 to 18.3
288	Waterloo-Cedar Falls, IA	17.8%	71,540	17.3 to 18.2
289	Alexandria, LA	17.7%	47,610	17.3 to 18.2
290	Yuba City, CA	17.7%	39,630	17 to 18.5
291	Barnstable Town, MA	17.7%	82,710	17.1 to 18.4
292	Goldsboro, NC	17.7%	31,650	17.2 to 18.1
293	Scranton--Wilkes-Barre, PA	17.7%	224,190	17.3 to 18
294	Lafayette, LA	17.6%	152,170	17.1 to 18.2
295	Hattiesburg, MS	17.6%	51,590	17.2 to 18.1
296	Midland, TX	17.6%	109,700	17.1 to 18.2
297	Sheboygan, WI	17.6%	50,270	17.2 to 18
298	Watertown-Fort Drum, NY	17.5%	31,310	17.1 to 18
299	Cape Girardeau, MO-IL	17.5%	34,750	17.1 to 18
300	Norwich-New London-Willimantic, CT	17.5%	101,700	17.2 to 17.9
301	Pueblo, CO	17.5%	50,630	17 to 18
302	Guayama, PR	17.5%	7,490	16.6 to 18.4
303	Vallejo, CA	17.5%	119,920	17.2 to 17.8
304	Great Falls, MT	17.4%	28,920	16.9 to 18
305	Rapid City, SD	17.4%	66,260	16.8 to 18
306	Lake Havasu City-Kingman, AZ	17.4%	48,750	16.9 to 17.9
307	Lewiston-Auburn, ME	17.4%	38,540	16.9 to 17.9
308	Elkhart-Goshen, IN	17.4%	99,430	17.1 to 17.8
309	Johnstown, PA	17.4%	38,890	16.9 to 17.9
310	Kenosha, WI	17.4%	65,080	17 to 17.8
311	Altoona, PA	17.4%	48,790	16.9 to 17.8
312	York-Hanover, PA	17.3%	163,440	17 to 17.6
313	Canton-Massillon, OH	17.3%	144,180	16.9 to 17.6
314	Valdosta, GA	17.3%	44,670	16.9 to 17.7
315	Decatur, AL	17.3%	50,130	16.9 to 17.7
316	Myrtle Beach-Conway-North Myrtle Beach, SC	17.2%	131,850	16.8 to 17.7
317	Toledo, OH	17.2%	260,550	16.9 to 17.5
318	Hickory-Lenoir-Morganton, NC	17.2%	137,090	16.9 to 17.5
319	Homosassa Springs, FL	17.2%	28,420	16.6 to 17.8
320	Tuscaloosa, AL	17.2%	85,820	16.8 to 17.6
321	Fort Smith, AR-OK	17.1%	82,790	16.7 to 17.6
322	Rockford, IL	17%	122,620	16.7 to 17.4
323	Bay City, MI	17%	25,860	16.5 to 17.5
324	Beaumont-Port Arthur, TX	17%	146,740	16.7 to 17.3
325	Huntington-Ashland, WV-KY-OH	17%	124,280	16.6 to 17.3
326	Dothan, AL	17%	51,950	16.5 to 17.4
327	Grand Forks, ND-MN	16.9%	41,850	16.5 to 17.3
328	Mount Vernon-Anacortes, WA	16.9%	43,350	16.6 to 17.3
329	Sherman-Denison, TX	16.9%	42,480	16.4 to 17.4
330	Minot, ND	16.9%	25,760	16.4 to 17.5
331	Anniston-Oxford, AL	16.9%	34,580	16.5 to 17.3
332	Flagstaff, AZ	16.9%	55,640	16.5 to 17.3
333	Owensboro, KY	16.9%	40,290	16.4 to 17.4
334	Jackson, TN	16.8%	70,250	16.4 to 17.2
335	Gulfport-Biloxi, MS	16.8%	142,550	16.4 to 17.2
336	Chambersburg, PA	16.8%	50,980	16.3 to 17.2
337	Atlantic City-Hammonton, NJ	16.7%	151,550	16.4 to 17.1
338	Bowling Green, KY	16.7%	70,640	16.2 to 17.2
339	Grants Pass, OR	16.7%	18,870	16.2 to 17.2
340	Springfield, OH	16.7%	37,050	16.3 to 17.1
341	Monroe, MI	16.7%	32,280	16.2 to 17.1
342	Longview-Kelso, WA	16.5%	31,810	16 to 17.1
343	Enid, OK	16.5%	17,130	15.8 to 17.2
344	Daphne-Fairhope-Foley, AL	16.5%	77,160	16 to 17
345	Farmington, NM	16.5%	38,740	16 to 17
346	Texarkana, TX-AR	16.4%	47,790	16.1 to 16.8
347	Paducah, KY-IL	16.4%	41,840	15.9 to 16.9
348	Morristown, TN	16.3%	36,890	15.8 to 16.8
349	Stockton-Lodi, CA	16.3%	236,730	16 to 16.6
350	Staunton-Stuarts Draft, VA	16.3%	39,870	15.9 to 16.7
351	Decatur, IL	16.2%	33,980	15.8 to 16.7
352	Riverside-San Bernardino-Ontario, CA	16.2%	1,504,530	16 to 16.5
353	Joplin, MO-KS	16.2%	71,510	15.8 to 16.6
354	Casper, WY	16.1%	31,270	15.6 to 16.7
355	Grand Island, NE	16.1%	30,170	15.7 to 16.6
356	St. Joseph, MO-KS	16%	44,910	15.6 to 16.4
357	Florence-Muscle Shoals, AL	16%	46,050	15.6 to 16.5
358	El Centro, CA	15.9%	47,240	15.6 to 16.3
359	Muskegon-Norton Shores, MI	15.9%	49,780	15.6 to 16.3
360	Kokomo, IN	15.8%	24,560	15.4 to 16.2
361	Napa, CA	15.8%	66,140	15.3 to 16.3
362	Twin Falls, ID	15.8%	41,300	15.3 to 16.3
363	Houma-Bayou Cane-Thibodaux, LA	15.8%	70,960	15.3 to 16.3
364	Sioux City, IA-NE-SD	15.8%	63,650	15.4 to 16.1
365	Bakersfield-Delano, CA	15.8%	293,530	15.5 to 16.1
366	Lima, OH	15.7%	41,030	15.4 to 16.1
367	Hammond, LA	15.7%	36,620	15.2 to 16.2
368	Youngstown-Warren, OH	15.6%	141,080	15.2 to 16
369	Albany, OR	15.6%	37,180	15.2 to 16
370	Modesto, CA	15.6%	168,130	15.3 to 15.9
371	Terre Haute, IN	15.5%	53,860	15.2 to 15.9
372	Kahului-Wailuku, HI	15.5%	64,940	15 to 16
373	Hot Springs, AR	15.5%	31,570	15 to 16
374	Gadsden, AL	15.4%	25,630	14.9 to 15.9
375	Mansfield, OH	15.4%	39,680	15 to 15.8
376	Odessa, TX	15.4%	70,660	14.9 to 15.8
377	Vineland, NJ	15.3%	50,260	15 to 15.7
378	Kankakee, IL	15.3%	33,650	14.9 to 15.7
379	Lebanon, PA	15.3%	43,080	14.9 to 15.6
380	Beckley, WV	15.3%	34,900	14.7 to 15.8
381	Jonesboro, AR	15.2%	52,050	14.9 to 15.6
382	Wenatchee-East Wenatchee, WA	15.2%	43,380	14.8 to 15.6
383	Wheeling, WV-OH	15.2%	49,740	14.8 to 15.5
384	Yakima, WA	15.1%	80,980	14.7 to 15.5
385	Gettysburg, PA	14.9%	25,130	14.5 to 15.3
386	Michigan City-La Porte, IN	14.6%	32,070	14.2 to 14.9
387	Sandusky, OH	14.4%	40,680	14.1 to 14.8
388	Weirton-Steubenville, WV-OH	14.2%	28,530	13.8 to 14.7
389	Merced, CA	14.1%	63,340	13.7 to 14.5
390	Hanford-Corcoran, CA	14.1%	34,930	13.7 to 14.5
391	Lake Charles, LA	14%	87,380	13.6 to 14.5
392	Salinas, CA	13.8%	162,740	13.5 to 14.1
393	Visalia, CA	13.2%	147,390	12.8 to 13.5

States and territories

States and territories: reportable rows only. Highly exposed / matched jobs (%).
Rank	Place	Highly exposed / matched jobs (%)	Matched jobs (n)	Approximate 95% interval
1	District of Columbia	38.3%	651,360	37.6 to 39.1
2	Massachusetts	26.1%	3,316,400	25.8 to 26.4
3	Colorado	25.9%	2,665,660	25.5 to 26.3
4	Virginia	25.8%	3,820,920	25.6 to 26.1
5	New York	25.8%	8,603,430	25.5 to 26
6	Maryland	25.8%	2,544,030	25.4 to 26.1
7	Washington	24.8%	3,248,880	24.4 to 25.2
8	Delaware	24.7%	436,680	24.2 to 25.2
9	New Jersey	24.5%	3,940,570	24.2 to 24.9
10	Georgia	24.5%	4,511,550	24.2 to 24.8
11	North Carolina	24.5%	4,635,580	24.2 to 24.7
12	Florida	24.4%	9,361,590	24.2 to 24.6
13	Minnesota	24.4%	2,632,090	24.1 to 24.7
14	Utah	24.2%	1,592,430	23.9 to 24.5
15	Puerto Rico	23.8%	856,900	23.4 to 24.3
16	Texas	23.7%	13,119,130	23.5 to 23.9
17	California	23.5%	16,239,170	23.3 to 23.7
18	Rhode Island	23.4%	446,460	22.8 to 23.9
19	Arizona	23.3%	3,004,780	23 to 23.7
20	New Hampshire	22.7%	628,150	22.3 to 23.2
21	Illinois	22.7%	5,636,790	22.4 to 23
22	Michigan	22.4%	4,021,580	22.2 to 22.7
23	Connecticut	22.4%	1,534,040	22.1 to 22.7
24	South Carolina	22.2%	2,143,360	21.9 to 22.4
25	New Mexico	22.1%	789,200	21.8 to 22.4
26	Wisconsin	22%	2,685,750	21.8 to 22.2
27	Pennsylvania	22%	5,525,580	21.8 to 22.2
28	Tennessee	22%	3,031,930	21.7 to 22.3
29	Nebraska	21.8%	960,220	21.5 to 22.1
30	Oregon	21.8%	1,781,150	21.5 to 22.1
31	Missouri	21.8%	2,685,870	21.5 to 22.1
32	Ohio	21.4%	5,143,030	21.2 to 21.6
33	Kansas	21.3%	1,327,490	20.9 to 21.6
34	Oklahoma	20.9%	1,617,570	20.6 to 21.2
35	Idaho	20.6%	789,810	20.2 to 21
36	Iowa	20.6%	1,434,530	20.3 to 20.9
37	Vermont	20.5%	264,520	20 to 20.9
38	Alabama	20.2%	1,949,230	20 to 20.5
39	Virgin Islands	20%	26,040	19.3 to 20.7
40	Kentucky	20%	1,838,740	19.7 to 20.3
41	Guam	19.9%	56,300	19.3 to 20.5
42	Maine	19.9%	581,030	19.4 to 20.3
43	Alaska	19.4%	295,280	19 to 19.8
44	Indiana	19.3%	2,941,870	19.1 to 19.6
45	Hawaii	19.3%	575,300	18.9 to 19.7
46	Arkansas	19.2%	1,213,370	18.8 to 19.5
47	Montana	19.1%	468,150	18.8 to 19.5
48	Nevada	18.4%	1,452,610	18 to 18.7
49	South Dakota	18.3%	430,740	17.9 to 18.6
50	Louisiana	18.3%	1,767,640	18 to 18.5
51	North Dakota	18.1%	392,550	17.8 to 18.5
52	West Virginia	17.9%	646,690	17.6 to 18.2
53	Mississippi	17.4%	1,065,320	17.1 to 17.7
54	Wyoming	16.6%	257,010	16.2 to 17

At least 60% of known employment must match an exposure score. Highly exposed means a mean human-rated beta score of at least 0.5. Rankings use the point estimate, not a test of differences.

THE FINDING
AND ITS MEANING

Change the score, and the local order moves.

Under the human-rated score, Olympia-Lacey-Tumwater is No. 2. Under GPT-4's rating, it is No. 16. Boulder moves from No. 5 to No. 2. The jobs stay the same. Only the exposure scores do.

College Station-Bryan shifts from rank 238 to 79. Its employment count hasn't changed. Its rating has.

This is a warning about precision, not a new forecast.

Across 393 metros, the Spearman rank correlation is 0.97, so the broad ordering is similar. But a reporter's local story usually turns on particular places, where movement can be more visible.

We wouldn't call a place more vulnerable because it sits one row higher. Close estimates can overlap, and a different defensible score changes the order.

Source records and definitions ↗

The comparison / 01

Same jobs. Different exposure ratings.

Olympia-Lacey-Tumwater, WA

Human-rated rank#2

GPT-4-rated rank#16

Source: Primary: 32.0% (approximate 95% interval 31.6 to 32.4%); sensitivity: 34.5%. Matched jobs: 115,100.

Boulder, CO

Human-rated rank#5

GPT-4-rated rank#2

Source: Primary: 31.7% (approximate 95% interval 31 to 32.5%); sensitivity: 41.8%. Matched jobs: 170,070.

College Station-Bryan, TX

Human-rated rank#238

GPT-4-rated rank#79

Source: Primary: 18.8% (approximate 95% interval 18.5 to 19.2%); sensitivity: 28.1%. Matched jobs: 111,690.

May 2025 OEWS employment with 2023 exposure ratings. Rank 1 means the highest share among 393 reportable metros. We don't have an interval for the sensitivity ranks. They aren't tests of significance.

THE FINDING
AND ITS MEANING

The denominator is part of the story.

We could match 92.5% of 155,481,840 known US jobs to an exposure score. We don't count suppressed employment as known. Unmatched occupations stay outside the headline base.

In Boulder, the primary share is 31.7% of matched jobs, with an approximate 95% interval of 31 to 32.5%. The same exposed-job count is 29.8% of all known jobs. Its coverage is 93.8%, with 170,070 matched jobs.

The missing jobs matter.

We haven't scored them as safe. Boulder's two shares show what happens when we change the base, not two forecasts of layoffs. Our local records show coverage so readers can see how much of the known workforce sits behind the headline.

Source records and definitions ↗

Our interpretation and the next reporting questionsWhat the evidence suggests, alternative explanations, and checks not yet performed.

How to turn exposure into evidence.

02 / Paloren's interpretation

Our reading of the evidence, not an additional measured result.

Start in Boulder with one practical question: what happened to the time saved? Faster drafting could expand output, raise quality, cut overtime, shift staff, or mean fewer hires than last year.

A staffing change could also come from demand, funding, mergers, turnover or measurement. The 2023 exposure score can't tell those paths apart.

The 2023 scores can't answer for today's workplace.

Our comparison gives readers a test for confident headlines. Olympia moves from rank 2 to 16 when the rating changes, yet its employment record stays fixed. Before reporting a local effect, we'd want task-level evidence of what changed, who gained time, and what the employer did next.

Without that, the story misleads. Workers may read a probability of replacement the source never measured; employers may hear occupational averages presented as a forecast.

What to investigate next

Open questions for further reporting. These aren't findings or interviews we claim to have completed.

Interview matched employers and workers in one metro: which tasks changed since 2023, whether AI was involved, and what happened to time saved.
Compare the reported rank with both exposure scores, the matched-job coverage and the interval before quoting it.
Follow staffing, output, quality and pay over time; the score alone can't tell reinvestment, monitoring, a hiring freeze or displacement apart.

Behind the analysis

Inspect the source records 5 records / definitions / provenance

We link every comparison to the records used. Open the provenance download to check the selected values and the source-file checksums. Interpretation stays separate from those measured results.

Source: E1
findings.json
Source: id: OEWS-08
Source: E2
tables/geo_national.csv
Source: level: national / CSV line 2
Source: E3
tables/geo_metro.csv
Source: area_code: 36500 / CSV line 3
Source: E4
tables/geo_metro.csv
Source: area_code: 14500 / CSV line 6
Source: E5
tables/geo_metro.csv
Source: area_code: 17780 / CSV line 239

Get this edition's analysis source record (JSON) ↓Open the primary data publisher ↗

The leading places.
The defined measure.

03 / The local comparison

Different places have different mixes of occupations. This study joins those employment counts to task-exposure scores. The result describes the mix of work, not what employers plan to do.

Dots show the reported value. Lines show the approximate 95% interval. A rank isn't a test of differences.

Source: BLS OEWS and Eloundou et al.

Leading metro areas by highly exposed / matched jobs (%). May 2025 employment / 2023 exposure scores.

Download chart SVG ↓

Reporter notes: explanations, unknowns and citationDownload the full briefing or inspect the sources and methods.

The finding.
The meaning.
The next question.

For the newsroom

Where do task-exposure scores put more jobs in the highly exposed group, and how much does the chosen score change the answer?

Download reporter brief ↓Editorial edition / 26 October 2026

01 / Measured finding

23.2% of matched US jobs in highly exposed occupations. Approximate 95% interval: 23.1% to 23.2%. Matched employment: n = 143,751,120. Source period: May 2025 employment / 2023 exposure scores.

Exposure means tasks an LLM could speed up. It isn't a job-loss forecast. We don't measure actual AI use or count people who will be replaced. At least 60% of known employment must match an exposure score. Highly exposed means a mean human-rated beta score of at least 0.5. Rankings use the point estimate, not a test of differences.

02 / Our interpretation

Start in Boulder with one practical question: what happened to the time saved? Faster drafting could expand output, raise quality, cut overtime, shift staff, or mean fewer hires than last year. A staffing change could also come from demand, funding, mergers, turnover or measurement. The 2023 exposure score can't tell those paths apart.

Other explanations and what we cannot answer

Different occupation mixes can produce different exposure shares without different adoption rates.
Changing the exposure rating changes some local ranks even when the employment records stay fixed.

Where the reporting goes next

Interview matched employers and workers in one metro: which tasks changed since 2023, whether AI was involved, and what happened to time saved.
Compare the reported rank with both exposure scores, the matched-job coverage and the interval before quoting it.
Follow staffing, output, quality and pay over time; the score alone can't tell reinvestment, monitoring, a hiring freeze or displacement apart.

We haven't done these checks yet. They're a route for further reporting, not extra findings.

Sources, methods and citation

No one was interviewed for this brief, and no cause was tested.

We updated this brief on 26 October 2026. We haven't refreshed the data. Use the period beside the finding.

Copyable citation

Paloren Research. Which places have the most work exposed to AI?. Editorial edition 26 October 2026. https://paloren.ai/research/ai-exposed-jobs-by-metro-2026

Use our analysis with credit to Paloren Research and a link. Don't drop the caveats. We can't grant rights to a source publisher's material.

Request comment or a correction ↗

Method and limitationsDefinitions, exclusions, source records and the checks behind this edition.

What this can
and cannot say.

Method / Before you quote

At least 60% of known employment must match an exposure score. Highly exposed means a mean human-rated beta score of at least 0.5. Rankings use the point estimate, not a test of differences.

Open the primary source ↗
Occupation exposure scores ↗

01
We count matched jobs. If an occupation has no usable score, or BLS suppresses its employment, we don't treat those missing workers as unexposed. Each local record shows how much of the known job base we could match.
02
The scores date to 2023. They aren't a test of today's tools.
03
We use BLS employment errors to form an approximate 95% range. It assumes the cells are independent, which may not hold. That range doesn't test the exposure score itself. Self-employed workers aren't in OEWS.

Leave with the record.

The reporter's desk

Keep the definition, period and qualification with any figure you reuse. The download includes the local facts supplied by the study, source-row references and reporting rules.

Request comment or a source check ↗

01All reportable places CSV / values, ranges, local facts↓ 02Publication chart SVG / source and caveat included↓ 03Sources behind this edition JSON / selected records and checksums↓

How to read
our research.

The editorial record

Original analysis, with the evidence and its limits kept together.

Publisher and methods

We're part of Paloren, an AI consultancy and commercial publisher. We don't claim to be an independent research institution.

We show what we counted, then explain our reading of it. They're different. An AI name isn't proof of an AI business; a filing's warning isn't an incident report.

Corrections and version history 26 October 2026

We changed the articles on 26 October 2026, not the source data. Edition newsroom-20261026.

Copy edit / 28 October 2026 / Research copy notes
We made copy edits for clarity and consistency. Methods, findings and figures have not changed.
Reading and presentation update / All ten studies
The opening links to the downloadable briefing. Local tables come first. You can expand secondary detail or print the study, keeping the source notes and caveats beside the results so they don't get lost when you share a finding. Source values are unchanged. We also corrected punctuation and displayed council-record grammar.
Editorial update / All ten studies
Each of the ten studies now has a brief. It sets out what we found, our take, and what we'd need to check next. The datasets haven't been refreshed.

We keep each source period beside its finding. We haven't set a schedule for new data releases.

Reuse and corrections

Use our original charts and analysis with credit to Paloren Research and a link. Keep the source period and caveats. We can't grant rights to material owned by a source publisher.

Found a mistake? Send us the page and the record we should check at aaron@palorenresearch.com.

For a quote from Aaron, please ask for a current comment. Our labelled analysis isn't a spoken quotation.

Keep reading / The local series

Infrastructure / United StatesThe hosting boom is bigger than the data-centre count.↗Company registers / United KingdomLondon has an AI-name cluster. It is not yet an industry map.↗Business registers / AustraliaNSW leads one AI-register measure. The ACT leads another.↗

All local investigations ↗

Research

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